Difference between revisions of "GeMoMa"

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'''Ge'''ne '''Mo'''del '''Ma'''pper (GeMoMa) is a homology-based gene prediction program. GeMoMa uses the annotation of protein-coding genes in a reference genome to infer the annotation of protein-coding genes in a target genome. Thereby, GeMoMa utilizes amino acid sequence and intron position conservation. In addition, GeMoMa allows to incorporate RNA-seq evidence for splice site prediction.
 
'''Ge'''ne '''Mo'''del '''Ma'''pper (GeMoMa) is a homology-based gene prediction program. GeMoMa uses the annotation of protein-coding genes in a reference genome to infer the annotation of protein-coding genes in a target genome. Thereby, GeMoMa utilizes amino acid sequence and intron position conservation. In addition, GeMoMa allows to incorporate RNA-seq evidence for splice site prediction.
  
[[File:GeMoMa-schema.png|thumb|right|350px|Schema of GeMoMa algorithm]]
+
GeMoMa is available in a public web-server at [http://galaxy.informatik.uni-halle.de galaxy.informatik.uni-halle.de]. The provided web-server only allows a limited number of reference genes and uses a time out of 2 minutes per transcript prediction. For unlimited use, please use the command line program or integrate GeMoMa in your own [https://galaxyproject.org/ Galaxy] instance.
  
== Paper ==
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{|
If you use GeMoMa, please cite
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|__TOC__
 +
|[[File:GeMoMa-schema.png|thumb|right|450px|Schema of GeMoMa algorithm]]
 +
|}
 +
 
 +
== Installation ==
 +
 
 +
GeMoMa is now available via bioconda. Here is the [https://anaconda.org/bioconda/gemoma direct link to the package]. To install this package with conda run:
 +
conda install -c bioconda gemoma 
 +
However, you can also install GeMoMa manually.
  
J. Keilwagen, M. Wenk, J. L. Erickson, M. H. Schattat, J. Grau, and F. Hartung. [https://nar.oxfordjournals.org/content/44/9/e89 Using intron position conservation for homology-based gene prediction]. ''Nucleic Acids Research'', 2016. doi: 10.1093/nar/gkw092
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=== Requirements ===
  
J. Keilwagen, F. Hartung, M. Paulini, S. O. Twardziok, and J. Grau
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For running the GeMoMa, you need the following software on your computer
[https://rdcu.be/QbKc Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi]. ''BMC Bioinformatics'', 2018. doi: 10.1186/s12859-018-2203-5
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* Java v1.8 or later
 +
* [ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ blast] or [https://github.com/soedinglab/MMseqs2 mmseqs]
  
== Download ==
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=== Download ===
  
 
GeMoMa is implemented in Java using Jstacs. You can [http://www.jstacs.de/download.php?which=GeMoMa download a zip file] containing a readme, the GeMoMa jar file and some tiny scripts for running GeMoMa. The jar file allows for  
 
GeMoMa is implemented in Java using Jstacs. You can [http://www.jstacs.de/download.php?which=GeMoMa download a zip file] containing a readme, the GeMoMa jar file and some tiny scripts for running GeMoMa. The jar file allows for  
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</ul>
 
</ul>
  
You can also [[:File:GeMoMa-manual.pdf|download a small manual for GeMoMa]] which explains the main steps for the analysis.
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== In a nutshell ==
  
== Galaxy ==
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GeMoMa is a modular, homology-based gene prediction program with huge flexibility. However, we also provide a pipeline allowing to use GeMoMa easily. If you like to start GeMoMa for the first time, we recommend to use the GeMoMaPipeline like this
GeMoMa is available in a public web-server at [http://galaxy.informatik.uni-halle.de galaxy.informatik.uni-halle.de]. The provided web-server only allows a limited number of reference genes and uses a time out of 2 minutes per transcript prediction. For unlimited use, please use the command line program or integrate GeMoMa in your only Galaxy instance.
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java -jar GeMoMa-1.7.1.jar CLI GeMoMaPipeline threads=<threads> outdir=<outdir> GeMoMa.Score=ReAlign AnnotationFinalizer.r=NO o=true t=<target_genome> i=<reference_1_id> a=<reference_1_annotation> g=<reference_1_genome>
 +
there are several parameters that need to be set indicated with '''&lt;'''foo'''&gt;'''. You can specify
 +
* the number of threads
 +
* the output directory
 +
* the target genome
 +
* and the reference ID (optional), annotation and genome. If you have several references just repeat <code>s=own</code> and the parameter tags <code>i</code>, <code>a</code>, <code>g</code> with the corresponding values.
 +
In addition, we recommend to set several parameters:
 +
* <code>GeMoMa.Score=ReAlign</code>: states that the score from mmseqs should be recomputed as mmseqs uses an approximation
 +
* <code>AnnotationFinalizer.r=NO</code>: do not rename genes and transcripts
 +
* <code>o=true</code>: output individual predictions for each reference as a separate file allowing to rerun the combination step ('''GAF''') very easily and quickly
 +
If you like to specify the maximum intron length please consider the parameters <code>GeMoMa.m</code> and <code>GeMoMa.sil</code>.
 +
If you have RNA-seq data either from own experiments or publicly available data sets (cf. [https://www.ncbi.nlm.nih.gov/sra NCBI SRA], [https://www.ebi.ac.uk/ena EMBL-EBI ENA]), we recommend to use them. You need to map the data against the target genome with your favorite read mapper. In addition, we recommend to check the parameters of the section '''DenoiseIntrons'''.
  
[[File:GeMoMa-Workflow.png|thumb|center|700px|GeMoMa workflow adapted from Galaxy]]
 
  
== Running the command line application ==
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The complete documentation describing all GeMoMa modules and all parameters can be accessed at [[GeMoMa-Docs]].
  
For running the command line application, Java v1.8 or later is required.
+
You can also [[:File:GeMoMa-manual.pdf|download a small manual for GeMoMa]] which explains the main steps for the analysis.
 
 
=== GeMoMaPipeline ===
 
 
 
If you like to run the GeMoMaPipeline on a server as a single job, you can use the module GeMoMaPipeline which allows to exploit the full compute power of the computer server via multi-threading. However, GeMoMaPipeline does not distribute task on a compute cluster.
 
You can run GeMoMaPipeline from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI GeMoMaPipeline [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">t</font></td>
 
<td>target genome (Target genome file (FASTA))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>species (data for reference species, range={own, pre-extracted}, default = own)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>Parameters for selection &quot;own&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>ID (ID to distinguish the different reference species, default = , OPTIONAL)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>annotation (Reference annotation file (GFF or GTF), which contains gene models annotated in the reference genome)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>genome (Reference genome file (FASTA))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">w</font></td>
 
<td>weight (the weight can be used to prioritize predictions from different input files; each prediction will get an additional attribute sumWeight that can be used in the filter, valid range = [0.0, 1000.0], default = 1.0, OPTIONAL)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;pre-extracted&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>ID (ID to distinguish the different reference species, default = , OPTIONAL)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>cds parts (The query cds parts file (FASTA), i.e., the cds parts that have been blasted)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>assignment (The assignment file, which combines parts of the CDS to transcripts, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">q</font></td>
 
<td>query proteins (optional query protein file (FASTA) for computing the optimal alignment score against complete protein prediction, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">w</font></td>
 
<td>weight (the weight can be used to prioritize predictions from different input files; each prediction will get an additional attribute sumWeight that can be used in the filter, valid range = [0.0, 1000.0], default = 1.0, OPTIONAL)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
</table></td></tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">selected</font></td>
 
<td>selected (The path to list file, which allows to make only a predictions for the contained transcript ids. The first column should contain transcript IDs as given in the annotation. Remaining columns can be used to determine a target region that should be overlapped by the prediction, if columns 2 to 5 contain chromosome, strand, start and end of region, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">gc</font></td>
 
<td>genetic code (optional user-specified genetic code, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">tag</font></td>
 
<td>tag (A user-specified tag for transcript predictions in the third column of the returned gff. It might be beneficial to set this to a specific value for some genome browsers., default = prediction)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">r</font></td>
 
<td>RNA-seq evidence (data for RNA-seq evidence, range={NO, MAPPED, EXTRACTED}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;MAPPED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.s</font></td>
 
<td>Stranded (Defines whether the reads are stranded. In case of FR_FIRST_STRAND, the first read of a read pair or the only read in case of single-end data is assumed to be located on forward strand of the cDNA, i.e., reverse to the mRNA orientation. If you are using Illumina TruSeq you should use FR_FIRST_STRAND., range={FR_UNSTRANDED, FR_FIRST_STRAND, FR_SECOND_STRAND}, default = FR_UNSTRANDED)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.m</font></td>
 
<td>mapped reads file (BAM/SAM files containing the mapped reads)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.v</font></td>
 
<td>ValidationStringency (Defines how strict to be when reading a SAM or BAM, beyond bare minimum validation., range={STRICT, LENIENT, SILENT}, default = LENIENT)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.u</font></td>
 
<td>use secondary alignments (allows to filter flags in the SAM or BAM, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.c</font></td>
 
<td>coverage (allows to output the coverage, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ERE.mmq</font></td>
 
<td>minimum mapping quality (reads with a mapping quality that is lower than this value will be ignored, valid range = [0, 254], default = 40)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;EXTRACTED&quot;:</b></td></tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">introns</font></td>
 
<td>introns (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage</font></td>
 
<td>coverage (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;UNSTRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_unstranded</font></td>
 
<td>coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;STRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_forward</font></td>
 
<td>coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_reverse</font></td>
 
<td>coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table></td></tr>
 
</table>
 
</td></tr>
 
</table></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">tblastn</font></td>
 
<td>tblastn (if *true* tblastn is used as search algorithm, otherwise mmseqs is used. Tblastn and mmseqs need to be installed to use the corresponding option, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Extractor.p</font></td>
 
<td>proteins (whether the complete proteins sequences should returned as output, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Extractor.r</font></td>
 
<td>repair (if a transcript annotation can not be parsed, the program will try to infer the phase of the CDS parts to repair the annotation, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Extractor.a</font></td>
 
<td>Ambiguity (This parameter defines how to deal with ambiguities in the DNA. There are 3 options: EXCEPTION, which will remove the corresponding transcript, AMBIGUOUS, which will use an X for the corresponding amino acid, and RANDOM, which will randomly select an amnio acid from the list of possibilities., range={EXCEPTION, AMBIGUOUS, RANDOM}, default = EXCEPTION)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Extractor.s</font></td>
 
<td>stop-codon excluded from CDS (A flag that states whether the reference annotation contains the stop codon in the CDS annotation or not, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Extractor.f</font></td>
 
<td>full-length (A flag which allows for choosing between only full-length and all (i.e., full-length and partial) transcripts, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.r</font></td>
 
