Skip to content

Gene prediction using BRAKER3

BRAKER3 is a pipeline that combines GeneMark-ET and AUGUSTUS to predict genes in eukaryotic genomes. This pipeline is particularly useful for annotating newly sequenced genomes. The flexibility of BRAKER3 allows users to provide various input datasets for improving gene prediction accuracy. In this example, we will use various scenarios to predict genes in a Maize genome using BRAKER3. Following are the scenarios we will cover:

Input Type Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8
Genome ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
RNA-Seq ❌ ✔️^* ✔️ ❌ ✔️ ❌ ❌ ✔️
Iso-Seq ❌ ❌ ❌ ❌ ❌ ❌ ✔️ ✔️
Conserved proteins ❌ ❌ ❌ ✔️ ✔️ ❌ ✔️ ✔️
Pretrained species model ❌ ❌ ❌ ❌ ❌ ✔️ ❌ ❌

minimal RNA-Seq data (one library/one tissue)

Installation

We will use the apptainer tool to build a Singularity container for BRAKER3. The Singularity container will contain all the necessary dependencies and tools required to run BRAKER3. To build the Singularity container, run the following command:

1
apptainer build --fakeroot braker3.sif docker://teambraker/braker3:latest

This will create a Singularity container named braker3.sif with BRAKER3 installed.

Setting up BRAKER3

Before running BRAKER3, we need to set up:

  1. GeneMark-ES/ET/EP/ETP license key
  2. The AUGUSTUS_CONFIG_PATH configuration path

The license key for GeneMark-ES/ET/EP/ETP can be obtained from the GeneMark website. Once downloaded, you need to place it in your home directory:

1
2
tar xf gm_key_64.gz
cp gm_key_64 ~/.gm_key

For the AUGUSTUS_CONFIG_PATH, we need to copy the config directory from the Singularity container to the scratch directory. This is required because BRAKER3 needs to write to the config directory, and the Singularity container is read-only. To copy the config directory, run the following command:

1
apptainer exec braker3.sif cp -r /opt/Augustus/config ${RCAC_SCRATCH}/braker/augustus_config

Running BRAKER3

The paths to the following variables need to be set:

1
2
3
4
5
BRAKER_SIF="${RCAC_SCRATCH}/braker/braker3.sif"
AUGUSTUS_CONFIG_PATH="${RCAC_SCRATCH}/braker/augustus_config"
GENEMARK_PATH="/opt/ETP/bin/gmes"
genome="${RCAC_SCRATCH}/braker/Zm-B73-REFERENCE-NAM-5.0_softmasked.fa"
workdir=${PWD}/$(basename ${genome%.*})_braker

Input datasets

With genome only (no external evidence)

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data None
Protein sequences None
Long-read data None
Pretrained species model None 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
mkdir -p ${workdir}
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --esmode \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c1 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with minimal RNA-Seq data (one library/one tissue)

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data RNAseq (single library)
Protein sequences None
Long-read data None
Pretrained species model None 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
mkdir -p ${workdir}
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --rnaseq_sets_ids=B73_V11_middle_MN01042 \
        --rnaseq_sets_dirs=${RCAC_SCRATCH}/RNAseq/ \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c2 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with exhaustive RNA-Seq data (11 tissues)

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data RNAseq (11 tissues)
Protein sequences None
Long-read data None
Pretrained species model None 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
rna_seq_sets_ids="B73_8DAS_root_MN01011,B73_8DAS_root_MN01012,B73_8DAS_shoot_MN01021,B73_8DAS_shoot_MN01022,B73_16DAP_embryo_MN01101,B73_16DAP_embryo_MN01102,B73_16DAP_endosperm_MN01091,B73_16DAP_endosperm_MN01092,,B73_R1_anther_MN01081,B73_R1_anther_MN01082,B73_R1_anther_MNA1081,B73_V11_base_MN01031,B73_V11_base_MN01032,B73_V11_middle_MN01041,B73_V11_middle_MN01042,B73_V11_middle_MN01043,B73_V11_tip_MN01051,B73_V11_tip_MN01052,B73_V18_ear_MN01071,B73_V18_ear_MN01072,B73_V18_tassel_MN01061,B73_V18_tassel_MN01062"
mkdir -p ${workdir}
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --rnaseq_sets_ids=${rnaseq_sets_ids} \
        --rnaseq_sets_dirs=${RCAC_SCRATCH}/rnaseq/ \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c3 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with conserved protein sequences

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data None
Protein sequences Viridiplantae protein sequences
Long-read data None
Pretrained species model None

Using the orthodb-clades tool, we can download protein sequences for a specific clade. In this scenario, since we are using the Maize genome, we can download the clade specific Viridiplantae.fa OrthoDB v12 protein sets.

