TSEBRA: Transcript Selector for BRAKER

Introduction

TSEBRA is a combiner tool that selects transcripts from gene predictions based on the support by extrisic evidence in form of introns and start/stop codons. It was developed to combine BRAKER1¹ and BRAKER2² predicitons to increase their accuracies.

Prerequisites

Python 3.5.2 or higher is required.

Installation

Download TSEBRA:

git clone https://github.com/Gaius-Augustus/TSEBRA

Usage

The main script is ./bin/tsebra.py. For usage information run ./bin/tsebra.py --help.

Input Files

TSEBRA takes a list of gene prediciton files, a list of hintfiles and a configuration file as mandatory input.

Gene Predictions

The gene prediction files have to be in gtf format. This is the standard output format of a BRAKER or AUGUSTUS^3,4 gene prediciton.

Example:

2L      AUGUSTUS        gene    83268   87026   0.88    -       .       g5332
2L      AUGUSTUS        transcript      83268   87026   0.88    -       .       g5332.t1
2L      AUGUSTUS        intron  84278   87019   1       -       .       transcript_id "file_1_file_1_g5332.t1"; gene_id "file_1_file_1_g5332";
2L      AUGUSTUS        CDS     87020   87026   0.88    -       0       transcript_id "file_1_file_1_g5332.t1"; gene_id "file_1_file_1_g5332";
2L      AUGUSTUS        exon    87020   87026   .       -       .       transcript_id "file_1_file_1_g5332.t1"; gene_id "file_1_file_1_g5332";

Hint Files

The hints files have to be in gff format, the last column must include an attribute for the source for the hint with 'src=' and can include the number of hints supporting the gene structure segment with 'mult='. This is the standard file format of the hintsfile.gff in a BRAKER working directory.

Example:

2L      ProtHint        intron  279806  279869  2       +       .       src=P;mult=25;pri=4;al_score=0.437399;
2L      ProtHint        intron  275252  275318  2       -       .       src=P;mult=19;pri=4;al_score=0.430006;
2L      ProtHint        stop    293000  293002  1       +       0       grp=7220_0:002b08_g42;src=C;pri=4;
2L      ProtHint        intron  207632  207710  1       +       .       grp=7220_0:002afa_g26;src=C;pri=4;
2L      ProtHint        start   207512  207514  1       +       0       grp=7220_0:002afa_g26;src=C;pri=4;

Configuration File

The configuration file has to include three different sets of parameter:

1. Weights for all sources of hints. The source of a hint is specified by the mandatory 'src=' attribute in the last column of the hintsfile.gff (see section 'Hint Files'). See section 'Transcript scores' in TSEBRA for more information on how these weigths are used.
   A weight is set to 1, if the weight for a hint source is not specified in the configuration file.

   • Notes on adjusting these parameters: Increase the weight of the hint sources that have the highest quality. For example, if the protein database includes only species that are remotely related to the target species, the hints produced by BRAKER2 might be less accurate than the RNA-seq evidence. Then, you should increase the weight of the source related to the RNA-seq hints.

2. Required fractions of supported introns or supported start/stop-codons for a transcript. A transcript is not included in the TSEBRA result if the fractions of introns and start/stop codons supported by extrinsic evidence are lower than the thresholds.

   • Notes on adjusting these parameters: The low evidence support thresholds for low evidence support are quite strict in the default configuration file. In this configuration, only transcripts with very high evidence support are allowed in the TSBERA result. In some cases, the default setting might be too strict, so that too many transcripts are filtered out. In this case, you should reduce the threshold of 'intron_support' (e.g., to 0.2).

3. Allowed difference between two overlapping transcripts for the four transcript scores (see section 'Transcript scores' in TSEBRA for a definition of the different transcript scores and section 'Pairwise transcript comparison rule' for more information on how transcripts are compared). TSEBRA compares transcripts via their transcript scores and removes the one with the lower score if their difference exceeds the respective threshold.
   Note that e_1, e_2 are fractions (e_1, e_2 ∊ [0,1]), and e_3, e_4 can be larger than 1 (e_3, e_4 ≥ 0).

   • Notes on adjusting these parameters: The higher the thresholds are set the less transcripts are filtered by the respective rule. With these thresholds one can adjust the effect of each filtering rule of TSEBRA. As these thresholds are increased, more transcripts are included in the TSEBRA result, in particular, more alternatively spliced isoforms per gene are contained in the result.