<td>reads (if introns are given by a GFF, only use those which have at least this number of supporting split reads, valid range = [1, 2147483647], default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.s</font></td>
 
<td>splice (if no intron is given by RNA-seq, compute candidate splice sites or not, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.sm</font></td>
 
<td>substitution matrix (optional user-specified substitution matrix, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.g</font></td>
 
<td>gap opening (The gap opening cost in the alignment, default = 11)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.ge</font></td>
 
<td>gap extension (The gap extension cost in the alignment, default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.m</font></td>
 
<td>maximum intron length (The maximum length of an intron, default = 15000)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.i</font></td>
 
<td>intron-loss-gain-penalty (The penalty used for intron loss and gain, default = 25)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.e</font></td>
 
<td>e-value (The e-value for filtering blast results, default = 100.0)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.c</font></td>
 
<td>contig threshold (The threshold for evaluating contigs, valid range = [0.0, 1.0], default = 0.4)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.rt</font></td>
 
<td>region threshold (The threshold for evaluating regions, valid range = [0.0, 1.0], default = 0.9)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.h</font></td>
 
<td>hit threshold (The threshold for adding additional hits, valid range = [0.0, 1.0], default = 0.9)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.p</font></td>
 
<td>predictions (The (maximal) number of predictions per transcript, default = 10)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.a</font></td>
 
<td>avoid stop (A flag which allows to avoid stop codons in a transcript (except the last AS), default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.approx</font></td>
 
<td>approx (whether an approximation is used to compute the score for intron gain, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.prefix</font></td>
 
<td>prefix (A prefix to be used for naming the predictions, default = )</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.t</font></td>
 
<td>timeout (The (maximal) number of seconds to be used for the predictions of one transcript, if exceeded GeMoMa does not output a prediction for this transcript., valid range = [0, 604800], default = 3600)</td>
 
<td style="width:100px;">LONG</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GeMoMa.Score</font></td>
 
<td>Score (A flag which allows to do nothing, re-score or re-align the search results, range={Trust, ReScore, ReAlign}, default = Trust)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.s</font></td>
 
<td>sorting (comma-separated list that defines the way predictions are sorted within a cluster, default = evidence,score)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.m</font></td>
 
<td>missing intron evidence filter (the filter for single-exon transcripts or if no RNA-seq data is used, decides for overlapping other transcripts whether they should be used (=true) or discarded (=false), default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.i</font></td>
 
<td>intron evidence filter (the filter on the intron evidence given by RNA-seq-data for overlapping transcripts, valid range = [0.0, 1.0], default = 1.0)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.c</font></td>
 
<td>common border filter (the filter on the common borders of transcripts, the lower the more transcripts will be checked as alternative splice isoforms, valid range = [0.0, 1.0], default = 0.75)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.mnotpg</font></td>
 
<td>maximal number of transcripts per gene (the maximal number of allowed transcript predictions per gene, valid range = [1, 2147483647], default = 2147483647)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">GAF.f</font></td>
 
<td>filter (A filter can be applied to transcript predictions to receive only reasonable predictions. The filter is applied to the GFF attributes. The deault filter decides based on the completeness of the prediction (start=='M' and stop=='*') and the relative score (score/AA>=0.75) whether a prediction is used or not. Different criteria can be combined using 'and ' and 'or'. A more sophisticated filter could be applied for instance using the completeness, the relative score, the evidence as well as statistics based on RNA-seq: start=='M' and stop =='*' and score/AA>=0.75 and (evidence>1 or tpc==1.0), default = start=='M' and stop =='*' and score/AA>=0.75, OPTIONAL)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.u</font></td>
 
<td>UTR (allows to predict UTRs using RNA-seq data, range={NO, YES}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>No parameters for selection &quot;YES&quot;</b></td></tr>
 
</table></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.r</font></td>
 
<td>rename (allows to generate generic gene and transcripts names (cf. attribute &quot;Name&quot;), range={COMPOSED, SIMPLE, NO}, default = COMPOSED)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>Parameters for selection &quot;COMPOSED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.p</font></td>
 
<td>prefix (the prefix of the generic name)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.i</font></td>
 
<td>infix (the infix of the generic name, default = G)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.s</font></td>
 
<td>suffix (the suffix of the generic name, default = 0)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.d</font></td>
 
<td>digits (the number of informative digits, valid range = [4, 10], default = 5)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.di</font></td>
 
<td>delete infix (a comma-separated list of infixes that is deleted from the sequence names before building the gene/transcript name, default = )</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;SIMPLE&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.p</font></td>
 
<td>prefix (the prefix of the generic name)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">AnnotationFinalizer.d</font></td>
 
<td>digits (the number of informative digits, valid range = [4, 10], default = 5)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
</table></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>predicted proteins (If *true*, returns the predicted proteins of the target organism as fastA file, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">pc</font></td>
 
<td>predicted CDSs (If *true*, returns the predicted CDSs of the target organism as fastA file, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">pgr</font></td>
 
<td>predicted genomic regions (If *true*, returns the genomic regions of predicted gene models of the target organism as fastA file, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">o</font></td>
 
<td>output individual predictions (If *true*, returns the predictions for each reference species, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">d</font></td>
 
<td>debug (If *false* removes all temporary files even if the jobs exits unexpected, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">threads</font></td>
 
<td>The number of threads used for the tool, defaults to 1</td>
 
<td>INT</td>
 
</tr>
 
</table>
 
 
 
=== Extract RNA-seq Evidence (ERE) ===
 
 
 
For post-processing the mapped RNA-seq data, we provide the tool ExtractRNAseqEvidence (ERE). You can run Extractor from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI ERE [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>Stranded (Defines whether the reads are stranded. In case of FR_FIRST_STRAND, the first read of a read pair or the only read in case of single-end data is assumed to be located on forward strand of the cDNA, i.e., reverse to the mRNA orientation. If you are using Illumina TruSeq you should use FR_FIRST_STRAND., range={FR_UNSTRANDED, FR_FIRST_STRAND, FR_SECOND_STRAND}, default = FR_UNSTRANDED)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">m</font></td>
 
<td>mapped reads file (BAM/SAM files containing the mapped reads)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">v</font></td>
 
<td>ValidationStringency (Defines how strict to be when reading a SAM or BAM, beyond bare minimum validation., range={STRICT, LENIENT, SILENT}, default = LENIENT)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">u</font></td>
 
<td>use secondary alignments (allows to filter flags in the SAM or BAM, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>coverage (allows to output the coverage, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">mmq</font></td>
 
<td>minimum mapping quality (reads with a mapping quality that is lower than this value will be ignored, valid range = [0, 254], default = 40)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== CheckIntrons===
 
 
 
This tool allows to check whether the extracted introns show the expected patterns of di-nucleotides at the splice sites. You can run CheckIntrons from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI CheckIntrons [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">t</font></td>
 
<td>target genome (The target genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>introns (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">v</font></td>
 
<td>verbose (A flag which allows to output a wealth of additional information per transcript, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== Extractor ===
 
 
 
For preparing the data, we provide the tool Extractor. You can run Extractor from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI Extractor [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>annotation (Reference annotation file (GFF or GTF), which contains gene models annotated in the reference genome)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>genome (Reference genome file (FASTA))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">gc</font></td>
 
<td>genetic code (optional user-specified genetic code, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>proteins (whether the complete proteins sequences should returned as output, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>cds (whether the complete CDSs should returned as output, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">genomic</font></td>
 
<td>genomic (whether the genomic regions should be returned (upper case = coding, lower case = non coding), default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">r</font></td>
 
<td>repair (if a transcript annotation can not be parsed, the program will try to infer the phase of the CDS parts to repair the annotation, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>selected (The path to list file, which allows to make only a predictions for the contained transcript ids. The first column should contain transcript IDs as given in the annotation. Remaining columns will be ignored., OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Ambiguity</font></td>
 
<td>Ambiguity (This parameter defines how to deal with ambiguities in the DNA. There are 3 options: EXCEPTION, which will remove the corresponding transcript, AMBIGUOUS, which will use an X for the corresponding amino acid, and RANDOM, which will randomly select an amnio acid from the list of possibilities., range={EXCEPTION, AMBIGUOUS, RANDOM}, default = EXCEPTION)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">sefc</font></td>
 
<td>stop-codon excluded from CDS (A flag that states whether the reference annotation contains the stop codon in the CDS annotation or not, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">f</font></td>
 
<td>full-length (A flag which allows for choosing between only full-length and all (i.e., full-length and partial) transcripts, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">v</font></td>
 
<td>verbose (A flag which allows to output a wealth of additional information, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== Gene Model Mapper (GeMoMa) ===
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>search results (The search results, e.g., from tblastn or mmseqs)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">t</font></td>
 
<td>target genome (The target genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>cds parts (The query cds parts file (FASTA), i.e., the cds parts that have been blasted)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>assignment (The assignment file, which combines parts of the CDS to transcripts, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">q</font></td>
 
<td>query proteins (optional query protein file (FASTA) for computing the optimal alignment score against complete protein prediction, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>introns (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">r</font></td>
 
<td>reads (if introns are given by a GFF, only use those which have at least this number of supporting split reads, valid range = [1, 2147483647], default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">splice</font></td>
 
<td>splice (if no intron is given by RNA-seq, compute candidate splice sites or not, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage</font></td>
 
<td>coverage (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;UNSTRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_unstranded</font></td>
 
<td>coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;STRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_forward</font></td>
 
<td>coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_reverse</font></td>
 
<td>coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table></td></tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>genetic code (optional user-specified genetic code, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">sm</font></td>
 
<td>substitution matrix (optional user-specified substitution matrix, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">go</font></td>
 
<td>gap opening (The gap opening cost in the alignment, default = 11)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ge</font></td>
 
<td>gap extension (The gap extension cost in the alignment, default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">m</font></td>
 
<td>maximum intron length (The maximum length of an intron, default = 15000)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">intron-loss-gain-penalty</font></td>
 
<td>intron-loss-gain-penalty (The penalty used for intron loss and gain, default = 25)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">e</font></td>
 
<td>e-value (The e-value for filtering blast results, default = 100.0)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ct</font></td>
 
<td>contig threshold (The threshold for evaluating contigs, valid range = [0.0, 1.0], default = 0.4)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">rt</font></td>
 
<td>region threshold (The threshold for evaluating regions, valid range = [0.0, 1.0], default = 0.9)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">h</font></td>
 
<td>hit threshold (The threshold for adding additional hits, valid range = [0.0, 1.0], default = 0.9)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>predictions (The (maximal) number of predictions per transcript, default = 10)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">selected</font></td>
 
<td>selected (The path to list file, which allows to make only a predictions for the contained transcript ids. The first column should contain transcript IDs as given in the annotation. Remaining columns can be used to determine a target region that should be overlapped by the prediction, if columns 2 to 5 contain chromosome, strand, start and end of region, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">as</font></td>
 
<td>avoid stop (A flag which allows to avoid stop codons in a transcript (except the last AS), default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">approx</font></td>
 
<td>approx (whether an approximation is used to compute the score for intron gain, default = true)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">prefix</font></td>
 
<td>prefix (A prefix to be used for naming the predictions, default = )</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">tag</font></td>
 
<td>tag (A user-specified tag for transcript predictions in the third column of the returned gff. It might be beneficial to set this to a specific value for some genome browsers., default = prediction)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">v</font></td>
 
<td>verbose (A flag which allows to output a wealth of additional information per transcript, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">timeout</font></td>
 
<td>timeout (The (maximal) number of seconds to be used for the predictions of one transcript, if exceeded GeMoMa does not output a prediction for this transcript., valid range = [0, 604800], default = 3600)</td>
 
<td style="width:100px;">LONG</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">sort</font></td>
 
<td>sort (A flag which allows to sort the search results, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">Score</font></td>
 
<td>Score (A flag which allows to do nothing, re-score or re-align the search results, range={Trust, ReScore, ReAlign}, default = Trust)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
GeMoMa returns the predicted annotation as gff file.
 
