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Mapping Raw Reads to a Reference Genome

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Shaojun Xie

ngs workshop

Data & Analytics
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Read Processing and Mapping: From Raw to Analysis-ready Reads Ben Passarelli Stem Cell Institute Genome Center NGS Workshop 12 September 2012
Click to edit Master title styleSamples to Information Variant calling Gene expression Chromatin structure Methylome Immunorepertoires De novo assembly …
Click to edit Master title style http://www.broadinstitute.org/gsa/wiki/images/7/7a/Overall_flow.jpg http://www.broadinstitute.org/gatk/guide/topic?name=intro Many Analysis Pipelines Start with Read Mapping http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html Genotyping (GATK) RNA-seq (Tuxedo)
Click to edit Master title styleFrom Raw to Analysis-ready Reads Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Session Topics • Understand read data formats and quality scores • Identify and fix some common read data problems • Find and prepare a genomic reference for mapping • Map reads to a genome reference • Understand alignment output • Sort, merge, index alignment for further analysis • Locally realign at indels to reduce alignment artifacts • Mark/eliminate duplicate reads • Recalibrate base quality scores • An easy way to get started
Click to edit Master title styleInstrument Output Illumina MiSeq Illumina HiSeq IonTorrent PGM Roche 454 Pacific Biosciences RS Images (.tiff) Cluster intensity file (.cif) Base call file (.bcl) Standard flowgram file (.sff) Movie Trace (.trc.h5) Pulse (.pls.h5) Base (.bas.h5) Sequence Data (FASTQ Format)
Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads FASTQ Format (Illumina Example) @DJG84KN1:272:D17DBACXX:2:1101:12432:5554 1:N:0:AGTCAA CAGGAGTCTTCGTACTGCTTCTCGGCCTCAGCCTGATCAGTCACACCGTT + BCCFFFDFHHHHHIJJIJJJJJJJIJJJJJJJJJJIJJJJJJJJJIJJJJ @DJG84KN1:272:D17DBACXX:2:1101:12454:5610 1:N:0:AG AAAACTCTTACTACATCAGTATGGCTTTTAAAACCTCTGTTTGGAGCCAG + @@@DD?DDHFDFHEHIIIHIIIIIBBGEBHIEDH=EEHI>FDABHHFGH2 @DJG84KN1:272:D17DBACXX:2:1101:12438:5704 1:N:0:AG CCTCCTGCTTAAAACCCAAAAGGTCAGAAGGATCGTGAGGCCCCGCTTTC + CCCFFFFFHHGHHJIJJJJJJJI@HGIJJJJIIIJGIGIHIJJJIIIIJJ @DJG84KN1:272:D17DBACXX:2:1101:12340:5711 1:N:0:AG GAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGG + CCCFFFFFHHHHHGGIJJJIJJJJJJIJJIJJJJJGIJJJHIIJJJIJJJ Read Record Header Read Bases Separator (with optional repeated header) Read Quality Scores Flow Cell ID Lane Tile Tile Coordinates Barcode NOTE: for paired-end runs, there is a second file with one-to-one corresponding headers and reads
Click to edit Master title style Phred* quality score Q with base-calling error probability P Q = -10 log10P * Name of first program to assign accurate base quality scores. From the Human Genome Project. SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS..................................................... ...............................IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII...................... LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.................................................... !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_`abcdefghijklmnopqrstuvwxyz{|}~ | | | | | | 33 59 64 73 104 126 S - Sanger Phred+33 range: 0 to 40 I - Illumina 1.3+ Phred+64 range: 0 to 40 L - Illumina 1.8+ Phred+33 range: 0 to 41 Q score Probability of base error Base confidence Sanger-encoded (Q Score + 33) ASCII character 10 0.1 90% “+” 20 0.01 99% “5” 30 0.001 99.9% “?” 40 0.0001 99.99% “I” Base Call Quality: Phred Quality Scores
Click to edit Master title style [benpass@solexalign]$ ls Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads File Organization [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Barcode [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Read [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Format [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed
Click to edit Master title styleInitial Read Assessment Common problems that can affect analysis • Low confidence base calls – typically toward ends of reads – criteria vary by application • Presence of adapter sequence in reads – poor fragment size selection – protocol execution or artifacts • Over-abundant sequence duplicates • Library contamination Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
Click to edit Master title styleInitial Read Assessment: FastQC • Free Download Download: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Tutorial : http://www.youtube.com/watch?v=bz93ReOv87Y • Samples reads (200K default): fast, low resource use Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
