Creating a SNP calling pipeline
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The document describes creating a SNP calling pipeline for potato data from RNA-Seq experiments. Key steps included aligning reads to the potato genome using BWA or Bowtie, converting SAM to BAM and sorting, generating coverage profiles with SAMtools, and calling SNPs from the BAM files using SAMtools and bcftools. SNPs identified from the RNA-Seq data were then selected for inclusion on an Illumina GoldenGate SNP chip to genotype samples for genetic mapping. Comparison of the SNP chip results to the original RNA-Seq data was performed to evaluate accuracy. Remaining questions around discrepancies in the data were noted for further investigation.
![Align (using BWA)
1) Index the potato genome assembly
bwa index [-a bwtsw|div|is] [-c]
<in.fasta>
2) Perform the alignment
bwa aln [options] <in.fasta>
<in.fq>
3) Output results in SAM format (single end)
bwa samse <in.fasta> <in.sai>
<in.fq> 5](https://image.slidesharecdn.com/pipeline-03x-130220181153-phpapp02/85/Creating-a-SNP-calling-pipeline-5-320.jpg)
![Align (using Bowtie)
1) Index the potato genome assembly
bowtie-build [options] <in.fasta>
<ebwt>
2) Perform the alignment and output results
bowtie [options] <ebwt> <in.fq>](https://image.slidesharecdn.com/pipeline-03x-130220181153-phpapp02/85/Creating-a-SNP-calling-pipeline-6-320.jpg)
Creating a SNP calling pipeline
- 1. Potato SNPs Dan Bolser and David Martin Next Gen Bug, Dundee 01/18/2010 1
- 2. Aims of the work 1) Learn about handling RNASeq Create a SNP calling pipeline 2) Select SNPs for genetic mapping Using Illumina's GoldenGate SNP chip (OPA) 2
- 3. Creating a SNP calling pipeline 3
- 4. 4
- 5. Align (using BWA) 1) Index the potato genome assembly bwa index [-a bwtsw|div|is] [-c] <in.fasta> 2) Perform the alignment bwa aln [options] <in.fasta> <in.fq> 3) Output results in SAM format (single end) bwa samse <in.fasta> <in.sai> <in.fq> 5
- 6. Align (using Bowtie) 1) Index the potato genome assembly bowtie-build [options] <in.fasta> <ebwt> 2) Perform the alignment and output results bowtie [options] <ebwt> <in.fq>
- 7. 7
- 8. Convert (using SAMtools) 1) Convert SAM to BAM for sorting samtools view -S -b <in.sam> 2) Sort BAM for SNP calling samtools sort <in.bam> <out.bam.s> Alignments are both compressed for long term storage and sorted for variant discovery. 8
- 9. 9
- 10. Coverage profiles / Depth vectors 10
- 11. SAMtools... Dump a coverage profile samtools mpileup -f <in.fasta> <my.bam.s> P1 244526 A 10 ...,.,,,.. BBQa`aaaa[ P1 244527 A 10 ...,.,,,.. BBZ_`^a_a[ P1 244528 C 10 .$.$.,.,,,.. >>RaZ`aaaa P1 244529 C 8 .,.,,,.. NaXaaaa` P1 244530 T 8 .,.,,,.. Xa_aaa` P1 244531 C 8 .,.,,,.. Rbabbaa P1 244532 T 9 .,.,,,..^~. EE^^^^^^A P1 244533 T 9 .,.,,,... BBB P1 244534 T 9 .$,$.,,,... @@^^^^^^E 11
- 12. SAMtools Bio::DB::Sam (BioPerl) Dump a coverage profile 2 12
- 13. SAMtools Bio::DB::Sam (BioPerl) P41630 Matches : 9 0233333333333345555555555 666778888888899999999999 999999999999999999999999 999976666666666665444444 44443332211111111000 13
- 14. 14
- 15. mpileup samtools mpileup collects summary information in the input BAMs, computes the likelihood of data given each possible genotype and stores the likelihoods in the BCF format. bcftools view applies the prior and does the actual calling. Finally, we filter. 15
- 16. SNP call 1) Index the potato genome assembly (again!) samtools faidx in.fasta 2) Run 'mpileup' to generate VCF format samtools mpileup -ug -f in.fasta my1.bam.s my2.bam.s > my.raw.bcf Actually, all we did (I think) is perform a format conversion (BAM to VCF).
