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Vg Call, not detecting SV due to soft-clipping
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Entering edit mode
3 months ago
jcmouren • 0

Hello,

I have reads from a genome I modified by adding insertions and deletions (≃100bp). I'm using VG to map those reads to the reference genome (the same genome without my modifications). When calling, deletions are easily detected but most insertions aren't due to clipping.

e.g: Here's an insertion i added to the reference genome : enter image description here

You can see on the image below that all reads have ends massively soft-clipped at the position where my insertion is supposed to be : enter image description here

Is this normal ? How can I do to make VG detect those SV ? Thanks in advance

NB : I'm using VG version v1.31.0 "Caffaraccia", and here's the commands I use :

Graph construction

vg construct -r reference.fa -v samtools_call.vcf.gz -t 8 > graph.vg

Indexing

vg index -x index.xg graph.vg -t 8 ; vg index -g index.gcsa graph.vg -t 8

Mapping

vg map -x index.xg -g index.gcsa -f reads_1.fastq -f reads_2.fastq -t 8 > mapped.gam

Augment

vg augment graph.vg mapped.gam -A aug_mapped.gam -t 8 > aug_graph.vg

Reindexing

vg index aug_graph.vg -x aug_index.xg -t 8

Packing

vg pack -x aug_index.xg -g aug_mapped.gam -Q 5 -o aln_aug.pack -t 8

Calling

vg call aug_index.xg -k aln_aug.pack -t 8 > calls.vcf

And here's the commands I used to make the variant file used to construct the graph :

bwa mem -t8 reference.fa reads_1.fastq reads_2.fastq | samtools sort -o mapped_reads_sorted.bam

bcftools mpileup mapped_reads_sorted.bam -f reference.fa --output samtools_mpileup.vcf

cat samtools_mpileup.vcf | bcftools call -mv -Ov -o samtools_call.vcf

vgteam vg variation graph • 177 views
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0
Entering edit mode
3 months ago
glenn.hickey ▴ 240

You might try this following vg augment option

-S, --keep-softclips include softclips from input alignments (they are cut by default)

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Entering edit mode

Hi,

I tried that already, but only a very small amounts of insertions are then detected (2 out of 24), and they got a low depth (≃3) while others SNPs got an average depth of 50.

I feel like it would be possible to detect more of them if I double my reads quantity, but it would be too unrealistic.

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