 
 
=== GeMoMa Annotation Filter (GAF) ===
 
 
 
The GeMoMa Annotation Filter allows to combine and reduce predictions from GeMoMa into a single final prediction. It is able to handle predictions from different reference species. It also handles overlapping or identical predictions.
 
You can run GeMoMa from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI GAF[&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">t</font></td>
 
<td>tag (the tag used to read the GeMoMa annotations, default = prediction)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>sorting (comma-separated list that defines the way predictions are sorted within a cluster, default = evidence,score)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">m</font></td>
 
<td>missing intron evidence filter (the filter for single-exon transcripts or if no RNA-seq data is used, decides for overlapping other transcripts whether they should be used (=true) or discarded (=false), default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>intron evidence filter (the filter on the intron evidence given by RNA-seq-data for overlapping transcripts, valid range = [0.0, 1.0], default = 1.0)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>common border filter (the filter on the common borders of transcripts, the lower the more transcripts will be checked as alternative splice isoforms, valid range = [0.0, 1.0], default = 0.75)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">mnotpg</font></td>
 
<td>maximal number of transcripts per gene (the maximal number of allowed transcript predictions per gene, valid range = [1, 2147483647], default = 2147483647)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>prefix (the prefix can be used to distinguish predictions from different input files, OPTIONAL)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">w</font></td>
 
<td>weight (the weight can be used to prioritize predictions from different input files; each prediction will get an additional attribute sumWeight that can be used in the filter, valid range = [0.0, 1000.0], default = 1.0, OPTIONAL)</td>
 
<td style="width:100px;">DOUBLE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>gene annotation file (GFF files containing the gene annotations (predicted by GeMoMa))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">f</font></td>
 
<td>filter (A filter can be applied to transcript predictions to receive only reasonable predictions. The filter is applied to the GFF attributes. The deault filter decides based on the completeness of the prediction (start=='M' and stop=='*') and the relative score (score/AA>=0.75) whether a prediction is used or not. Different criteria can be combined using 'and ' and 'or'. A more sophisticated filter could be applied for instance using the completeness, the relative score, the evidence as well as statistics based on RNA-seq: start=='M' and stop =='*' and score/AA>=0.75 and (evidence>1 or tpc==1.0), default = start=='M' and stop =='*' and score/AA>=0.75, OPTIONAL)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== CompareTranscripts ===
 
 
 
For comparing gene models from GeMoMa predictions with existing annotation, we provide the tool CompareTranscripts. You can run CompareTranscripts from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI CompareTranscripts [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>prediction (The predicted annotation)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>annotation (The true annotation)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== AnnotationEvidence ===
 
 
 
For providing RNA-seq evidence (e.g. tie) for existing annotation, we provide the tool AnnotationEvidence. You can run AnnotationEvidence from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI AnnotationEvidence [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>annotation (The genome annotation file (GFF))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>genome (The genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>introns file (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">r</font></td>
 
<td>reads (if introns are given by a GFF, only use those which have at least this number of supporting split reads, valid range = [1, 2147483647], default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>coverage file (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;UNSTRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_unstranded</font></td>
 
<td>coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;STRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_forward</font></td>
 
<td>coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_reverse</font></td>
 
<td>coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table></td></tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">ao</font></td>
 
<td>annotation output (if the annotation should be returned with attributes tie, tpc, and AA, default = false)</td>
 
<td style="width:100px;">BOOLEAN</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">gc</font></td>
 
<td>genetic code (optional user-specified genetic code, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
 
 
=== AnnotationFinializer ===
 
 
 
This tool allows to predict UTR and to rename predictions. You can run AnnotationEvidence from the command line with<br/>
 
<code>java -jar GeMoMa-1.6.1.jar CLI AnnotationFinalizer [&lt;parameter&gt;=&lt;value&gt; ...]</code><br/>
 
The parameters comprise:
 
 
 
<table border=0 cellpadding=10 align="center" width="100%">
 
<tr>
 
<td>name</td>
 
<td>comment</td>
 
<td>type</td>
 
</tr>
 
<tr><td colspan=3><hr></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">g</font></td>
 
<td>genome (The genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">a</font></td>
 
<td>annotation (The predicted genome annotation file (GFF))</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">t</font></td>
 
<td>tag (A user-specified tag for transcript predictions in the third column of the returned gff. It might be beneficial to set this to a specific value for some genome browsers., default = prediction)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">u</font></td>
 
<td>UTR (allows to predict UTRs using RNA-seq data, range={NO, YES}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;YES&quot;:</b></td></tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">i</font></td>
 
<td>introns file (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table>
 
</td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">r</font></td>
 
<td>reads (if introns are given by a GFF, only use those which have at least this number of supporting split reads, valid range = [1, 2147483647], default = 1)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3>The following parameter(s) can be used multiple times:</td></tr>
 
<tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr style="vertical-align:top">
 
<td><font color="green">c</font></td>
 
<td>coverage file (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;UNSTRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_unstranded</font></td>
 
<td>coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;STRANDED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_forward</font></td>
 
<td>coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">coverage_reverse</font></td>
 
<td>coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.)</td>
 
<td style="width:100px;">FILE</td>
 
</tr>
 
</table></td></tr>
 
</table>
 
</td></tr>
 
</table></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">rename</font></td>
 
<td>rename (allows to generate generic gene and transcripts names (cf. attribute &quot;Name&quot;), range={COMPOSED, SIMPLE, NO}, default = COMPOSED)</td>
 
<td style="width:100px;"></td></tr><tr><td></td><td colspan=2><table border=0 cellpadding=0 align="center" width="100%">
 
<tr><td colspan=3><b>Parameters for selection &quot;COMPOSED&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>prefix (the prefix of the generic name)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">infix</font></td>
 
<td>infix (the infix of the generic name, default = G)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">s</font></td>
 
<td>suffix (the suffix of the generic name, default = 0)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">d</font></td>
 
<td>digits (the number of informative digits, valid range = [4, 10], default = 5)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">di</font></td>
 
<td>delete infix (a comma-separated list of infixes that is deleted from the sequence names before building the gene/transcript name, default = )</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr><td colspan=3><b>Parameters for selection &quot;SIMPLE&quot;:</b></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">p</font></td>
 
<td>prefix (the prefix of the generic name)</td>
 
<td style="width:100px;">STRING</td>
 
</tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">d</font></td>
 
<td>digits (the number of informative digits, valid range = [4, 10], default = 5)</td>
 
<td style="width:100px;">INT</td>
 
</tr>
 
<tr><td colspan=3><b>No parameters for selection &quot;NO&quot;</b></td></tr>
 
</table></td></tr>
 
<tr style="vertical-align:top">
 
<td><font color="green">outdir</font></td>
 
<td>The output directory, defaults to the current working directory (.)</td>
 
<td>STRING</td>
 
</tr>
 
</table>
 
  
 
== GFF attributes ==
 
== GFF attributes ==
  
Using GeMoMa and GAF, you'll obtain GFFs containing some special attributes. We briefly explain the most prominent attributes in the following table.
+
Using GeMoMa, you'll obtain GFFs containing some special attributes. We briefly explain the most prominent attributes in the following table.
  
 
{| class="wikitable sortable"
 
{| class="wikitable sortable"
Line 1,116: Line 57:
 
!Attribute !!Long name !!Tool !!Necessary parameter !!Feature !!Description
 
!Attribute !!Long name !!Tool !!Necessary parameter !!Feature !!Description
 
|-
 
|-
| score || GeMoMa score || GeMoMa || || prediction || score computed by GeMoMa using the substitution matrix, gap costs and additional penalties
+
| aa || amino acids || GeMoMa || || mRNA || the number of amino acids in the protein
 +
|-
 +
| score || GeMoMa score || GeMoMa || || mRNA || score computed by GeMoMa using the substitution matrix, gap costs and additional penalties
 +
|-
 +
| nps || number of premature stops || GeMoMa || || mRNA || the number of premature stop codons in the prediction
 
|-
 
|-
| minCov || minimal coverage || GeMoMa || coverage, ... || prediction || minimal coverage of any base of the prediction given RNA-seq evidence
+
| ce || coding exons || GeMoMa || assignment || mRNA || the number of coding exons of the prediction
 
|-
 
|-
| avgCov || average coverage || GeMoMa || coverage, ... || prediction || average coverage of all bases of the prediction given RNA-seq evidence
+
| rce || reference coding exons || GeMoMa || assignment || mRNA || the number of coding exons of the reference transcript
 
|-
 
|-
| tpc || transcript percentage coverage || GeMoMa || coverage, ... || prediction || percentage of covered bases per predicted transcript given RNA-seq evidence
+
| minCov || minimal coverage || GeMoMa || coverage, ... || mRNA || minimal coverage of any base of the prediction given RNA-seq evidence
 
|-
 
|-
| tae || transcript acceptor evidence || GeMoMa || introns || prediction || percentage of predicted acceptor sites per predicted transcript with RNA-seq evidence
+
| avgCov || average coverage || GeMoMa || coverage, ... || mRNA || average coverage of all bases of the prediction given RNA-seq evidence
 
|-
 
|-
| tde || transcript donor evidence || GeMoMa || introns || prediction || percentage of predicted donor sites per predicted transcript with RNA-seq evidence
+
| tpc || transcript percentage coverage || GeMoMa || coverage, ... || mRNA || percentage of covered bases per predicted transcript given RNA-seq evidence
 
|-
 
|-
| tie || transcript intron evidence || GeMoMa || introns || prediction || percentage of predicted introns per predicted transcript with RNA-seq evidence
+
| tae || transcript acceptor evidence || GeMoMa || introns || mRNA || percentage of predicted acceptor sites per predicted transcript with RNA-seq evidence
 