1
2
3
4
git clone git@github.com:tomasbruna/orthodb-clades.git
ml biocontainers
ml snakemake
snakemake --cores ${SLURM_CPUS_ON_NODE}

When this is done, you should see a folder named clade with Viridiplantae.fa in the orthodb-clades directory. We will use this as one of the input datasets for BRAKER3. The following command will run BRAKER3 with the input genome and protein sequences:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
protein_sequences="${RCAC_SCRATCH}/braker/orthodb-clades/clade/Viridiplantae.fa"
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --species=Zm_$(date +"%Y%m%d").1a \
        --prot_seq=${proteins} \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c4 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with RNA-Seq and conserved protein sequences

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data RNAseq (11 tissues)
Protein sequences Viridiplantae protein sequences
Long-read data None
Pretrained species model None 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
mkdir -p ${workdir}
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --rnaseq_sets_ids=${rnaseq_sets_ids} \
        --rnaseq_sets_dirs=${RCAC_SCRATCH}/rnaseq/ \
        --prot_seq=${proteins} \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c5 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with pretrained species model ("Maize")

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data None
Protein sequences None
Long-read data None
Pretrained species model "Maize" 
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
mkdir -p ${workdir}
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --skipAllTraining \
        --genome=${genome} \
        --species=maize5 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with Iso-Seq and conserved protein sequences

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data None
Protein sequences Viridiplantae protein sequences
Long-read data Iso-Seq data
Pretrained species model None

The IsoSeq data for maize (B73) was obtained from the publication PMC7028979 and is available in the ENA BioProject PRJEB32007. To proceed, you will need the original files listed in the Submitted files: FTP column of the BioProject page. We will download the data (.bam files) and process them using the isoseq3 tool to demultiplex and map the reads to the B73 reference genome. The primers and adapters required for demultiplexing were sourced from the original publication (Supplementary Table 1).

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
ml purge
ml anaconda
conda activate isoseq
for input in *subreads.bam; do
base=$(basename ${input} |sed 's/.subreads.bam//g')
# convert subreads to ccs
ccs ${input} ${base}.ccs.bam --skip-polish --min-passes 1
# demultiplex
lima ${base}.ccs.bam primer.fasta ${base}_lima.bam --isoseq --dump-clips
done
# move the files to separate directories
for f in B73 Ki11 Ki11xB73 B73xKi11; do
mkdir -p $f;
mv *_${f}_* ./${f}/;
done
cd B73
# merge the files
ls *.bam > input.fofn
ml purge
ml biocontainers
ml bamtools samtools
bamtools merge -list input.fofn -out merged_B73.bam
# convert bam to fastq
samtools fastq --threads ${SLURM_CPUS_ON_NODE} -o merged_B73.fastq merged_B73.bam
ml minimap2
minimap2 \
        -t ${SLURM_CPUS_ON_NODE}\
        -ax splice:hq \
        -uf ${genome} \
        merged_B73.fastq > isoseq_B73.sam
samtools view \
        -bS \
        --threads ${threads} \
        -o isoseq.bam \
        isoseq_b73.sam \
samtools sort \
        --threads ${threads} \
        -o isoseq_sorted.bam \
        isoseq.bam

We will need isoseq_sorted.bam (and merged_B73.fastq) for the case 8 as well. For this case 7, we only need isoseq_sorted.bam. To setup BRAKER3 with the Iso-Seq data and conserved protein sequences:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
isoseq="${RCAC_SCRATCH}/isoseq/isoseq_sorted.bam"
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} braker.pl \
        --AUGUSTUS_CONFIG_PATH=${AUGUSTUS_CONFIG_PATH} \
        --GENEMARK_PATH=${GENEMARK_PATH} \
        --prot_seq=${proteins} \
        --bam=${isoseq} \
        --genome=${genome} \
        --species=Zm_$(date +"%Y%m%d").c7 \
        --workingdir=${workdir} \
        --gff3 \
        --threads ${SLURM_CPUS_ON_NODE}

with Iso-Seq, RNA-Seq and conserved protein sequences

Input Type
Genome B73.v5 (softmasked)
RNA-Seq data RNAseq (11 tissues)
Protein sequences Viridiplantae protein sequences
Long-read data Iso-Seq data
Pretrained species model None

To run this, you need to first run case 3 (full-RNAseq data) [BRAKER-1] and case 5 (conserved proteins data) [BRAKER-2]. You will also need the Iso-Seq BAM file generated in case 7.

