GBRS is a suite of tools for reconstructing individualized diploid genomes using RNA-Seq data from multiparent population and quantifying allele specific expression. Although we tested it with mouse models only, GBRS should work for any multiparent populations. If you are interested in using it for other multiparent population model, please contact Kwangbom “KB” Choi at The Jackson Laboratory. For the Diversity Outbred (DO), Collaborative Cross (CC) mice, or F1 hybrids of CC’s (CCRIX), required data files are available at ftp://churchill-lab.jax.org/software/GBRS/.
First of all, please pull and run the image.
$ docker pull kbchoi/gbrs
$ docker run -i -t kbchoi/gbrs
(base) root@a6b69ed57991:/#The environment variable, $GBRS_DATA is set and all the required files are available there already.
(base) root@a6b69ed57991:/# echo $GBRS_DATA
/usr/share/gbrs/R84-REL1505
(base) root@a6b69ed57991:/# ls -lh $GBRS_DATA
total 3.0G
-rw-r--r-- 1 root root 6.0M May 16 2016 avecs.npz
-rw-r--r-- 1 root root 80 May 16 2016 founder.hexcolor.info
-rw-r--r-- 1 root root 23M May 16 2016 gbrs.hybridized.targets.info
-rw-r--r-- 1 root root 627 May 16 2016 ref.fa.fai
-rw-r--r-- 1 root root 3.0M May 16 2016 ref.gene2transcripts.tsv
-rw-r--r-- 1 root root 155K May 16 2016 ref.gene_ids.ordered.npz
-rw-r--r-- 1 root root 380K May 16 2016 ref.gene_pos.ordered.npz
-rw-r--r-- 1 root root 1.5M Jul 12 2016 ref.genome_grid.64k.noYnoMT.txt
-rw-r--r-- 1 root root 1.6M May 16 2016 ref.genome_grid.64k.txt
-rw-r--r-- 1 root root 1.9M Mar 18 2019 ref.genome_grid.69k.noYnoMT.txt
-rw-r--r-- 1 root root 2.0M Mar 18 2019 ref.genome_grid.69k.txt
-rw-r--r-- 1 root root 2.6M May 16 2016 ref.transcripts.info
-rw-r--r-- 1 root root 16M May 16 2016 tranprob.DO.G0.F.npz
-rw-r--r-- 1 root root 16M May 16 2016 tranprob.DO.G0.M.npz
...
-rw-r--r-- 1 5156 10000 462M May 11 2016 transcripts.1.ebwt
-rw-r--r-- 1 5156 10000 187M May 11 2016 transcripts.2.ebwt
-rw-r--r-- 1 5156 10000 8.0M May 11 2016 transcripts.3.ebwt
-rw-r--r-- 1 5156 10000 373M May 11 2016 transcripts.4.ebwt
-rw-r--r-- 1 5156 10000 462M May 11 2016 transcripts.rev.1.ebwt
-rw-r--r-- 1 5156 10000 187M May 11 2016 transcripts.rev.2.ebwtGBRS is installed in its own conda virtual environment. First of all, you have to “activate” the virtual environment by doing the following:
(base) root@a6b69ed57991:/# conda activate gbrsThe first step is to align our RNA-Seq reads against pooled transcriptome of all founder strains:
(gbrs) root@a6b69ed57991:/# bowtie -q -a --best --strata --sam -v 3 ${GBRS_DATA}/transcripts ${FASTQ} \
| samtools view -bS - > ${BAM_FILE}
Before quantifying multiway allele specificity, bam file should be converted into emase format:
(gbrs) root@a6b69ed57991:/# gbrs bam2emase -i ${BAM_FILE} \
-m ${GBRS_DATA}/ref.transcripts.info \
-s A,B,C,D,E,F,G,H \
-o ${EMASE_FILE}
We can compress EMASE format alignment incidence matrix:
(gbrs) root@a6b69ed57991:/# gbrs compress -i ${EMASE_FILE} \
-o ${COMPRESSED_EMASE_FILE}
If storage space is tight, you may want to delete ${BAM_FILE} or ${EMASE_FILE} at this point since ${COMPRESSED_EMASE_FILE} has all the information the following steps would need. If you want to merge emase format files in order to, for example, pool technical replicates, you run ‘compress’ once more listing files you want to merge with commas:
(gbrs) root@a6b69ed57991:/# gbrs compress -i ${COMPRESSED_EMASE_FILE1},${COMPRESSED_EMASE_FILE2},... \
-o ${MERGED_COMPRESSED_EMASE_FILE}
and use ${MERGED_COMPRESSED_EMASE_FILE} in the following steps. Now we are ready to quantify multiway allele specificity:
(gbrs) root@a6b69ed57991:/# gbrs quantify -i ${COMPRESSED_EMASE_FILE} \
-g ${GBRS_DATA}/ref.gene2transcripts.tsv \
-L ${GBRS_DATA}/gbrs.hybridized.targets.info \
-M 4 --report-alignment-counts
Then, we reconstruct the genome based upon gene-level TPM quantities (assuming the sample is a female from the 20th generation Diversity Outbred mice population)
(gbrs) root@a6b69ed57991:/# gbrs reconstruct -e gbrs.quantified.multiway.genes.tpm \
-t ${GBRS_DATA}/tranprob.DO.G20.F.npz \
-x ${GBRS_DATA}/avecs.npz \
-g ${GBRS_DATA}/ref.gene_pos.ordered.npz
We can now quantify allele-specific expressions on diploid transcriptome:
(gbrs) root@a6b69ed57991:/# gbrs quantify -i ${COMPRESSED_EMASE_FILE} \
-G gbrs.reconstructed.genotypes.tsv \
-g ${GBRS_DATA}/ref.gene2transcripts.tsv \
-L ${GBRS_DATA}/gbrs.hybridized.targets.info \
-M 4 --report-alignment-counts
Genotype probabilities are on a grid of genes. For eQTL mapping or plotting genome reconstruction, we may want to interpolate probability on a decently-spaced grid of the reference genome.:
(gbrs) root@a6b69ed57991:/# gbrs interpolate -i gbrs.reconstructed.genoprobs.npz \
-g ${GBRS_DATA}/ref.genome_grid.69k.noYnoMT.txt \
-p ${GBRS_DATA}/ref.gene_pos.ordered.npz \
-o gbrs.interpolated.genoprobs.npz
To plot a reconstructed genome:
(gbrs) root@a6b69ed57991:/# gbrs plot -i gbrs.interpolated.genoprobs.npz \
-o gbrs.plotted.genome.pdf \
-n ${SAMPLE_ID}
Content type
Image
Digest
Size
2.8 GB
Last updated
over 6 years ago
docker pull kbchoi/gbrs