CBSU/3CPG/CVG Joint Workshop Series Reference genome based sequence variation detection

Transcription

1 CBSU/3CPG/CVG Joint Workshop Series Reference genome based sequence variation detection Computational Biology Service Unit (CBSU) Cornell Center for Comparative and Population Genomics (3CPG) Center for Vertebrate Genomics (CVG)

2 Two different data analysis strategies Assembly Alignment

3 De novo Assembly ACGAGCAACACGGTACCTA ACGGTACCTAAACCGG TACCTAAACCGGA TACCTAAACCGGACCCGGAAAGAC ACGAGCAACACGGTAGCTA ACGGTAGCTAAACCGG TAGCTAAACCGGA TAGCTAAACCGGACCCGGAAAGAC...ACGAGCAACACGGTACCTAAACCGGACCCGGAAAGAC......ACGAGCAACACGGTAGCTAAACCGGACCCGGAAAGAC...

4 De novo Assembly...ACGAGCAACACGGTACCTAAACCGGACCCGGAAAGAC......ACGAGCAACACGGTAGCTAAACCGGACCCGGAAAGAC... ACGAGCAACACGGTACCTA ACGGTACCTAAACCGG TACCTAAACCGGA TACCTAAACCGGACCCGGAAAGAC ACGAGCAACACGGTAGCTA ACGGTAGCTAAACCGG TAGCTAAACCGGA TAGCTAAACCGGACCCGGAAAGAC...ACGAGCAACACGGTACCTAAACCGGACCCGGAAAGAC......ACGAGCAACACGGTAGCTAAACCGGACCCGGAAAGAC...

5 Reference Alignment ACGAGCAACACGGTACCTA ACGGTACCTAAACCGG TACCTAAACCGGA TACCTAAACCGGACCCGGAAAGAC TAGCTAAACCGGA ACGGTAGCTAAACCGG ACGAGCAACACGGTAGCTA TAGCTAAACCGGACCCGGAAAGAC

6 Reference Alignment Reference Genome C ACGAGCAACACGGTACCTA ACGGTACCTAAACCGG TACCTAAACCGGA TACCTAAACCGGACCCGGAAAGAC ACGAGCAACACGGTAGCTA ACGGTAGCTAAACCGG TAGCTAAACCGGA TAGCTAAACCGGACCCGGAAAGAC TAGCTAAACCGGA ACGAGCAACACGGTACCTA ACGGTAGCTAAACCGG ACGGTACCTAAACCGG TACCTAAACCGGA ACGAGCAACACGGTAGCTA TACCTAAACCGGACCCGGAAAGAC TAGCTAAACCGGACCCGGAAAGAC

7 With limited number of individuals, whole genome/exome sequencing do not always reveal the causative mutations Chr Position Ref Coverage Depth Genotypes Gene chr C T() C() T() chr G C() G() C() chr G A() G() A() chr A M(AC) A() A() chr G G() R(GA) G() chr C Y(CT) Y(CT) C() chr G G() K(GT) K(GT) chr G G() R(GA) G() chr C C() Y(CT) Y(CT) chr A A() R(AG) R(AG) chr C C() M(CA) M(CA) chr T T() Y(TC) Y(TC) chr A A() M(AC) M(AC) chr A A() R(AG) A() chr A A() R(AG) R(AG) chr A R(AG) A() R(AG) chr T Y(TC) Y(TC) T() chr C T() Y(TC) T() chr G A() G() A() chr A C() A() C() chr T C() Y(CT) C() chr A G(KT) R(GA) G() chr G C(SG) G() G() chr G T(KG) G() T() chr T G(KT) T() G() chr C A() C() A() chr A G(RA) A() G()