Click to edit Master title style http://proteo.me.uk/2011/05/interpreting-the-duplicate-sequence-plot-in-fastqc Read Duplication Read Assessment Examples ~8% of sampled sequences occur twice ~6% of sequences occur more than 10x ~71.48% of sequences are duplicates Sanger Quality Score by Cycle Median, Inner Quartile Range, 10-90 percentile range, Mean Note: Duplication based on read identity, not alignment at this point
Click to edit Master title style Per base sequence content should resemble this… Read Assessment Example (Cont’d)
Click to edit Master title styleRead Assessment Example (Cont’d)
Click to edit Master title styleRead Assessment Example (Cont’d) TruSeq Adapter, Index 9 5’ GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG
Click to edit Master title styleRead Assessment Example (Cont’d) Trim for base quality or adapters (run or library issue) Trim leading bases (library artifact)
Click to edit Master title style Fastx toolkit* http://hannonlab.cshl.edu/fastx_toolkit/ (partial list) FASTQ Information: Chart Quality Statistics and Nucleotide Distribution FASTQ Trimmer: Shortening FASTQ/FASTA reads (removing barcodes or noise). FASTQ Clipper: Removing sequencing adapters FASTQ Quality Filter: Filters sequences based on quality FASTQ Quality Trimmer: Trims (cuts) sequences based on quality FASTQ Masker: Masks nucleotides with 'N' (or other character) based on quality *defaults to old Illumina fastq (ASCII offset 64). Use –Q33 option. SepPrep https://github.com/jstjohn/SeqPrep Adapter trimming Merge overlapping paired-end read Biopython http://biopython.org, http://biopython.org/DIST/docs/tutorial/Tutorial.html (for python programmers) Especially useful for implementing custom/complex sequence analysis/manipulation Galaxy http://galaxy.psu.edu Great for beginners: upload data, point and click Just about everything you’ll see in today’s presentations Selected Tools to Process Reads
Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Read Mapping http://www.broadinstitute.org/igv/
Click to edit Master title style SOAP2 (2.20) Bowtie (0.12.8) BWA (0.6.2) Novoalign (2.07.00) License GPL v3 LGPL v3 GPL v3 Commercial Mismatch allowed exactly 0,1,2 0-3 max in read user specified. max is function of read length and error rate up to 8 or more Alignments reported per read random/all/none user selected user selected random/all/none Gapped alignment 1-3bp gap no yes up to 7bp Pair-end reads yes yes yes yes Best alignment minimal number of mismatches minimal number of mismatches minimal number of mismatches highest alignment score Trim bases 3’ end 3’ and 5’ end 3’ and 5’ end 3’ end Read Mapping: Aligning to a Reference Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
Click to edit Master title style BWA Features • Uses Burrows Wheeler Transform — fast — modest memory footprint (<4GB) • Accurate • Tolerates base mismatches — increased sensitivity — reduces allele bias • Gapped alignment for both single- and paired-ended reads • Automatically adjusts parameters based on read lengths and error rates • Native BAM/SAM output (the de facto standard) • Large installed base, well-supported • Open-source (no charge) Read Mapping: BWA Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
Click to edit Master title style Sequence References and Annotations http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/data.shtml http://www.ncbi.nlm.nih.gov/guide/howto/dwn-genome Comprehensive reference information http://hgdownload.cse.ucsc.edu/downloads.html Comprehensive reference, annotation, and translation information ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle References and SNP information data by GATK Human only http://cufflinks.cbcb.umd.edu/igenomes.html Pre-indexed references and gene annotations for Tuxedo suite Human, Mouse, Rat , Cow, Dog, Chicken, Drosophila, C. elegans, Yeast http://www.repeatmasker.org/
Click to edit Master title style Fasta Sequence Format >chr1 … TGGACTTGTGGCAGGAATgaaatccttagacctgtgctgtccaatatggt agccaccaggcacatgcagccactgagcacttgaaatgtggatagtctga attgagatgtgccataagtgtaaaatatgcaccaaatttcaaaggctaga aaaaaagaatgtaaaatatcttattattttatattgattacgtgctaaaa taaccatatttgggatatactggattttaaaaatatatcactaatttcat … >chr2 … >chr3 … • One or more sequences per file • “>” denotes beginning of sequence or contig • Subsequent lines up to the next “>” define sequence • Lowercase base denotes repeat masked base • Contig ID may have comments delimited by “|”
