GeMoMa

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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.

Schema of GeMoMa algorithm

Paper

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

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.

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

Galaxy

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 only Galaxy instance.

GeMoMa workflow adapted from Galaxy

Running the command line application

For running the command line application, Java v1.8 or later is required.

NCBIReferenceRetriever

We provide the module NCBIReferenceRetriever allowing to retrieve data for reference organisms easily from NCBI. You can run NCBIReferenceRetriever from the command line with
java -jar GeMoMa-<version>.jar CLI NRR [<parameter>=<value> ...]
The parameters comprise:


name comment type
r reference directory (the directory where the genome and annotation files of the reference organisms should be stored, default = references/) STRING
n number of tries (the number of tries for downloading a reference file, valid range = [1, 100], default = 10) INT
rl reference list (a list of reference organisms) FILE
outdir The output directory, defaults to the current working directory (.) STRING

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
java -jar GeMoMa-<version>.jar CLI GeMoMaPipeline [<parameter>=<value> ...]
The parameters comprise:

name comment type
t target genome (Target genome file (FASTA)) FILE
The following parameter(s) can be used multiple times:
s species (data for reference species, range={own, pre-extracted}, default = own)
Parameters for selection "own":
i ID (ID to distinguish the different reference species, default = , OPTIONAL) STRING
a annotation (Reference annotation file (GFF or GTF), which contains gene models annotated in the reference genome) FILE
g genome (Reference genome file (FASTA)) FILE
w 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) DOUBLE
ai annotation info (annotation information of the reference, tab-delimted file containing at least the columns transcriptName, GO and .*defline, OPTIONAL) FILE
Parameters for selection "pre-extracted":
i ID (ID to distinguish the different reference species, default = , OPTIONAL) STRING
c cds parts (The query cds parts file (FASTA), i.e., the cds parts that have been blasted) FILE
a assignment (The assignment file, which combines parts of the CDS to transcripts, OPTIONAL) FILE
q query proteins (optional query protein file (FASTA) for computing the optimal alignment score against complete protein prediction, OPTIONAL) FILE
w 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) DOUBLE
ai annotation info (annotation information of the reference, tab-delimted file containing at least the columns transcriptName, GO and .*defline, OPTIONAL) FILE
selected 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) FILE
gc genetic code (optional user-specified genetic code, OPTIONAL) FILE
tblastn 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) BOOLEAN
tag 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) STRING
r RNA-seq evidence (data for RNA-seq evidence, range={NO, MAPPED, EXTRACTED}, default = NO)
No parameters for selection "NO"
Parameters for selection "MAPPED":
ERE.s 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) STRING
The following parameter(s) can be used multiple times:
ERE.m mapped reads file (BAM/SAM files containing the mapped reads) FILE
ERE.v ValidationStringency (Defines how strict to be when reading a SAM or BAM, beyond bare minimum validation., range={STRICT, LENIENT, SILENT}, default = LENIENT) STRING
ERE.u use secondary alignments (allows to filter flags in the SAM or BAM, default = true) BOOLEAN
ERE.c coverage (allows to output the coverage, default = false) BOOLEAN
ERE.mmq minimum mapping quality (reads with a mapping quality that is lower than this value will be ignored, valid range = [0, 255], default = 40) INT
Parameters for selection "EXTRACTED":
The following parameter(s) can be used multiple times:
introns introns (Introns (GFF), which might be obtained from RNA-seq) FILE
The following parameter(s) can be used zero or multiple times:
coverage coverage (experimental coverage (RNA-seq), range={UNSTRANDED, STRANDED}, default = UNSTRANDED)
Parameters for selection "UNSTRANDED":
coverage_unstranded coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
Parameters for selection "STRANDED":
coverage_forward coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
coverage_reverse coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
d denoise (removing questionable introns that have been extracted by ERE, range={DENOISE, RAW}, default = DENOISE)
Parameters for selection "DENOISE":
DenoiseIntrons.m maximum intron length (The maximum length of an intron, default = 15000) INT
DenoiseIntrons.me minimum expression (The threshold for removing introns, valid range = [0.0, 1.0], default = 0.01) DOUBLE
DenoiseIntrons.c context (The context upstream a donor and donwstream an acceptor site that is used to determine the expression of the region, valid range = [0, 100], default = 10) INT
No parameters for selection "RAW"
Extractor.p proteins (whether the complete proteins sequences should returned as output, default = true) BOOLEAN
Extractor.r 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) BOOLEAN
Extractor.a 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 = AMBIGUOUS) STRING
Extractor.s 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) BOOLEAN