8 With limited number of individuals, whole genome/exome sequencing do not always reveal the causative mutations Chr Position Ref Coverage Depth Genotypes Gene chr C T() C() T() chr G C() G() C() chr G A() G() A() chr A M(AC) A() A() chr G G() R(GA) G() chr C Y(CT) Y(CT) C() chr G G() K(GT) K(GT) chr G G() R(GA) G() chr C C() Y(CT) Y(CT) chr A A() R(AG) R(AG) chr C C() M(CA) M(CA) chr T T() Y(TC) Y(TC) chr A A() M(AC) M(AC) chr A A() R(AG) A() chr A A() R(AG) R(AG) chr A R(AG) A() R(AG) chr T Y(TC) Y(TC) T() chr C T() Y(TC) T() chr G A() G() A() chr A C() A() C() chr T C() Y(CT) C() chr A G(KT) R(GA) G() chr G C(SG) G() G() chr G T(KG) G() T() chr T G(KT) T() G() chr C A() C() A() chr A G(RA) A() G() Sequence a mapping population

9 Reference genome based sequence variation detection Step 1: Alignment FASTQ files Step 2: Call SNP/INDELs SAM/BAM files VCF file

10 Reference genome based sequence variation detection Step 3: Filter SNP/INDELs Step 4: Annotate SNP/INDELs

11 Reference genome based sequence variation detection Step 1: Alignment BWA Li H. and Durbin R. (2009) Bioinformatics, 25: Step 2: Call SNP/INDELs SAMtools or GATK + Picard Li H. et al. Bioinformatics, 25, Broad Institute

12 Reference genome based sequence variation detection Step 3: Filtering GATK Write your own code Step 4: Annotation Annovar

13 Standard file formats FASTQ SAM/BAM VCF

14 FASTQ ACCTTGTTGAGAAACAGGAGGTGTTGTTCTTCAAAG +20F75AAXX:5:1:335:1565 GGAAGCAACAGCTAATACATGAATGGATATCGATCG +20F75AAXX:5:1:466:1056 GCCCAACAAAGACCGGTCACCAAAGACAGATGATTC +20F75AAXX:5:1:256:1724 ]][]][]][[[[]L[[[[][[[Z[[[[[S[[ZW[[[

15 SAM file: HWI EAS83_20F7TAAXX:1:1: 379: M * 0 0 HWI EAS83_20F7TAAXX:1:1: 582:80 4 * 0 0 * * 0 0 AGAAAACT GCAAAGCA CGAGTCTA GCAGATAC h?dhhhld POhhhhhh hhhhhhhh hhhhhhhh hhhh XT:A:U NM:i:2 X0:i:1 X1:i:0 XM:i:2 XO:i:0 XG:i:0 MD:Z:2C32 G0 CCTT GCACCCTTT VbINbYZh_ AACTCGGG huhqhd\^ HWI CTAACTATC hfhhhhhhh EAS83_20F7TAAXX:1:1: TTGCTTCAC hhhhhhhh 98: M * 0 0 C hh XT:A:U NM:i:1 X0:i:1 X1:i:0 XM:i:1 XO:i:0 XG:i:0 MD:Z:33G2 ATGGCTGC hfhhhhahh CTCGCAGA `hhavheha ATCGAAAG hqkhkqa_ TTAGTGCC IIPPF@DhE HWI EAS83_20F7TAAXX:1:1: 169: M * 0 0 GCAC AAAACCAT ATCTGCTG GAAACTCT GCTTCCAC AAGC V CDhKDBhD hfagghmh ahhhhphh hhhhhhhh hhhh XT:A:U NM:i:2 X0:i:1 X1:i:0 XM:i:2 XO:i:0 XG:i:0 MD:Z:0T0C 34 Information encoded in SAM file Sequence (forward strand of the reference genome) Quality score Alignment information (position, strand, mismatches, gap) Ambigous alignments Paired end information Read group

16 BAM is a compressed SAM file BAM file is several times smaller than SAM; BAM file can be indexed and queried; Most software operates directly on BAM; BAM format can potentially replace fastq format.