|-
 
|-
| minSplitReads || minimal split reads || GeMoMa || introns || prediction || minimal number of split reads for any of the predicted introns per predicted transcript
+
| tde || transcript donor evidence || GeMoMa || introns || mRNA || percentage of predicted donor sites per predicted transcript with RNA-seq evidence
 
|-
 
|-
| iAA || identical amino acid || GeMoMa || query proteins || prediction || percentage of identical amino acids between reference transcript and prediction
+
| tie || transcript intron evidence || GeMoMa || introns || mRNA || percentage of predicted introns per predicted transcript with RNA-seq evidence
 
|-
 
|-
| pAA || positive amino acid || GeMoMa || query proteins || prediction || percentage of aligned positions between reference transcript and prediction yielding a positive score in the substitution matrix
+
| minSplitReads || minimal split reads || GeMoMa || introns || mRNA || minimal number of split reads for any of the predicted introns per predicted transcript
 
|-
 
|-
| evidence || || GAF || || prediction || number of reference organisms that have a transcript yielding this prediction
+
| iAA || identical amino acid || GeMoMa || query proteins || mRNA || percentage of identical amino acids between reference transcript and prediction
 
|-
 
|-
| alternative || || GAF || || prediction || alternative gene ID(s) leading to the same prediction
+
| pAA || positive amino acid || GeMoMa || query proteins || mRNA || percentage of aligned positions between reference transcript and prediction yielding a positive score in the substitution matrix
 
|-
 
|-
| sumWeight || || GAF || || prediction || the sum of the weights of the references that perfectly support this prediction
+
| evidence || || GAF || || mRNA || number of reference organisms that have a transcript yielding this prediction
 +
|-
 +
| alternative || || GAF || || mRNA || alternative gene ID(s) leading to the same prediction
 +
|-
 +
| sumWeight || || GAF || || mRNA || the sum of the weights of the references that perfectly support this prediction
 
|-
 
|-
 
| maxTie || maximal tie || GAF || || gene || maximal tie of all transcripts of this gene
 
| maxTie || maximal tie || GAF || || gene || maximal tie of all transcripts of this gene
Line 1,148: Line 97:
 
|}
 
|}
  
== FAQs ==
+
The name of the feature describing a transcript prediction can be altered using the parameter "tag". Before version 1.7 the default value of tag was "prediction" instead of "mRNA".
 +
 
 +
== Frequently asked questions ==
  
 
; Why does the Extractor not return a single CDS-part, protein, ...?
 
; Why does the Extractor not return a single CDS-part, protein, ...?
: First, please check whether the names of your contigs/chromosomes in your annotation (gff) and genome file (fasta) are identical. The fasta comments should at best only contain the contig/chromosome name. (Since GeMoMa 1.4, comments, which contain the contig/chromosome name and some additional information separated by a space, are also fine.) Second, please check whether you have a valid GFF/GTF file. Valid GFF files should have a valid "ID" or "Parent" entry in the attributes column. Valid GTF files should have a valid "gene_id" and "transcript_id" entry. Finally, please check the statistics that are given by the Extractor. It lists how many genes have been read and how many genes have been removed for different reasons. One common problem is that some annotation files do not include the stop codon in the CDS annotation.
+
:Please check whether the names of your contigs/chromosomes in your annotation (gff) and genome file (fasta) are identical. The fasta comments should at best only contain the contig/chromosome name. (Since GeMoMa 1.4, comments, which contain the contig/chromosome name and some additional information separated by a space, are also fine.) In addition, check the statistics that are given by the Extractor. It lists how many genes have been read and how many genes have been removed for different reasons. One common problem is that some annotation files do not include the stop codon in the CDS annotation.
 +
 
 
; How can I force GeMoMa to make more predictions?
 
; How can I force GeMoMa to make more predictions?
: There are several parameters affecting the number of predictions. The most prominent are the number of predictions (p) and the contig threshold (ct). For each reference transcript/CDS, GeMoMa initially makes a preliminary prediction and uses this prediction to determine whether a contig is promising and should be used to determine the final predictions. You may decrease ct and increase p to have more contigs in the final prediction. Increasing the number of predictions allows GeMoMa to output more predictions that have been computed. Decreasing the contig threshold allows to increase the number of predictions that are (internally) computed. Increasing p to a very large number without decreasing ct does not help.
+
:There are several parameters affecting the number of predictions. The most prominent are the number of predictions (p) and the contig threshold (ct). For each reference transcript/CDS, GeMoMa initially makes a preliminary prediction and uses this prediction to determine whether a contig is promising and should be used to determine the final predictions. You may decrease ct and increase p to have more contigs in the final prediction. Increasing the number of predictions allows GeMoMa to output more predictions that have been computed. Decreasing the contig threshold allows to increase the number of predictions that are (internally) computed. Increasing p to a very large number without decreasing ct does not help.
 +
 
 
; Running GeMoMa on a single contig of my assembly yield thousands of weird predictions. What went wrong?
 
; Running GeMoMa on a single contig of my assembly yield thousands of weird predictions. What went wrong?
: By default, GeMoMa is not build to be run on a single contig. GeMoMa tries to make predictions for all given reference CDS in the given target sequence(s). If the given target sequence is only a fraction of the complete target genome/assembly, GeMoMa will produce weird predictions as it does not filter for the quality of the predictions internally. There are two options to handle this:
+
:By default, GeMoMa is not build to be run on a single contig. GeMoMa tries to make predictions for all given reference CDS in the given target sequence(s). If the given target sequence is only a fraction of the complete target genome/assembly, GeMoMa will produce weird predictions as it does not filter for the quality of the predictions internally. There are two options to handle this:
 
:* Use a list of gene models that you expect to be located on this contig (cf. parameter "selected").
 
:* Use a list of gene models that you expect to be located on this contig (cf. parameter "selected").
 
:* Filter the predictions using GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>).
 
:* Filter the predictions using GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>).
 +
 
; Is it mandatory to use RNA-seq data?
 
; Is it mandatory to use RNA-seq data?
: No, GeMoMa is able to make predictions with and without RNA-seq evidence.
+
:No, GeMoMa is able to make predictions with and without RNA-seq evidence.
 +
 
 
; Is it possible to use multiple reference organisms?
 
; Is it possible to use multiple reference organisms?
: It is possible to use multiple reference organisms for GeMoMa. Just run GeMoMa on each reference organism separately. Finally, you can employ GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>) to combine these annotations.
+
:It is possible to use multiple reference organisms for GeMoMa. Just run GeMoMa on each reference organism separately. Finally, you can employ GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>) to combine these annotations.
 +
 
 
; Why do some reference genes not lead to a prediction in the target genome?
 
; Why do some reference genes not lead to a prediction in the target genome?
: Please first check whether your reference genes have been discarded by the Extractor (cf. assignment file).
+
:Please first check whether your reference genes have been discarded by the Extractor (cf. assignment file).
: If the genes have been discarded, there are two possibilities:
+
:If the genes have been discarded, there are two possibilities:
 
:* The CDS might be redundant, i.e. the coding exons are identical to those of another transcript. In this case, only one CDS is further evaluated.
 
:* The CDS might be redundant, i.e. the coding exons are identical to those of another transcript. In this case, only one CDS is further evaluated.
 
:* There might be something wrong with your reference genes, e.g., missing start codon, missing stop codon, premature stop codon, ambiguous nucleotides, ... and you should check the options of Extractor or the annotation.
 
:* There might be something wrong with your reference genes, e.g., missing start codon, missing stop codon, premature stop codon, ambiguous nucleotides, ... and you should check the options of Extractor or the annotation.
: If the reference genes passed the Extractor, there are several possible explanations for this behavior. The two most prominent are:
+
:If the reference genes passed the Extractor, there are several possible explanations for this behavior. The two most prominent are:
 
:* GeMoMa stopped the prediction of a reference genes since it does not return a result within the given time (cf. parameter "timeout").
 
:* GeMoMa stopped the prediction of a reference genes since it does not return a result within the given time (cf. parameter "timeout").
:* GeMoMa simply did not find a prediction matching the remaining quality criteria
+
:* GeMoMa simply did not find a prediction matching the remaining quality criteria.
:* GeMoMa did find a prediction, but it was filtered out by GAF, e.g. to low relative score, missing start or stop codon (cf. GAF parameters).
+
 
 
; What does "partial gene model" mean in the context of GeMoMa?
 
; What does "partial gene model" mean in the context of GeMoMa?
: We called a gene model partial if it does not contain an initial start codon and a final stop codon. However, this does not mean that the gene model is located at or close to the border of a chromosome or contig.
+
:We called a gene model partial if it does not contain an initial start codon and a final stop codon. However, this does not mean that the gene model is located at or close to the border of a chromosome or contig.
 +
 
 
; For two different reference transcripts, the predictions of GeMoMa overlap or are identical. What should I do with those?
 
; For two different reference transcripts, the predictions of GeMoMa overlap or are identical. What should I do with those?
: GeMoMa makes the predictions for each reference transcript independently. Hence, it can occur that some of predictions of different reference transcripts overlap or are identical especially in gene families. Typically, you might like to filter or rank these predictions. We have implemented GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>) to do this automatically. However, you can also do it by hand using the GFF attributes. Using RNA-seq data in GeMoMa yields additional fields in the annotation that can be used, e.g., average coverage (avgCov).
+
:GeMoMa makes the predictions for each reference transcript independently. Hence, it can occur that some of predictions of different reference transcripts overlap or are identical especially in gene families. Typically, you might like to filter or rank these predictions. We have implemented GAF (cf. <code>java -jar GeMoMa-<version>.jar CLI GAF</code>) to do this automatically. However, you can also do it by hand using the GFF attributes. Using RNA-seq data in GeMoMa yields additional fields in the annotation that can be used, e.g., average coverage (avgCov).
 +
 
 
; A lot of transcripts have been filtered out by the Extractor. What can I do?
 
; A lot of transcripts have been filtered out by the Extractor. What can I do?
: There are several reasons for removing transcripts by the Extractor. At least in two cases you can try to get more transcripts by setting specific parameter values. First, if the transcript contains ambiguous nucleotides, please test the parameter "Ambiguity". Second, sometimes we received GFFs which contain wrong phases for CDS entries (e.g., 0 for all CDS entries in the phase column of the GFF). Since version 1.3.2, we provide the option "r" which stands for repair. If r=true is chosen, the Extractor tries to infer all phases for transcripts that show an error and would be filtered out.
+
:There are several reasons for removing transcripts by the Extractor. At least in two cases you can try to get more transcripts by setting specific parameter values. First, if the transcript contains ambiguous nucleotides, please test the parameter "Ambiguity". Second, sometimes we received GFFs which contain wrong phases for CDS entries (e.g., 0 for all CDS entries in the phase column of the GFF). Since version 1.3.2, we provide the option "r" which stands for repair. If r=true is chosen, the Extractor tries to infer all phases for transcripts that show an error and would be filtered out.
 +
 
 
; Is GeMoMa able to predict pseudo-genes/ncRNA?
 