Extractor.f full-length (A flag which allows for choosing between only full-length and all (i.e., full-length and partial) transcripts, default = true) BOOLEAN
GeMoMa.r 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) INT
GeMoMa.s splice (if no intron is given by RNA-seq, compute candidate splice sites or not, default = true) BOOLEAN
GeMoMa.sm substitution matrix (optional user-specified substitution matrix, OPTIONAL) FILE
GeMoMa.g gap opening (The gap opening cost in the alignment, default = 11) INT
GeMoMa.ge gap extension (The gap extension cost in the alignment, default = 1) INT
GeMoMa.m maximum intron length (The maximum length of an intron, default = 15000) INT
GeMoMa.sil static intron length (A flag which allows to switch between static intron length, which can be specified by the user and is identical for all genes, and dynamic intron length, which is based on the gene-specific maximum intron length in the reference organism plus the user given maximum intron length, default = true) BOOLEAN
GeMoMa.i intron-loss-gain-penalty (The penalty used for intron loss and gain, default = 25) INT
GeMoMa.e e-value (The e-value for filtering blast results, default = 100.0) DOUBLE
GeMoMa.c contig threshold (The threshold for evaluating contigs, valid range = [0.0, 1.0], default = 0.4) DOUBLE
GeMoMa.rt region threshold (The threshold for evaluating regions, valid range = [0.0, 1.0], default = 0.9) DOUBLE
GeMoMa.h hit threshold (The threshold for adding additional hits, valid range = [0.0, 1.0], default = 0.9) DOUBLE
GeMoMa.p predictions (The (maximal) number of predictions per transcript, default = 10) INT
GeMoMa.a avoid stop (A flag which allows to avoid stop codons in a transcript (except the last AS), default = true) BOOLEAN
GeMoMa.approx approx (whether an approximation is used to compute the score for intron gain, default = true) BOOLEAN
GeMoMa.prefix prefix (A prefix to be used for naming the predictions, default = ) STRING
GeMoMa.t 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) LONG
GeMoMa.Score Score (A flag which allows to do nothing, re-score or re-align the search results, range={Trust, ReScore, ReAlign}, default = Trust) STRING
GAF.c 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) DOUBLE
GAF.m maximal number of transcripts per gene (the maximal number of allowed transcript predictions per gene, valid range = [1, 2147483647], default = 2147483647) INT
GAF.d default attributes (Comma-separated list of attributes that is set to NaN if they are not given in the annotation file. This allows to use these attributes for sorting or filter criteria. It is especially meaningful if the gene annotation files were received fom different software packages (e.g., GeMoMa, Braker, ...) having different attributes., default = tie,tde,tae,iAA,pAA,score) STRING
GAF.f 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'. In addition, you can check for NaN, e.g., 'isNaN(score)'. 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) STRING
GAF.s sorting (comma-separated list that defines the way predictions are sorted within a cluster, default = evidence,score) STRING
GAF.a alternative transcript filter (If a prediction is suggested as an alternative transcript, this additional filter will be applied. The filter works syntactically like the 'filter' parameter and allows you to keep the number of alternative transcripts small based on meaningful criteria. Reasonable filter could be based on RNA-seq data (tie==1) or on evidence (evidence>1). A more sophisticated filter could be applied combining several criteria: tie==1 or evidence>1, default = tie==1 or evidence>1, OPTIONAL) STRING
AnnotationFinalizer.u UTR (allows to predict UTRs using RNA-seq data, range={NO, YES}, default = NO)
No parameters for selection "NO"
No parameters for selection "YES"
AnnotationFinalizer.r rename (allows to generate generic gene and transcripts names (cf. attribute "Name"), range={COMPOSED, SIMPLE, NO}, default = COMPOSED)
Parameters for selection "COMPOSED":
AnnotationFinalizer.p prefix (the prefix of the generic name) STRING
AnnotationFinalizer.i infix (the infix of the generic name, default = G) STRING
AnnotationFinalizer.s suffix (the suffix of the generic name, default = 0) STRING
AnnotationFinalizer.d digits (the number of informative digits, valid range = [4, 10], default = 5) INT
AnnotationFinalizer.di delete infix (a comma-separated list of infixes that is deleted from the sequence names before building the gene/transcript name, default = ) STRING
Parameters for selection "SIMPLE":
AnnotationFinalizer.p prefix (the prefix of the generic name) STRING
AnnotationFinalizer.d digits (the number of informative digits, valid range = [4, 10], default = 5) INT
No parameters for selection "NO"
p predicted proteins (If *true*, returns the predicted proteins of the target organism as fastA file, default = true) BOOLEAN
pc predicted CDSs (If *true*, returns the predicted CDSs of the target organism as fastA file, default = false) BOOLEAN
pgr predicted genomic regions (If *true*, returns the genomic regions of predicted gene models of the target organism as fastA file, default = false) BOOLEAN
o output individual predictions (If *true*, returns the predictions for each reference species, default = false) BOOLEAN
debug debug (If *false* removes all temporary files even if the jobs exits unexpected, default = true) BOOLEAN
outdir The output directory, defaults to the current working directory (.) STRING
threads The number of threads used for the tool, defaults to 1 INT