The steps are as follows:

  1. Run BRAKER using the spliced alignments of short-read RNA-seq (here case 3 with full-RNAseq data).
  2. Run BRAKER using the conserved proteins data (here case 5 with conserved proteins data).
  3. Run GeneMarkS-T protocol on the Iso-Seq data to predict protein-coding regions in the transcripts:
    • map the long reads to the genome using minimap2 (here case 7 isoseq_sorted.bam)
    • collapse redundant isoforms
    • predict protein-coding regions using GeneMarkS-T
  4. Run the long read version of TSEBRA to combine the three gene sets using all extrinsic evidence

Since we have already run case 3 and case 5, we will proceed with the remaining steps.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
collapse_isoforms_by_sam.py \
        --input merged.fastq \
        --fq \
        -b isoseq_sorted.bam \
        -o isoseq_sorted \
        --dun-merge-5-shorter \
        --cpus ${SLURM_CPUS_ON_NODE}
stringtie2fa.py \
        -g ${genome} \
        -f isoseq_sorted.collapsed.gff \
        -o cupcake.fa

gmst.pl \
        --strand direct \
        cupcake.fa.mrna \
        --output gmst.out \
        --format GFF
git clone https://github.com/Gaius-Augustus/BRAKER
cd BRAKER && \
   git checkout long-reads && \
   cd ..
BRAKER/scripts/gmst2globalCoords.py \
        -t isoseq_sorted.collapsed.gff \
        -p gmst.out \
        -o gmst.global.gtf \
        -g ${genome}

We will need the hintsfile.gff and augustus.hints.gtf files from case 3 and case 5. We will also need the gmst.global.gtf file generated from the GeneMarkS-T protocol. The following command will run TSEBRA to combine the three gene sets:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
ln -s ${RCAC_SCRATCH}/braker/case3/Augustus/augustus.hints.gtf braker1.augustus.hints.gtf
ln -s ${RCAC_SCRATCH}/braker/case5/Augustus/augustus.hints.gtf braker2.augustus.hints.gtf
ln -s ${RCAC_SCRATCH}/braker/case3/hintsfile.gff braker1.hintsfile.gff
ln -s ${RCAC_SCRATCH}/braker/case5/hintsfile.gff braker2.hintsfile.gff
ln -s ${RCAC_SCRATCH}/braker/case7/genemark_st/gmst.global.gtf
git clone https://github.com/Gaius-Augustus/TSEBRA
cd TSEBRA
git checkout long-reads
apptainer exec --bind ${RCAC_SCRATCH} ${BRAKER_SIF} ./TSEBRA/bin/tsebra.py \
        -g braker1.augustus.hints.gtf,braker2.augustus.hints.gtf \
        -e braker1.hintsfile.gff,braker2.hintsfile.gff \
        -l gmst.global.gtf \
        -c ./TSEBRA/config/long_reads.cfg \
        -o tsebra.gtf
ml purge
ml biocontainers
ml cufflinks
gffread tsebra.gtf \
        -g ${genome} \
        -y  tsebra_pep.fa \
        -x  tsebra_cds.fa

Processing output

Comparing and Evaluating

A. BUSCO profiling

Busco results

B. Reference comparison

With Isoforms

Assigned features

Unassigned features

Without Isoforms

Assigned features

Unassigned features

C. Feature assignment

Assigned features

Unassigned features

D. Functional annotation

Pfam Domains

Functional Annotation

Percent identity

E. Phylostrata analysis

Assigned features

Unassigned features

F. GFF3 stats

GFF3 stats

G. OMArk assessment

braker consistency

braker_conservedHOGs

H. CDS assessments

braker_cds-gc

braker_cds-length

braker_gc-content

braker_codon-type

Conclusions

References