; Is GeMoMa able to predict pseudo-genes/ncRNA?
: No, currently not.
+
:No, currently not.
 +
 
 
; My RNA-seq data indicates there is an additional intron in a transcipt, but GeMoMa does not predict this. Or vice versa, GeMoMa predicts an intron that is not supported by RNA-seq data. What's the reason?
 
; My RNA-seq data indicates there is an additional intron in a transcipt, but GeMoMa does not predict this. Or vice versa, GeMoMa predicts an intron that is not supported by RNA-seq data. What's the reason?
: GeMoMa is mainly based on the assumptions of amino acid and intron position conservation between reference and target species. Hence, GeMoMa tries to predict a gene model with similar exon-intron structure in the target species and does not stick too much to RNA-seq data. Although intron position conservation can be observed in most cases, sometimes new introns evolve or others vanish. For this reasons, GeMoMa also allows for the inclusion or exclusion of introns adding some additional costs (cf. GeMoMa parameter intron-loss-gain-penalty). However, the behaviour of GeMoMa depends on the parameters settings (especially intron-loss-gain-penalty, sm (substitution matrix), go (gap opening), ge (gap extension)) and the length of the missed/additional intron. Nevertheless, such cases can only occur if the additional/missed intron has a length that can be divided by 3 preserving the reading frame.
+
:GeMoMa is mainly based on the assumptions of amino acid and intron position conservation between reference and target species. Hence, GeMoMa tries to predict a gene model with similar exon-intron-structure in the target species and does not stick too much with RNA-seq data. Although intron position conservation can be observed in most cases, sometimes new introns evolve or others vanish. For this reasons, GeMoMa also allows for the inclusion or exclusion of introns adding some additional costs (cf. GeMoMa parameter intron-loss-gain-penalty). However, the behaviour of GeMoMa depends on the parameters settings (especially intron-loss-gain-penalty, sm (substitution matrix), go (gap opening), ge (gap extension)) and the length of the missed/additional intron. Nevertheless, such cases can only occur if the additional/missed intron has a length that can be divided by 3 preserving the reading frame. Since the available RNA-seq data only reflects a fraction of tissues/environmental conditions/..., missing RNA-seq evidence does not necessarily mean that the predictions is wrong.
:Since the available RNA-seq data only reflects a fraction of tissues/environmental conditions/..., missing RNA-seq evidence does not necessarily mean that the predictions is wrong.
+
 
 
; My RNA-seq data indicates two alternative, highly overlapping introns. Interestingly, GeMoMa does not take the intron that is more abundant. Why?
 
; My RNA-seq data indicates two alternative, highly overlapping introns. Interestingly, GeMoMa does not take the intron that is more abundant. Why?
: GeMoMa reads the introns from the input file using some filter (cf. GeMoMa parameter r (reads)). All introns that pass the filter are used and treated equally. Hence, GeMoMa uses the intron that matches the expectation of intron position and amino acid conservation compared to the reference transcript.
+
:GeMoMa reads the introns from the input file using some filter (cf. GeMoMa parameter r (reads)). All introns that pass the filter are used and treated equally. Hence, GeMoMa uses the intron that matches the expectation of intron position and amino acid conservation compared to the reference transcript.
 +
 
 
; Does GeMoMa predict multiple transcripts per gene?
 
; Does GeMoMa predict multiple transcripts per gene?
: GeMoMa in principle allows to predict multiple transcripts per gene, if corresponding transcripts are given in the reference species or if multiple reference species are used.
+
:GeMoMa in principle allows to predict multiple transcripts per gene, if corresponding transcripts are given in the reference species or if multiple reference species are used.  
 +
 
 
; GeMoMa failed with java.lang.OutOfMemoryError. What can I do?
 
; GeMoMa failed with java.lang.OutOfMemoryError. What can I do?
: Whenever you see a java.lang.OutOfMemoryError, you should rerun the program with Java virtual machine (VM) options. More specifically you should set: '''-Xms''' the initally used RAM, e.g. to 5Gb (–Xms5G), and '''-Xmx''' the maximally used RAM, e.g. to 50Gb (-Xmx50G). GeMoMa often needs more memory if you have a large genome ''and'' if you’re providing a large coverage file (extracted from RNA-seq data). If you don’t have a compute node with enough memory, you can run GeMoMa without coverage, which will return the same predictions, but does not include all statistics. Another point could be the protein alignment, if you use the optional parameter query protein. Again you can run GeMoMa without this parameter, which will return the same predictions, but less statistics.
+
:Whenever you see a java.lang.OutOfMemoryError, you should rerun the program with Java virtual machine (VM) options. More specifically you should set: -Xms the initially used RAM, e.g. to 5Gb (–Xms5G), and -Xmx the maximally used RAM, e.g. to 50Gb (-Xmx50G). GeMoMa often needs more memory if you have a large genome and if you’re providing a large coverage file (extracted from RNA-seq data). If you don’t have a compute node with enough memory, you can run GeMoMa without coverage, which will return the same predictions, but does not include all statistics. Another point could be the protein alignment, if you use the optional parameter "query protein" (below version 1.; or "protein alignment" (since version 1.;. Again you can run GeMoMa without protein alignment, which will return the same predictions, but less statistics.  
 +
 
 
; I need to specify the genetic code for my organisms. What is the expected format?
 
; I need to specify the genetic code for my organisms. What is the expected format?
: The genetic code is given in a two column tab-delimited table, where the first column is the one letter code of the amino acid and the second column is a comma-separated list of triplets. As we are working on genomic DNA, GeMoMa expects the bases A, C, G, and T, and not U (as expected in mRNA). Here is the link to the default genetic code, which might be used as template:
+
:The genetic code is given in a two column tab-delimited table, where the first column is the one letter code of the amino acid and the second column is a comma-separated list of triplets. As we are working on genomic DNA, GeMoMa expects the bases A, C, G, and T, and not U (as expected in mRNA). Here is the link to the default genetic code, which might be used as template:
: https://github.com/Jstacs/Jstacs/blob/master/projects/gemoma/test_data/genetic_code.txt
+
:https://github.com/Jstacs/Jstacs/blob/master/projects/gemoma/test_data/genetic_code.txt
: Alternative genetic codes are described here using the RNA alphabet:
+
:Alternative genetic codes are described here using the RNA alphabet:
: https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi
+
:https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi
: The genetic code might be specified for a reference organism in the module Extractor or for a target organism in the module GeMoMa.
+
:The genetic code might be specified for a reference organism in the module Extractor or for a target organism in the module GeMoMa.
 +
 
 +
; I like to accelerate GeMoMa. What can I do?
 +
:You can use several threads for the computation. If you run the GeMoMaPipeline you just have to select threads=<your_number>.
 +
:In addition, you can change the search algorithm that is used in GeMoMa. Tblastn is used by default as search algorithm in GeMoMa (for historical reasons, until version 1.6.4). However, tblastn can be replaced by mmseqs which is typically much faster. If you run the GeMoMaPipeline you just have to select tblastn=false, which is default since version 1.7. However, changing the search algorithm can also effect the results. We try to minimize these effect using specific parameters for the search algorithms.
 +
:If you modify other parameters, you will probably receive results that differ to a larger extend from those received using the default parameters. 
 +
 
 +
; Is there a way to use GeMoMa to search a single CDS or protein sequence against a genome and return the predicted gene model (CDS fasta, protein fasta, GFF) similar to exonerate?
 +
:There at least two ways to do this. If you use GeMoMaPipeline you can
 +
: (A) Use the parameter “selected” to select specific gene models (=transcripts/proteins) from the annotation instead of using all or
 +
: (B) Use s=pre-extracted, use a fasta file with the proteins for the parameter cds-parts and leave assignment unset.
 +
:Using one of these options you can either look for a single or few transcripts/proteins either with (A) or without (B) intron-position conservation. In addition, you can use RNA-seq data to improve the predictions, which should be not possible with exonerate.
 +
 
 +
; Can I determine synteny based on GeMoMa predictions?
 +
:Yes, since version 1.7 we provide the module SyntenyChecker and a R script that can be used for this purpose. It exploits the fact the the reference gene and the alternative are known. Hence no alignment is need at this point and synteny can be determined quite fast.
 +
 
 +
; How, can I add additional attributes to the annotation?
 +
:Additional attribute, e.g., functional annotation from InterProScan, can be added to the structural gene annotation using the module AddAttribute, which has been included since version 1.7. Such additional attributes might be used in GAF for filtering and sorting and can also be displayed in genome browsers like IGV or WebApollo.
 +
 
 +
; Can structural gene annotation provided by GeMoMa be submitted to NCBI?
 +
:Yes, NCBI allows to submit structural gene annotation in GFF format (https://www.ncbi.nlm.nih.gov/genbank/genomes_gff/). If you run GeMoMaPipeline or AnnotationFinalizer, the GFF should be valid for conversion.
 +
 
 +
; Running GeMoMaPipeline throws an exception. Can I restart GeMoMaPipeline using intermediate results?
 +
:Yes, since version 1.7 we have a new parameter in GeMoMaPipeline called restart.
 +
:If you want to restart the last broken GeMoMaPipeline run, you have to execute GeMoMaPipeline with the same command line as before and add restart=true.
 +
:If necessary, you can also slightly change the other parameters. However, if the parameters differ too much from those used before, GeMoMaPipeline will decide to perform a new independent run.
 +
:A restart of GeMoMaPipeline is particularly useful if the time-consuming search (tblastn or mmseqs) was successful, since this can save runtime.
 +
 
 +
If you have any further questions, comments or bugs, please check the [[GeMoMa-Docs]], [https://github.com/Jstacs/Jstacs/labels/GeMoMa our github page] or contact [mailto:jens.keilwagen@julius-kuehn.de Jens Keilwagen].
 +
 
 +
== References ==
 +
If you use GeMoMa, please cite
 +
 
 +
J. Keilwagen, M. Wenk, J. L. Erickson, M. H. Schattat, J. Grau, and F. Hartung. [https://nar.oxfordjournals.org/content/44/9/e89 Using intron position conservation for homology-based gene prediction]. ''Nucleic Acids Research'', 2016. doi: 10.1093/nar/gkw092
 +
 
 +
J. Keilwagen, F. Hartung, M. Paulini, S. O. Twardziok, and J. Grau
 +
[https://rdcu.be/QbKc Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi]. ''BMC Bioinformatics'', 2018. doi: 10.1186/s12859-018-2203-5
  