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
java -jar GeMoMa-<version>.jar CLI ERE [<parameter>=<value> ...]
The parameters comprise:

name comment type
s 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) STRING
The following parameter(s) can be used multiple times:
m mapped reads file (BAM/SAM files containing the mapped reads) FILE
v ValidationStringency (Defines how strict to be when reading a SAM or BAM, beyond bare minimum validation., range={STRICT, LENIENT, SILENT}, default = LENIENT) STRING
u use secondary alignments (allows to filter flags in the SAM or BAM, default = true) BOOLEAN
c coverage (allows to output the coverage, default = false) BOOLEAN
mmq minimum mapping quality (reads with a mapping quality that is lower than this value will be ignored, valid range = [0, 254], default = 40) INT
outdir The output directory, defaults to the current working directory (.) STRING

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
java -jar GeMoMa-<version>.jar CLI CheckIntrons [<parameter>=<value> ...]
The parameters comprise:

name comment type
t target genome (The target genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code) FILE
The following parameter(s) can be used multiple times:
i introns (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL) FILE
v verbose (A flag which allows to output a wealth of additional information per transcript, default = false) BOOLEAN
outdir The output directory, defaults to the current working directory (.) STRING

DenoiseIntrons

This tool allows to remove potentially incorrectly extracted introns. You can run Denoise from the command line with
java -jar GeMoMa-<version>.jar CLI DenoiseIntrons [<parameter>=<value> ...]
The parameters comprise:

name comment type
The following parameter(s) can be used multiple times:
i introns (Introns (GFF), which might be obtained from RNA-seq) FILE
The following parameter(s) can be used multiple times:
c coverage (experimental coverage (RNA-seq), range={UNSTRANDED, STRANDED}, default = UNSTRANDED)
Parameters for selection "UNSTRANDED":
coverage_unstranded coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
Parameters for selection "STRANDED":
coverage_forward coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
coverage_reverse coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
m maximum intron length (The maximum length of an intron, default = 15000) INT
me minimum expression (The threshold for removing introns, valid range = [0.0, 1.0], default = 0.01) DOUBLE
context context (The context upstream a donor and donwstream an acceptor site that is used to determine the expression of the region, valid range = [0, 100], default = 10) INT
outdir The output directory, defaults to the current working directory (.) STRING

Extractor

For preparing the data, we provide the tool Extractor. You can run Extractor from the command line with
java -jar GeMoMa-<version>.jar CLI Extractor [<parameter>=<value> ...]
The parameters comprise:

name comment type
a annotation (Reference annotation file (GFF or GTF), which contains gene models annotated in the reference genome) FILE
g genome (Reference genome file (FASTA)) FILE
gc genetic code (optional user-specified genetic code, OPTIONAL) FILE
p proteins (whether the complete proteins sequences should returned as output, default = false) BOOLEAN
c cds (whether the complete CDSs should returned as output, default = false) BOOLEAN
genomic genomic (whether the genomic regions should be returned (upper case = coding, lower case = non coding), default = false) BOOLEAN
r 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) BOOLEAN
s 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) FILE
Ambiguity 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) STRING
sefc 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) BOOLEAN
f full-length (A flag which allows for choosing between only full-length and all (i.e., full-length and partial) transcripts, default = true) BOOLEAN
v verbose (A flag which allows to output a wealth of additional information, default = false) BOOLEAN
outdir The output directory, defaults to the current working directory (.) STRING

Gene Model Mapper (GeMoMa)