17 VCF file variant call format ##fileformat=vcfv4.0 ##filedate= ##source=myimputationprogramv3.1 ##reference=1000genomespilot NCBI36 ##phasing=partial ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=.,Type=Float,Description="Allele Frequency"> ##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129"> ##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership"> ##FILTER=<ID=q10,Description="Quality below 10"> ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 NA00002 NA rs G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0 0:48:1:51,51 1 0:48:8:51,51 1/1:43:5:., T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0 0:49:3:58,50 0 1:3:5:65,3 0/0:41: rs A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1 2:21:6:23,27 2 1:2:0:18,2 2/2:35: T. 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0 0:54:7:56,60 0 0:48:4:51,51 0/0:61: microsat1 GTCT G,GTACT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3

18 Alignment with BWA Commonly used parameters: Alignment step (aln): n: maximum number of edit distance (default 0.04) o: maximum number of gap opens (default 1) Write SAM file step (samse or sampe): n maximum number of alignments to report

19 Converting SAM to BAM Index BAM Samtools: Picard: view; index SamFormatConverter; BuildBamIndex *** If you want to use Broad GATK software to call SNPs, do not use SAMtools, always use Picard for processing SAM and BAM files.

20 BAM file can be visualized with IGV software

21 Clean up the BAM file Mark possible PCR duplicates Base quality score recalibration Local realignment around indels

22 Clean up the BAM file Mark possible PCR duplicates ** For sequence reads with exact same sequence, only one copy is kept. Base quality score recalibration Local realignment around indels

23 Clean up the BAM file Mark possible PCR duplicates Base quality score recalibration Phred quality score: 20 > 1% error rate. Illumina quality score: 0 to 62, need to be calibrated to reflect error rate. Local realignment around indels

24 Clean up the BAM file Mark possible PCR duplicates Base quality score recalibration Local realignment around indels

25 Multi sample SNP and INDEL calling Use Unified Genotyper (GATK) or mpileup (SAMtools) to call SNP and INDEL from multiple samples. Set the variants calling threshold Emission threshold: Q10 (>10x) Q3(<10x) Confidence threshold: Q30(>10x) Q4(<10x)

26 Filtering Read depth (DP) Allele frequency (AF) Number of samples with data (NS)

27 SAMtools GATK/Picard SAM > BAM Flag possible PCR duplicates Quality score calibration INDEL realignment * Call variants on multiple samples Filtering ** * SAMtools mpileup has built in realignment tool ** Limited filtering function. Poor documentation.

28 GATK Documentation:

29 SAMtools Variants Calling Documentation:

30 Practical aspects 1. Experimental Design. 2. Computational Resource at Cornell.

31 Whole genome sequencing vs Targeted sequencing Target enrichment by array or in solution based capturing technology. (e.g. Exome sequencing).

32 Whole genome sequencing vs Genotyping by Sequencing (GBS) ApeK I site Line 1 Line 2 Line 3 Ed Buckler Lab ( overview)

33 Advantage of GBS over whole genome sequencing 1. Reduced cost by multiplexing; 2. Possible to map markers that are not on the reference genome;

34 To identify causative mutations in a mutant strain, it is necessary to use both sequencing and genetic linkage analysis.

35 Mapping and Mutation Identification of the Pooled F2 population * * X F1 * F2 * ***

36 Using SHOREmap for mapping and mutation identification SHOREmap Schneeberger K et al (2009) Nat Methods.6(8):550 1.

37 Alternative approach: test for enrichment of new mutations Zuryn et al. (2010) A Strategy for Direct Mapping and Identification of Mutations by Whole Genome Sequencing. Genetics 186:

38 Computational Resource at Cornell CBSU / 3CPG BioHPC Laboratory (625 Rhodes Hall) Office Hour: 1:00 to 3:00 PM every Monday. cbsu@cornell.edu to get an BioHPC lab account.

39 Training workshops Linux for Biologists Programming workshop (PERL)