- 17. VCF format 17
- 18. VCF format A standard format for sequence variation: SNPs, indels and structural variants. Compressed and indexed. Developed for the 1000 Genomes Project. VCFtools for VCF like SAMtools for SAM. Specification and tools available from http://vcftools.sourceforge.net 18
- 19. 19
- 20. SNP call and filter 1) Call SNPs bcftools view -bvcg my.raw.bcf > my.var.bcf 2) Filter SNPs bcftools view my.var.bcf | vcfutils.pl varFilter my.var.bcf > my.var.bcf.filt 20
- 21. 21
- 22. Aims of the work 1) Learn about handling RNASeq Create a SNP calling pipeline 2) Select SNPs for genetic mapping Using Illumina's GoldenGate SNP chip (OPA) 22
- 23. Select SNPs for genetic mapping Using Illumina's GoldenGate SNP chip (OPA) 23
- 24. SNP chip (OPA) construction A set of DM SNP positions was provided by the SolCAP project (RNASeq derived). A subset was selected for developing OPAs (Illumina’s SNP chip technology). OPAs were run, and results have now been compared to RNASeq. 24
- 25. Comparison (using an early SAMtools)
- 26. Comparison (using an early SAMtools)
- 27. 27
- 29. Comparison (using an early SAMtools)
- 30. Comparison (using new SAMtools)
- 33. Comparison (using new SAMtools)
- 34. Looking into the RNASeq data… 34
- 35. 35
- 36. Potato genome assembly RNASeq RNASeq read library read library 36
- 37. 37
- 38. 38
- 39. 39
- 40. 40
- 41. 41
- 42. A lot more questions to answer… Track down more ‘strange’ SNPs based on the expected AFS of the two samples. Go beyond bialleleic SNPs Check the OPA base... − Was the right base probed by the chip? 42
- 43. Thank you for your patience! 43
- 45. OPAs in 5 steps... The DNA sample is activated for binding to paramagnetic particles.
- 46. OPAs in 5 steps... Three oligos are designed for each SNP locus. Two are specific to each allele of the SNP site (ASO) and a Locus- Specific Oligo (LSO).
- 47. OPAs in 5 steps... Several wash steps remove excess and mis-hybridized oligos. Extension of the appropriate ASO and ligation to the LSO joins information about the genotype to the address sequence on the LSO.
- 48. OPAs in 5 steps... The single-stranded, dye-labeled DNAs are hybridized to their complement bead type through their unique address sequences.
- 49. OPAs in 5 steps... Key to the assay: Scalable, multiplexing sample preparation (one tube reaction). Highly parallel array- based read-out. High-quality data: Average call rates above 99% accuracy.
Editor's Notes
- #47: All three oligo sequences contain regions of genomic complementarity and universal PCR primer sites; the LSO also contains a unique address sequence that targets a particular bead type. Up to 1,536 SNPs may be interrogated simultaneously in this manner. During the primer hybridization process, the assay oligos hybridize to the genomic DNA sample bound to paramagnetic particles. Because hybridization occurs prior to any amplification steps, no amplification bias can be introduced into the assay.
- #48: Extension of the appropriate ASO and ligation of the extended product to the LSO joins information about the genotype present at the SNP site to the address sequence on the LSO Allele-specific primer extension (ASPE). This step is used to preferentially extend the correctly matched ASO (at the 3' end) up to the 5' end of the LSO primer.
- #49: One to one mapping between an address sequence on the array and the locus being scored. As a result of this labeling scheme, the PCR product consists of double stranded DNA of which one strand, containing the complement to the Illumicode, is labeled with either Cy3 or Cy5 in an allele specific manner, and a complementary strand labeled with biotin. The biotinylated strand is removed and the single, florescently labeled strand hybridized to the BeadArray.