The name and the value of a parameter are separated by a space, and each parameter is listed in a different line.
Example:

# src weights
P 0.1
E 10
C 5
M 1
# Low evidence support
intron_support 0.75
stasto_support 1
# Feature differences
e_1 0
e_2 0.5
e_3 25
e_4 10

Use Case

The recommended and most common usage for TSEBRA is to combine the resulting braker.gtf files of a BRAKER1 and a BRAKER2 run using the hintsfile.gff from both working directories. However, TSEBRA can be applied to any number (>1) of gene predictions and hint files as long as they are in the correct format.

A common case might be that a user wants to annotate a novel genome with BRAKER and has:

• a novel genome with repeats masked: genome.fasta.masked,
• hints for intron positions from RNA-seq readsrna_seq_hints.gff,
• database of homologous proteins: proteins.fa.

1. Run BRAKER1 and BRAKER2 for example with

### BRAKER1
braker.pl --genome=genome.fasta.masked --hints=rna_seq_hints.gff \
            --softmasking --species=species_name --workingdir=braker1_out
### BRAKER2
braker.pl --genome=genome.fasta.masked --prot_seq=proteins.fa \
    --softmasking --species=species_name --epmode --prg=ph \
    --workingdir=braker2_out

2. Make sure that the gene and transcript IDs of the gene prediction files are in order (this step is optional)

./bin/fix_gtf_ids.py --gtf braker1_out/braker.gtf --out braker1_fixed.gtf
./bin/fix_gtf_ids.py --gtf braker2_out/braker.gtf --out braker2_fixed.gtf

3. Combine predicitons with TSEBRA

./bin/tsebra.py -g braker1_fixed.gtf,braker2_fixed.gtf -c default.cfg \
    -e braker1_out/hintsfile.gff,braker2_out/hintsfile.gff \
    -o braker1+2_combined.gtf

The combined gene prediciton is braker1+2_combined.gtf.

Example

A small example is located at example/. Run ./example/run_prevco_example.sh to execute the example and to check if TSEBRA runs properly.

Renaming the transcripts from the TSEBRA output

The IDs of the transcripts and genes in the TSEBRA output can be renamed such that the gene and transcript ID match. Genes and transcript are numbered consecutively and for example, the second transcript of gene "g12" has the ID "g12.t2". If a prefix is set then it will be added before all IDs, for example, the transcript ID is "dmel_g12.t2" if the prefix is set to "dmel". Additionally, a translation can be produced that provides the mapping from old to new transcript IDs.

Example for renaming tsebra_result.gtf:

./bin/rename_gtf.py --gtf tsebra_result.gtf --prefix dmel --translation_tab translation.tab --out tsebra_result_renamed.gtf

The arguments --prefix and translation_tab are optional.

Licence

All source code, i.e. bin/*.py are under the Artistic License (see https://opensource.org/licenses/Artistic-2.0).

Citing TSEBRA

Preprint: L. Gabriel, K. J. Hoff, T. Bruna, M. Borodovsky, M.Stanke. 2021. TSEBRA: "Transcript Selector for BRAKER." bioRxiv https://doi.org/10.1101/2021.06.07.447316

References

[1] Hoff, Katharina J, Simone Lange, Alexandre Lomsadze, Mark Borodovsky, and Mario Stanke. 2015. “BRAKER1: Unsupervised Rna-Seq-Based Genome Annotation with Genemark-et and Augustus.” Bioinformatics 32 (5). Oxford University Press: 767--69.↑

[2] Tomas Bruna, Katharina J. Hoff, Alexandre Lomsadze, Mario Stanke and Mark Borodvsky. 2021. “BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database." NAR Genomics and Bioinformatics 3(1):lqaa108.↑

[3] Stanke, Mario, Mark Diekhans, Robert Baertsch, and David Haussler. 2008. “Using Native and Syntenically Mapped cDNA Alignments to Improve de Novo Gene Finding.” Bioinformatics 24 (5). Oxford University Press: 637--44.↑

[4] Stanke, Mario, Oliver Schöffmann, Burkhard Morgenstern, and Stephan Waack. 2006. “Gene Prediction in Eukaryotes with a Generalized Hidden Markov Model That Uses Hints from External Sources.” BMC Bioinformatics 7 (1). BioMed Central: 62.↑