 
== Version history ==
 
== Version history ==
[http://www.jstacs.de/download.php?which=GeMoMa GeMoMa 1.6.1] (4.06.2019)
+
[http://www.jstacs.de/download.php?which=GeMoMa GeMoMa 1.7.1] (07.09.2020)
 +
*GeMoMa:
 +
**bugfix if assignment == null
 +
**bugfix remove toUpperCase
 +
*GeMoMaPipeline
 +
**Galaxy integration bugfix for hidden parameter restart
 +
**hide BLAST_PATH and MMSEQS_PATH from Galaxy integration
 +
**improved protocol output if threads=1
 +
**add addtional test to GeMoMaPipeline
 +
 
 +
[http://www.jstacs.de/downloads/GeMoMa-1.7.zip GeMoMa 1.7] (29.07.2020)
 +
*improved manual including new module and runtime
 +
*check whether input files exist before execution
 +
*partially checking MIME types in CLI before execution
 +
*changed homepage from http to https
 +
*new module AddAttribute: allows to add attributes (like functional annotation from InterProScan) to gene annotation files that might be used in GAF or displayed genome browsers like IGV or WebApollo
 +
*new module SyntenyChecker: creates a table that can be used to create dot plots between the annotation of the target and reference organism
 +
*changed default value of parameter "tag" from "prediction" to "mRNA"
 +
*AnnotationEvidence:
 +
**additional attributes: avgCov, minCov, nps, ce
 +
**changed default value of "annotation output" to true
 +
**bugfix: transcript start and end
 +
*ERE:
 +
**changed default value of coverage to "true"
 +
**new parameter "minimum context": allows to discard introns if all split reads have short aligned contexts
 +
*Extractor:
 +
**bugfix splitAA if coding exon is very short
 +
**improved verbose mode
 +
**new parameter "upcase IDs"
 +
**new parameter "introns" allowing to extract introns from the reference (only for test cases)
 +
**new parameter "discard pre-mature stop" allowing to discard or use transcripts with pre-mature stop
 +
**improved handling of corrupt annotations
 +
*GAF:
 +
**bugfix missing transcripts
 +
**slightly changed the default value of "filter"
 +
*GeMoMa:
 +
**replaced parameter "query proteins" by "protein alignment"
 +
**using splitAA for scoring predictions
 +
**new gff attributes:
 +
*** ce and rce for the feature prediction indicating the number of coding exons for the prediction and the reference, respectively
 +
*** nps for the number of premature stop codons (if avoid stop is false)
 +
**slightly changed the meaning of the parameter "avoid stop"
 +
*GeMoMaPipeline:
 +
**changed the default value of tblastn to false, hence mmseqs is used as search algorithm
 +
**changed the default value of score to ReAlign
 +
**remove "--dont-split-seq-by-len" from mmseqs createdb
 +
**new optional parameter BLAST_PATH
 +
**new optional parameter MMSEQS_PATH
 +
**new option to allow for incorporation of external annotation, e.g., from ab-initio gene prediction
 +
**new parameter restart allowing to restart the latest GeMoMaPipeline run, which was finished without results, with very similar parameters, e.g., after an exception was thrown (cf. parameter debug)
 +
 
 +
[http://www.jstacs.de/downloads/GeMoMa-1.6.4.zip GeMoMa 1.6.4] (24.04.2020)
 +
* improved help section
 +
* change gff attribute "AA" to "aa"
 +
* GAF:
 +
** bugfix overlapping genes
 +
** accelerated computation
 +
* GeMoMa:
 +
** bugfix: if no assignment file is used and protein ID are prefixes of other protein IDs
 +
** change GFF attribute AA to aa
 +
* AnnotationFinalizer: new parameter "name attribute" allowing to decide whether a name attribute or the Parent and ID attributes should be used for renaming
 +
 
 +
[http://www.jstacs.de/downloads/GeMoMa-1.6.3.zip GeMoMa 1.6.3] (05.03.2020)
 +
* Jstacs changes:
 +
** CLI: bugfix ExpandableParameterSet
 +
* python wrapper (for *conda)
 +
* updated tests.sh, run.sh, pipeline.sh
 +
* rename Denoise to DenoiseIntrons
 +
* AnnotationEvidence: write phase (as given) to gff
 +
* GAF: new parameter: default attributes allows to set attributes that are not included in some gene annotation files
 +
* GeMoMa: new parameter: static intron length allowing to use dynamic intron length if set to false
 +
* GeMoMaPipeline:
 +
** bugfix: time-out
 +
** improve output
 +
** separate parameters for maximum intron length (DenoiseIntrons, GeMoMa)
 +
 
 +
[http://www.jstacs.de/downloads/GeMoMa-1.6.2.zip GeMoMa 1.6.2] (17.12.2019)
 +
* Jstacs changes:
 +
** test methods for modules
 +
** live protocol for Galaxy
 +
* new module Denoise: allowing to clean introns extracted by ERE
 +
* new module NCBIReferenceRetriever: allowing to retrieve data for reference organisms easily from NCBI.
 +
* GAF:
 +
** bugfix for filter using specific attributes if no RNA-seq or query proteins was used
 +
** allow to add annotation info (as for instance provided by Phytozome) based on the reference organisms
 +
* GeMoMa: bugfix for timeout
 +
* GeMoMaPipeline:
 +
** bugfix reporting predicted partial proteins
 +
** improved protocol
 +
** new default value for query proteins (changed from false to true)
 +
** new default value for Ambiguity (changed from EXCEPTION to AMBIGUOUS)
 +
 
 +
[http://www.jstacs.de/downloads/GeMoMa-1.6.1.zip GeMoMa 1.6.1] (4.06.2019)
 
* createGalaxyIntegration.sh: bugfix for GeMoMaPipeline
 
* createGalaxyIntegration.sh: bugfix for GeMoMaPipeline
 
* new module CheckIntrons: allowing to create statistics for introns (extracted by ERE)
 
* new module CheckIntrons: allowing to create statistics for introns (extracted by ERE)

Latest revision as of 14:21, 9 September 2020

Gene Model Mapper (GeMoMa) is a homology-based gene prediction program. GeMoMa uses the annotation of protein-coding genes in a reference genome to infer the annotation of protein-coding genes in a target genome. Thereby, GeMoMa utilizes amino acid sequence and intron position conservation. In addition, GeMoMa allows to incorporate RNA-seq evidence for splice site prediction.

GeMoMa is available in a public web-server at galaxy.informatik.uni-halle.de. The provided web-server only allows a limited number of reference genes and uses a time out of 2 minutes per transcript prediction. For unlimited use, please use the command line program or integrate GeMoMa in your own Galaxy instance.

Schema of GeMoMa algorithm

Installation

GeMoMa is now available via bioconda. Here is the direct link to the package. To install this package with conda run:

conda install -c bioconda gemoma  

However, you can also install GeMoMa manually.

Requirements

For running the GeMoMa, you need the following software on your computer

Download

GeMoMa is implemented in Java using Jstacs. You can download a zip file containing a readme, the GeMoMa jar file and some tiny scripts for running GeMoMa. The jar file allows for

  • creating the XML file needed for the Galaxy integration
  • running the command line interface (CLI) version.

In a nutshell

GeMoMa is a modular, homology-based gene prediction program with huge flexibility. However, we also provide a pipeline allowing to use GeMoMa easily. If you like to start GeMoMa for the first time, we recommend to use the GeMoMaPipeline like this

java -jar GeMoMa-1.7.1.jar CLI GeMoMaPipeline threads=<threads> outdir=<outdir> GeMoMa.Score=ReAlign AnnotationFinalizer.r=NO o=true t=<target_genome> i=<reference_1_id> a=<reference_1_annotation> g=<reference_1_genome>

there are several parameters that need to be set indicated with <foo>. You can specify

  • the number of threads
  • the output directory
  • the target genome
  • and the reference ID (optional), annotation and genome. If you have several references just repeat s=own and the parameter tags i, a, g with the corresponding values.

In addition, we recommend to set several parameters:

  • GeMoMa.Score=ReAlign: states that the score from mmseqs should be recomputed as mmseqs uses an approximation
  • AnnotationFinalizer.r=NO: do not rename genes and transcripts
  • o=true: output individual predictions for each reference as a separate file allowing to rerun the combination step (GAF) very easily and quickly

If you like to specify the maximum intron length please consider the parameters GeMoMa.m and GeMoMa.sil. If you have RNA-seq data either from own experiments or publicly available data sets (cf. NCBI SRA, EMBL-EBI ENA), we recommend to use them. You need to map the data against the target genome with your favorite read mapper. In addition, we recommend to check the parameters of the section DenoiseIntrons.


The complete documentation describing all GeMoMa modules and all parameters can be accessed at GeMoMa-Docs.

You can also download a small manual for GeMoMa which explains the main steps for the analysis.

GFF attributes

Using GeMoMa, you'll obtain GFFs containing some special attributes. We briefly explain the most prominent attributes in the following table.

Attribute Long name Tool Necessary parameter Feature Description
aa amino acids GeMoMa mRNA the number of amino acids in the protein
score GeMoMa score GeMoMa mRNA score computed by GeMoMa using the substitution matrix, gap costs and additional penalties
nps number of premature stops GeMoMa mRNA the number of premature stop codons in the prediction
ce coding exons GeMoMa assignment mRNA the number of coding exons of the prediction
rce reference coding exons GeMoMa assignment mRNA the number of coding exons of the reference transcript
minCov minimal coverage GeMoMa coverage, ... mRNA minimal coverage of any base of the prediction given RNA-seq evidence
avgCov average coverage GeMoMa coverage, ... mRNA average coverage of all bases of the prediction given RNA-seq evidence
tpc transcript percentage coverage GeMoMa coverage, ... mRNA percentage of covered bases per predicted transcript given RNA-seq evidence
tae transcript acceptor evidence GeMoMa introns mRNA percentage of predicted acceptor sites per predicted transcript with RNA-seq evidence
tde transcript donor evidence GeMoMa introns mRNA percentage of predicted donor sites per predicted transcript with RNA-seq evidence
tie transcript intron evidence GeMoMa introns mRNA percentage of predicted introns per predicted transcript with RNA-seq evidence
minSplitReads minimal split reads GeMoMa introns mRNA minimal number of split reads for any of the predicted introns per predicted transcript
iAA identical amino acid GeMoMa query proteins mRNA percentage of identical amino acids between reference transcript and prediction
pAA positive amino acid GeMoMa query proteins mRNA percentage of aligned positions between reference transcript and prediction yielding a positive score in the substitution matrix
evidence GAF mRNA number of reference organisms that have a transcript yielding this prediction
alternative GAF mRNA alternative gene ID(s) leading to the same prediction
sumWeight GAF mRNA the sum of the weights of the references that perfectly support this prediction
maxTie maximal tie GAF gene maximal tie of all transcripts of this gene
maxEvidence maximal evidence GAF gene maximal evidence of all transcripts of this gene

The name of the feature describing a transcript prediction can be altered using the parameter "tag". Before version 1.7 the default value of tag was "prediction" instead of "mRNA".