For predicting gene models, we provide the tool GeMoMa. You can run GeMoMa from the command line with
java -jar GeMoMa-<version>.jar CLI GeMoMa [<parameter>=<value> ...]
The parameters comprise:

name comment type
s search results (The search results, e.g., from tblastn or mmseqs) FILE
t target genome (The target genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code) FILE
c cds parts (The query cds parts file (FASTA), i.e., the cds parts that have been blasted) FILE
a assignment (The assignment file, which combines parts of the CDS to transcripts, OPTIONAL) FILE
q query proteins (optional query protein file (FASTA) for computing the optimal alignment score against complete protein prediction, OPTIONAL) FILE
The following parameter(s) can be used zero or multiple times:
i introns (Introns (GFF), which might be obtained from RNA-seq) FILE
r 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) INT
splice splice (if no intron is given by RNA-seq, compute candidate splice sites or not, default = true) BOOLEAN
The following parameter(s) can be used zero or multiple times:
coverage coverage (experimental coverage (RNA-seq), range={UNSTRANDED, STRANDED}, default = UNSTRANDED)
Parameters for selection "UNSTRANDED":
coverage_unstranded coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
Parameters for selection "STRANDED":
coverage_forward coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
coverage_reverse coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
g genetic code (optional user-specified genetic code, OPTIONAL) FILE
sm substitution matrix (optional user-specified substitution matrix, OPTIONAL) FILE
go gap opening (The gap opening cost in the alignment, default = 11) INT
ge gap extension (The gap extension cost in the alignment, default = 1) INT
m maximum intron length (The maximum length of an intron, default = 15000) INT
sil static intron length (A flag which allows to switch between static intron length, which can be specified by the user and is identical for all genes, and dynamic intron length, which is based on the gene-specific maximum intron length in the reference organism plus the user given maximum intron length, default = true) BOOLEAN
intron-loss-gain-penalty intron-loss-gain-penalty (The penalty used for intron loss and gain, default = 25) INT
e e-value (The e-value for filtering blast results, default = 100.0) DOUBLE
ct contig threshold (The threshold for evaluating contigs, valid range = [0.0, 1.0], default = 0.4) DOUBLE
rt region threshold (The threshold for evaluating regions, valid range = [0.0, 1.0], default = 0.9) DOUBLE
h hit threshold (The threshold for adding additional hits, valid range = [0.0, 1.0], default = 0.9) DOUBLE
p predictions (The (maximal) number of predictions per transcript, default = 10) INT
selected 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) FILE
as avoid stop (A flag which allows to avoid stop codons in a transcript (except the last AS), default = true) BOOLEAN
approx approx (whether an approximation is used to compute the score for intron gain, default = true) BOOLEAN
prefix prefix (A prefix to be used for naming the predictions, default = ) STRING
tag 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) STRING
v verbose (A flag which allows to output a wealth of additional information per transcript, default = false) BOOLEAN
timeout 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) LONG
sort sort (A flag which allows to sort the search results, default = false) BOOLEAN
Score Score (A flag which allows to do nothing, re-score or re-align the search results, range={Trust, ReScore, ReAlign}, default = Trust) STRING
outdir The output directory, defaults to the current working directory (.) STRING

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
java -jar GeMoMa-<version>.jar CLI GAF[<parameter>=<value> ...]
The parameters comprise:

name comment type
t tag (the tag used to read the GeMoMa annotations, default = prediction) STRING
c 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) DOUBLE
m maximal number of transcripts per gene (the maximal number of allowed transcript predictions per gene, valid range = [1, 2147483647], default = 2147483647) INT
The following parameter(s) can be used multiple times:
p prefix (the prefix can be used to distinguish predictions from different input files, OPTIONAL) STRING
w 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) DOUBLE
g gene annotation file (GFF files containing the gene annotations (predicted by GeMoMa)) FILE
a annotation info (annotation information of the reference, tab-delimted file containing at least the columns transcriptName, GO and .*defline, OPTIONAL) FILE
d default attributes (Comma-separated list of attributes that is set to NaN if they are not given in the annotation file. This allows to use these attributes for sorting or filter criteria. It is especially meaningful if the gene annotation files were received fom different software packages (e.g., GeMoMa, Braker, ...) having different attributes., default = tie,tde,tae,iAA,pAA,score) STRING
f 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'. In addition, you can check for NaN, e.g., 'isNaN(score)'. 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) STRING
s sorting (comma-separated list that defines the way predictions are sorted within a cluster, default = evidence,score) STRING
atf alternative transcript filter (If a prediction is suggested as an alternative transcript, this additional filter will be applied. The filter works syntactically like the 'filter' parameter and allows you to keep the number of alternative transcripts small based on meaningful criteria. Reasonable filter could be based on RNA-seq data (tie==1) or on evidence (evidence>1). A more sophisticated filter could be applied combining several criteria: tie==1 or evidence>1, default = tie==1 or evidence>1, OPTIONAL) STRING
outdir The output directory, defaults to the current working directory (.) STRING