Frequently asked questions

Why does the Extractor not return a single CDS-part, protein, ...?
Please check whether the names of your contigs/chromosomes in your annotation (gff) and genome file (fasta) are identical. The fasta comments should at best only contain the contig/chromosome name. (Since GeMoMa 1.4, comments, which contain the contig/chromosome name and some additional information separated by a space, are also fine.) In addition, check the statistics that are given by the Extractor. It lists how many genes have been read and how many genes have been removed for different reasons. One common problem is that some annotation files do not include the stop codon in the CDS annotation.
How can I force GeMoMa to make more predictions?
There are several parameters affecting the number of predictions. The most prominent are the number of predictions (p) and the contig threshold (ct). For each reference transcript/CDS, GeMoMa initially makes a preliminary prediction and uses this prediction to determine whether a contig is promising and should be used to determine the final predictions. You may decrease ct and increase p to have more contigs in the final prediction. Increasing the number of predictions allows GeMoMa to output more predictions that have been computed. Decreasing the contig threshold allows to increase the number of predictions that are (internally) computed. Increasing p to a very large number without decreasing ct does not help.
Running GeMoMa on a single contig of my assembly yield thousands of weird predictions. What went wrong?
By default, GeMoMa is not build to be run on a single contig. GeMoMa tries to make predictions for all given reference CDS in the given target sequence(s). If the given target sequence is only a fraction of the complete target genome/assembly, GeMoMa will produce weird predictions as it does not filter for the quality of the predictions internally. There are two options to handle this:
  • Use a list of gene models that you expect to be located on this contig (cf. parameter "selected").
  • Filter the predictions using GAF (cf. java -jar GeMoMa-<version>.jar CLI GAF).
Is it mandatory to use RNA-seq data?
No, GeMoMa is able to make predictions with and without RNA-seq evidence.
Is it possible to use multiple reference organisms?
It is possible to use multiple reference organisms for GeMoMa. Just run GeMoMa on each reference organism separately. Finally, you can employ GAF (cf. java -jar GeMoMa-<version>.jar CLI GAF) to combine these annotations.
Why do some reference genes not lead to a prediction in the target genome?
Please first check whether your reference genes have been discarded by the Extractor (cf. assignment file).
If the genes have been discarded, there are two possibilities:
  • The CDS might be redundant, i.e. the coding exons are identical to those of another transcript. In this case, only one CDS is further evaluated.
  • There might be something wrong with your reference genes, e.g., missing start codon, missing stop codon, premature stop codon, ambiguous nucleotides, ... and you should check the options of Extractor or the annotation.
If the reference genes passed the Extractor, there are several possible explanations for this behavior. The two most prominent are:
  • GeMoMa stopped the prediction of a reference genes since it does not return a result within the given time (cf. parameter "timeout").
  • GeMoMa simply did not find a prediction matching the remaining quality criteria.
What does "partial gene model" mean in the context of GeMoMa?
We called a gene model partial if it does not contain an initial start codon and a final stop codon. However, this does not mean that the gene model is located at or close to the border of a chromosome or contig.
For two different reference transcripts, the predictions of GeMoMa overlap or are identical. What should I do with those?
GeMoMa makes the predictions for each reference transcript independently. Hence, it can occur that some of predictions of different reference transcripts overlap or are identical especially in gene families. Typically, you might like to filter or rank these predictions. We have implemented GAF (cf. java -jar GeMoMa-<version>.jar CLI GAF) to do this automatically. However, you can also do it by hand using the GFF attributes. Using RNA-seq data in GeMoMa yields additional fields in the annotation that can be used, e.g., average coverage (avgCov).
A lot of transcripts have been filtered out by the Extractor. What can I do?
There are several reasons for removing transcripts by the Extractor. At least in two cases you can try to get more transcripts by setting specific parameter values. First, if the transcript contains ambiguous nucleotides, please test the parameter "Ambiguity". Second, sometimes we received GFFs which contain wrong phases for CDS entries (e.g., 0 for all CDS entries in the phase column of the GFF). Since version 1.3.2, we provide the option "r" which stands for repair. If r=true is chosen, the Extractor tries to infer all phases for transcripts that show an error and would be filtered out.
Is GeMoMa able to predict pseudo-genes/ncRNA?
No, currently not.
My RNA-seq data indicates there is an additional intron in a transcipt, but GeMoMa does not predict this. Or vice versa, GeMoMa predicts an intron that is not supported by RNA-seq data. What's the reason?
GeMoMa is mainly based on the assumptions of amino acid and intron position conservation between reference and target species. Hence, GeMoMa tries to predict a gene model with similar exon-intron-structure in the target species and does not stick too much with RNA-seq data. Although intron position conservation can be observed in most cases, sometimes new introns evolve or others vanish. For this reasons, GeMoMa also allows for the inclusion or exclusion of introns adding some additional costs (cf. GeMoMa parameter intron-loss-gain-penalty). However, the behaviour of GeMoMa depends on the parameters settings (especially intron-loss-gain-penalty, sm (substitution matrix), go (gap opening), ge (gap extension)) and the length of the missed/additional intron. Nevertheless, such cases can only occur if the additional/missed intron has a length that can be divided by 3 preserving the reading frame. Since the available RNA-seq data only reflects a fraction of tissues/environmental conditions/..., missing RNA-seq evidence does not necessarily mean that the predictions is wrong.
My RNA-seq data indicates two alternative, highly overlapping introns. Interestingly, GeMoMa does not take the intron that is more abundant. Why?
GeMoMa reads the introns from the input file using some filter (cf. GeMoMa parameter r (reads)). All introns that pass the filter are used and treated equally. Hence, GeMoMa uses the intron that matches the expectation of intron position and amino acid conservation compared to the reference transcript.
Does GeMoMa predict multiple transcripts per gene?
GeMoMa in principle allows to predict multiple transcripts per gene, if corresponding transcripts are given in the reference species or if multiple reference species are used.
GeMoMa failed with java.lang.OutOfMemoryError. What can I do?
Whenever you see a java.lang.OutOfMemoryError, you should rerun the program with Java virtual machine (VM) options. More specifically you should set: -Xms the initially used RAM, e.g. to 5Gb (–Xms5G), and -Xmx the maximally used RAM, e.g. to 50Gb (-Xmx50G). GeMoMa often needs more memory if you have a large genome and if you’re providing a large coverage file (extracted from RNA-seq data). If you don’t have a compute node with enough memory, you can run GeMoMa without coverage, which will return the same predictions, but does not include all statistics. Another point could be the protein alignment, if you use the optional parameter "query protein" (below version 1.; or "protein alignment" (since version 1.;. Again you can run GeMoMa without protein alignment, which will return the same predictions, but less statistics.
I need to specify the genetic code for my organisms. What is the expected format?
The genetic code is given in a two column tab-delimited table, where the first column is the one letter code of the amino acid and the second column is a comma-separated list of triplets. As we are working on genomic DNA, GeMoMa expects the bases A, C, G, and T, and not U (as expected in mRNA). Here is the link to the default genetic code, which might be used as template:
https://github.com/Jstacs/Jstacs/blob/master/projects/gemoma/test_data/genetic_code.txt
Alternative genetic codes are described here using the RNA alphabet:
https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi
The genetic code might be specified for a reference organism in the module Extractor or for a target organism in the module GeMoMa.
I like to accelerate GeMoMa. What can I do?
You can use several threads for the computation. If you run the GeMoMaPipeline you just have to select threads=<your_number>.
In addition, you can change the search algorithm that is used in GeMoMa. Tblastn is used by default as search algorithm in GeMoMa (for historical reasons, until version 1.6.4). However, tblastn can be replaced by mmseqs which is typically much faster. If you run the GeMoMaPipeline you just have to select tblastn=false, which is default since version 1.7. However, changing the search algorithm can also effect the results. We try to minimize these effect using specific parameters for the search algorithms.
If you modify other parameters, you will probably receive results that differ to a larger extend from those received using the default parameters.
Is there a way to use GeMoMa to search a single CDS or protein sequence against a genome and return the predicted gene model (CDS fasta, protein fasta, GFF) similar to exonerate?
There at least two ways to do this. If you use GeMoMaPipeline you can
(A) Use the parameter “selected” to select specific gene models (=transcripts/proteins) from the annotation instead of using all or
(B) Use s=pre-extracted, use a fasta file with the proteins for the parameter cds-parts and leave assignment unset.
Using one of these options you can either look for a single or few transcripts/proteins either with (A) or without (B) intron-position conservation. In addition, you can use RNA-seq data to improve the predictions, which should be not possible with exonerate.
Can I determine synteny based on GeMoMa predictions?
Yes, since version 1.7 we provide the module SyntenyChecker and a R script that can be used for this purpose. It exploits the fact the the reference gene and the alternative are known. Hence no alignment is need at this point and synteny can be determined quite fast.
How, can I add additional attributes to the annotation?
Additional attribute, e.g., functional annotation from InterProScan, can be added to the structural gene annotation using the module AddAttribute, which has been included since version 1.7. Such additional attributes might be used in GAF for filtering and sorting and can also be displayed in genome browsers like IGV or WebApollo.
Can structural gene annotation provided by GeMoMa be submitted to NCBI?
Yes, NCBI allows to submit structural gene annotation in GFF format (https://www.ncbi.nlm.nih.gov/genbank/genomes_gff/). If you run GeMoMaPipeline or AnnotationFinalizer, the GFF should be valid for conversion.
Running GeMoMaPipeline throws an exception. Can I restart GeMoMaPipeline using intermediate results?
Yes, since version 1.7 we have a new parameter in GeMoMaPipeline called restart.
If you want to restart the last broken GeMoMaPipeline run, you have to execute GeMoMaPipeline with the same command line as before and add restart=true.
If necessary, you can also slightly change the other parameters. However, if the parameters differ too much from those used before, GeMoMaPipeline will decide to perform a new independent run.
A restart of GeMoMaPipeline is particularly useful if the time-consuming search (tblastn or mmseqs) was successful, since this can save runtime.

If you have any further questions, comments or bugs, please check the GeMoMa-Docs, our github page or contact Jens Keilwagen.

References

If you use GeMoMa, please cite

J. Keilwagen, M. Wenk, J. L. Erickson, M. H. Schattat, J. Grau, and F. Hartung. Using intron position conservation for homology-based gene prediction. Nucleic Acids Research, 2016. doi: 10.1093/nar/gkw092

J. Keilwagen, F. Hartung, M. Paulini, S. O. Twardziok, and J. Grau Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi. BMC Bioinformatics, 2018. doi: 10.1186/s12859-018-2203-5

Version history

GeMoMa 1.7.1 (07.09.2020)

  • GeMoMa:
    • bugfix if assignment == null
    • bugfix remove toUpperCase
  • GeMoMaPipeline
    • Galaxy integration bugfix for hidden parameter restart
    • hide BLAST_PATH and MMSEQS_PATH from Galaxy integration
    • improved protocol output if threads=1
    • add addtional test to GeMoMaPipeline

GeMoMa 1.7 (29.07.2020)

  • improved manual including new module and runtime
  • check whether input files exist before execution
  • partially checking MIME types in CLI before execution
  • changed homepage from http to https
  • new module AddAttribute: allows to add attributes (like functional annotation from InterProScan) to gene annotation files that might be used in GAF or displayed genome browsers like IGV or WebApollo
  • new module SyntenyChecker: creates a table that can be used to create dot plots between the annotation of the target and reference organism
  • changed default value of parameter "tag" from "prediction" to "mRNA"
  • AnnotationEvidence:
    • additional attributes: avgCov, minCov, nps, ce
    • changed default value of "annotation output" to true
    • bugfix: transcript start and end
  • ERE:
    • changed default value of coverage to "true"
    • new parameter "minimum context": allows to discard introns if all split reads have short aligned contexts
  • Extractor:
    • bugfix splitAA if coding exon is very short
    • improved verbose mode
    • new parameter "upcase IDs"
    • new parameter "introns" allowing to extract introns from the reference (only for test cases)
    • new parameter "discard pre-mature stop" allowing to discard or use transcripts with pre-mature stop
    • improved handling of corrupt annotations
  • GAF:
    • bugfix missing transcripts
    • slightly changed the default value of "filter"
  • GeMoMa:
    • replaced parameter "query proteins" by "protein alignment"
    • using splitAA for scoring predictions
    • new gff attributes:
      • ce and rce for the feature prediction indicating the number of coding exons for the prediction and the reference, respectively
      • nps for the number of premature stop codons (if avoid stop is false)
    • slightly changed the meaning of the parameter "avoid stop"
  • GeMoMaPipeline:
    • changed the default value of tblastn to false, hence mmseqs is used as search algorithm
    • changed the default value of score to ReAlign
    • remove "--dont-split-seq-by-len" from mmseqs createdb
    • new optional parameter BLAST_PATH
    • new optional parameter MMSEQS_PATH
    • new option to allow for incorporation of external annotation, e.g., from ab-initio gene prediction
    • new parameter restart allowing to restart the latest GeMoMaPipeline run, which was finished without results, with very similar parameters, e.g., after an exception was thrown (cf. parameter debug)

GeMoMa 1.6.4 (24.04.2020)

  • improved help section
  • change gff attribute "AA" to "aa"
  • GAF:
    • bugfix overlapping genes
    • accelerated computation
  • GeMoMa:
    • bugfix: if no assignment file is used and protein ID are prefixes of other protein IDs
    • change GFF attribute AA to aa
  • AnnotationFinalizer: new parameter "name attribute" allowing to decide whether a name attribute or the Parent and ID attributes should be used for renaming

GeMoMa 1.6.3 (05.03.2020)

  • Jstacs changes:
    • CLI: bugfix ExpandableParameterSet
  • python wrapper (for *conda)
  • updated tests.sh, run.sh, pipeline.sh
  • rename Denoise to DenoiseIntrons
  • AnnotationEvidence: write phase (as given) to gff
  • GAF: new parameter: default attributes allows to set attributes that are not included in some gene annotation files
  • GeMoMa: new parameter: static intron length allowing to use dynamic intron length if set to false
  • GeMoMaPipeline:
    • bugfix: time-out
    • improve output
    • separate parameters for maximum intron length (DenoiseIntrons, GeMoMa)

GeMoMa 1.6.2 (17.12.2019)

  • Jstacs changes:
    • test methods for modules
    • live protocol for Galaxy
  • new module Denoise: allowing to clean introns extracted by ERE
  • new module NCBIReferenceRetriever: allowing to retrieve data for reference organisms easily from NCBI.
  • GAF:
    • bugfix for filter using specific attributes if no RNA-seq or query proteins was used
    • allow to add annotation info (as for instance provided by Phytozome) based on the reference organisms
  • GeMoMa: bugfix for timeout
  • GeMoMaPipeline:
    • bugfix reporting predicted partial proteins
    • improved protocol
    • new default value for query proteins (changed from false to true)
    • new default value for Ambiguity (changed from EXCEPTION to AMBIGUOUS)

GeMoMa 1.6.1 (4.06.2019)

  • createGalaxyIntegration.sh: bugfix for GeMoMaPipeline
  • new module CheckIntrons: allowing to create statistics for introns (extracted by ERE)
  • AnnotationFinalizer: bugfix for sequence IDs with large numbers
  • CompareTranscripts:
    • bugfix for prefix of ref-gene
    • allow no transcript info, but making assignment non-optional if a transcript info is set
  • GAF: bugfix for Galaxy integration
  • GeMoMaPipeline:
    • improved output in case of Exceptions
    • new parameter "output individual predictions" allows to in- or exclude individual predictions from each reference organism in the final result
    • new parameter "weight" allows weights for reference species (cf. GAF)
  • ERE: new parameter "minimum mapping quality"

GeMoMa 1.6 (2.04.2019)

  • allow to use mmseqs as alternative to tblastn
  • AnnotationEvidence:
    • allows to add attributes to the input gff: tie, tpc, AA, start, stop
    • new parameter for gff output
  • AnnotationFinalizer: new tool for predicting UTRs and renaming predictions
  • GAF:
    • relative score filter and evidence filter are replaced by a flexible filter that allows to filter by relative score, evidence or other GFF attributes as well as combinations thereof
    • sorting criteria of the predictions within clusters can now be user-specified
    • new attribute for genes: combinedEvidence
    • new attribute for predictions: sumWeight
    • allows to use gene predictions from all sources, including for instance ab-initio gene predictors, purely RNA-seq based gene prediction and manually curation
    • bugfix for predictions from multiple reference organisms
    • improved statistic output
  • GeMoMa
    • renamed the parameter tblastn results to search results
    • new parameter for sorting the results of the similarity search (tblastn or mmseqs), if you use mmseqs for the similarity search you have use sort
    • new parameter for score of the search results: three options: Trust (as is), ReScore (use aligned sequence, but recompute score), and ReAlign (use detected sequence for realignment and rescore)
    • bugfix: threshold for introns from multiple files

GeMoMa 1.5.3 (23.07.2018)

  • improved parameter description and presentation
  • GeMoMaPipeline:
    • removed unnecessary parameters
  • GeMoMa:
    • bugfix: reading coverage file
    • removed parameter genomic (cf. Extractor)
    • removed protein output (cf. Extractor)
  • GAF:
    • bugfix: prefix
  • Extractor:
    • new parameter genomic

GeMoMa 1.5.2 (31.5.2018)

  • GAF:
    • new parameter that allows to restrict the maximal number of transcript predictions per gene
    • altered behavior of the evidence filter from percentages to absolute values
    • bugfix: nested genes
    • checking for duplicates in prediction IDs
  • GeMoMa:
    • warning if RNA-seq data does not match with target genome
  • GeMoMaPipeline: new tool for running the complete GeMoMa pipeline at once allowing multi-threading
  • folder for temporary files of GeMoMa

GeMoMa 1.5 (13.02.2018)

  • AnnotationEvidence: add chromosome to output
  • CompareTranscripts: new parameter that allows to remove prefixes introduces by GAF
  • Extractor: new parameter "stop-codon excluded from CDS" that might be used if the annotation does not contain the stop codons
  • ExtractRNASeqEvidence:
    • print intron length stats
    • include program infos in introns.gff3
  • GeMoMa:
    • new attribute pAA in gff output if query protein is given
    • include program infos in predicted_annotation.gff3
    • minor bugfix
  • GAF:
    • new parameter that allows to specify a prefix for each input gff
    • collect and print program infos to filtered_prediction.gff3
    • improved statistics output

GeMoMa 1.4.2 (21.07.2017)

  • automatic searching for available updates
  • AnnotationEvidence: bugfix (tie computation: Arrays.binarysearch does not find first match)
  • Extractor: bugfix (files that are not zipped)
  • GeMoMa: bugfix (tie computation: Arrays.binarysearch does not find first match)

GeMoMa 1.4.1 (30.05.2017)

  • CompareTranscripts: bugfix (NullPointerException)
  • Extractor: reference genome can be .*fa.gz and .*fasta.gz
  • GeMoMa: bugfix (shutdown problem after timeout)
  • modified additional scripts and documentation

GeMoMa 1.4 (03.05.2017)

  • AnnotationEvidence: new tool computing tie and tpc for given annotation (gff)
  • CompareTranscripts: new tool comparing predicted and given annotation (gff)
  • Extractor:
    • reading CDS with no parent tag (cf. discontinuous feature)
    • automatic recognition of GFF or GTF annotation
    • Warning if sequences mentioned in the annotation are not included in the reference sequence
  • GeMoMa:
    • allowing for multiple intron and coverage files (= using different library types at the same time)
    • NA instead of "?" for tae, tde, tie, minSplitReads of single coding exon genes
    • new default values for the parameters: predictions (10 instead of 1) and contig threshold (0.4 instead of 0.9)
    • bugfix (write pc and minCov if possible for last CDS part in predicted annotation)
    • bugfix (ref-gene name if no assignment is used)
    • bugfix (minSplitReads, minCov, tpc, avgCov if no coverage available)
  • GAF:
    • nested genes on the same strand
    • bugfix (if nothing passes the filter)

GeMoMa 1.3.2 (18.01.2017)

  • Extractor: new parameter repair for broken transcript annotations
  • GeMoMa: bugfixes (splice site computation)

GeMoMa 1.3.1 (09.12.2016)

  • GeMoMa bugfix (finding start/stop codon for very small exons)

GeMoMa 1.3 (06.12.2016)

  • ERE: new tool for extracting RNA-seq evidence
  • Extractor: offers options for
    • partial gene models
    • ambiguities
  • GeMoMa:
    • RNA-seq
      • defining splice sites
      • additional feature in GFF and output
        • transcript intron evidence (tie)
        • transcript acceptor evidence (tae)
        • transcript donor evidence (tde)
        • transcript percentage coverage (tpc)
        • ...
    • improved GFF
    • simplified the command line parameters
    • IMPORTANT: parameter names changed for some parameters
  • GAF: new tool for filtering and combining different predictions (especially of different reference organisms)

GeMoMa 1.1.3 (06.06.2016)

  • minor modifications to the Extractor tool

GeMoMa 1.1.2 (05.02.2016)

  • GeMoMa bugfix (upstream, downstream sequence for splice site detection)
  • Extractor: new parameter s for selecting transcripts
  • improved Galaxy integration

GeMoMa 1.1.1 (01.02.2016)

  • initial release for paper