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
java -jar GeMoMa-<version>.jar CLI CompareTranscripts [<parameter>=<value> ...]
The parameters comprise:

name comment type
p prediction (The predicted annotation) FILE
a annotation (The true annotation) FILE
The following parameter(s) can be used zero or multiple times:
prefix prefix (the prefix can be used to distinguish predictions from different input files, default = , OPTIONAL) STRING
assignment assignment (the transcript info for the reference of the prediction) FILE
outdir The output directory, defaults to the current working directory (.) STRING

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
java -jar GeMoMa-<version>.jar CLI AnnotationEvidence [<parameter>=<value> ...]
The parameters comprise:

name comment type
a annotation (The genome annotation file (GFF)) FILE
g genome (The genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code) FILE
The following parameter(s) can be used multiple times:
i introns file (Introns (GFF), which might be obtained from RNA-seq, OPTIONAL) FILE
r 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) INT
The following parameter(s) can be used multiple times:
c coverage file (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)
No parameters for selection "NO"
Parameters for selection "UNSTRANDED":
coverage_unstranded coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
Parameters for selection "STRANDED":
coverage_forward coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
coverage_reverse coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
ao annotation output (if the annotation should be returned with attributes tie, tpc, and AA, default = false) BOOLEAN
gc genetic code (optional user-specified genetic code, OPTIONAL) FILE
outdir The output directory, defaults to the current working directory (.) STRING

AnnotationFinializer

This tool allows to predict UTR and to rename predictions. You can run AnnotationEvidence from the command line with
java -jar GeMoMa-<version>.jar CLI AnnotationFinalizer [<parameter>=<value> ...]
The parameters comprise:

name comment type
a annotation (The predicted genome annotation file (GFF)) FILE
t 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) STRING
u UTR (allows to predict UTRs using RNA-seq data, range={NO, YES}, default = NO)
No parameters for selection "NO"
Parameters for selection "YES":
g genome (The genome file (FASTA), i.e., the target sequences in the blast run. Should be in IUPAC code) FILE
The following parameter(s) can be used multiple times:
i introns file (Introns (GFF), which might be obtained from RNA-seq) FILE
r 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) INT
The following parameter(s) can be used multiple times:
c coverage file (experimental coverage (RNA-seq), range={NO, UNSTRANDED, STRANDED}, default = NO)
No parameters for selection "NO"
Parameters for selection "UNSTRANDED":
coverage_unstranded coverage_unstranded (The coverage file contains the unstranded coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
Parameters for selection "STRANDED":
coverage_forward coverage_forward (The coverage file contains the forward coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
coverage_reverse coverage_reverse (The coverage file contains the reverse coverage of the genome per interval. Intervals with coverage 0 (zero) can be left out.) FILE
rename rename (allows to generate generic gene and transcripts names (cf. attribute "Name"), range={COMPOSED, SIMPLE, NO}, default = COMPOSED)
Parameters for selection "COMPOSED":
p prefix (the prefix of the generic name) STRING
infix infix (the infix of the generic name, default = G) STRING
s suffix (the suffix of the generic name, default = 0) STRING
d digits (the number of informative digits, valid range = [4, 10], default = 5) INT
di delete infix (a comma-separated list of infixes that is deleted from the sequence names before building the gene/transcript name, default = ) STRING
Parameters for selection "SIMPLE":
p prefix (the prefix of the generic name) STRING
d digits (the number of informative digits, valid range = [4, 10], default = 5) INT
No parameters for selection "NO"
outdir The output directory, defaults to the current working directory (.) STRING

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.

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
minCov minimal coverage GeMoMa coverage, ... prediction minimal coverage of any base of the prediction given RNA-seq evidence
avgCov average coverage GeMoMa coverage, ... prediction average coverage of all bases of the prediction given RNA-seq evidence
tpc transcript percentage coverage GeMoMa coverage, ... prediction percentage of covered bases per predicted transcript given RNA-seq evidence
tae transcript acceptor evidence GeMoMa introns prediction percentage of predicted acceptor sites per predicted transcript with RNA-seq evidence
tde transcript donor evidence GeMoMa introns prediction percentage of predicted donor sites per predicted transcript with RNA-seq evidence
tie transcript intron evidence GeMoMa introns prediction percentage of predicted introns 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
iAA identical amino acid GeMoMa query proteins prediction percentage of identical amino acids between reference transcript and prediction
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
evidence GAF prediction number of reference organisms that have a transcript yielding this prediction
alternative GAF prediction alternative gene ID(s) leading to the same prediction
sumWeight GAF prediction 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

FAQs

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.
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
  • 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?
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 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.
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 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.
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?
If you like to improve the runtime, 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). However, tblastn can be replaced by mmseqs which is typically much faster. If you run the GeMoMaPipeline you just have to select tblastn=false. 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.

Version history

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
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