Click to edit Master title style Input files: reference.fasta, read1.fastq.gz, read2.fastq.gz Step 1: Index the genome (~3 CPU hours for a human genome reference): bwa index -a bwtsw reference.fasta Step 2: Generate alignments in Burrows-Wheeler transform suffix array coordinates: bwa aln reference.fasta read1.fastq.gz > read1.sai bwa aln reference.fasta read2.fastq.gz > read2.sai Apply option –q<quality threshold> to trim poor quality bases at 3'-ends of reads Step 3: Generate alignments in the SAM format (paired-end): bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz > alignment_ouput.sam http://bio-bwa.sourceforge.net/bwa.shtml Running BWA
Click to edit Master title style Simple Form: bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz > alignment.sam Output to BAM: bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz | samtools view -Sbh - > alignment.bam With Read Group Information: bwa sampe -r "@RGtID:readgroupIDtLB:librarynametSM:samplenametPL:ILLUMINA“ reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz | samtools view -Sbh - > alignment.bam Running BWA (Cont’d)
Click to edit Master title styleSAM (BAM) Format Sequence Alignment/Map format – Universal standard – Human-readable (SAM) and compact (BAM) forms Structure – Header version, sort order, reference sequences, read groups, program/processing history – Alignment records
Click to edit Master title style [benpass align_genotype]$ samtools view -H allY.recalibrated.merge.bam @HD VN:1.0 GO:none SO:coordinate @SQ SN:chrM LN:16571 @SQ SN:chr1 LN:249250621 @SQ SN:chr2 LN:243199373 @SQ SN:chr3 LN:198022430 … @SQ SN:chr19 LN:59128983 @SQ SN:chr20 LN:63025520 @SQ SN:chr21 LN:48129895 @SQ SN:chr22 LN:51304566 @SQ SN:chrX LN:155270560 @SQ SN:chrY LN:59373566 … @RG ID:86-191 PL:ILLUMINA LB:IL500 SM:86-191-1 @RG ID:BsK010 PL:ILLUMINA LB:IL501 SM:BsK010-1 @RG ID:Bsk136 PL:ILLUMINA LB:IL502 SM:Bsk136-1 @RG ID:MAK001 PL:ILLUMINA LB:IL503 SM:MAK001-1 @RG ID:NG87 PL:ILLUMINA LB:IL504 SM:NG87-1 … @RG ID:SDH023 PL:ILLUMINA LB:IL508 SM:SDH023 @PG ID:GATK IndelRealigner VN:2.0-39-gd091f72 CL:knownAlleles=[] targetIntervals=tmp.intervals.list LODThresholdForCleaning=5.0 consensusDeterminationModel=USE_READS entropyThreshold=0.15 maxReadsInMemory=150000 maxIsizeForMovement=3000 maxPositionalMoveAllowed=200 maxConsensuses=30 maxReadsForConsensuses=120 maxReadsForRealignment=20000 noOriginalAlignmentTags=false nWayOut=null generate_nWayOut_md5s=false check_early=false noPGTag=false keepPGTags=false indelsFileForDebugging=null statisticsFileForDebugging=null SNPsFileForDebugging=null @PG ID:bwa PN:bwa VN:0.6.2-r126 samtools to view bam headersort order reference sequence names with lengths read groups with platform, library and sample information program (analysis) history SAM/BAM Format: Header
Click to edit Master title style [benpass align_genotype]$ samtools view allY.recalibrated.merge.bam HW-ST605:127:B0568ABXX:2:1201:10933:3739 147 chr1 27675 60 101M = 27588 -188 TCATTTTATGGCCCCTTCTTCCTATATCTGGTAGCTTTTAAATGATGACCATGTAGATAATCTTTATTGTCCCTCTTTCAGCAGACGGTATTTTCTTATGC =7;:;<=??<=BCCEFFEJFCEGGEFFDF?BEA@DEDFEFFDE>EE@E@ADCACB>CCDCBACDCDDDAB@@BCADDCBC@BCBB8@ABCCCDCBDA@>:/ RG:Z:86-191 HW-ST605:127:B0568ABXX:3:1104:21059:173553 83 chr1 27682 60 101M = 27664 -119 ATGGCCCCTTCTTCCTATATCTGGTAGCTTTTAAATGATGACCATGTAGATAATCTTTATTGTCCCTCTTTCAGCAGACGGTATTTTCTTATGCTACAGTA 8;8.7::<?=BDHFHGFFDCGDAACCABHCCBDFBE</BA4//BB@BCAA@CBA@CB@ABA>A??@B@BBACA>?;A@8??CABBBA@AAAA?AA??@BB0 RG:Z:SDH023 * Many fields after column 12 deleted (e.g., recalibrated base scores) have been deleted for improved readability SAM/BAM Format: Alignment Records http://samtools.sourceforge.net/SAM1.pdf 1 3 4 5 6 8 9 10 11
Click to edit Master title style • Subsequent steps require sorted and indexed bams – Sort orders: karyotypic, lexicographical – Indexing improves analysis performance • Picard tools: fast, portable, free http://picard.sourceforge.net/command-line-overview.shtml Sort: SortSam.jar Merge: MergeSamFiles.jar Index: BuildBamIndex.jar • Order: sort, merge (optional), index Preparing for Next Steps Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
Click to edit Master title styleLocal Realignment Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads • BWT-based alignment is fast for matching reads to reference • Individual base alignments often sub-optimal at indels • Approach – Fast read mapping with BWT-based aligner – Realign reads at indel sites using gold standard (but much slower) Smith-Waterman1 algorithm • Benefits – Refines location of indels – Reduces erroneous SNP calls – Very high alignment accuracy in significantly less time, with fewer resources 1Smith, Temple F.; and Waterman, Michael S. (1981). "Identification of Common Molecular Subsequences". Journal of Molecular Biology 147: 195–197. doi:10.1016/0022-2836(81)90087-5. PMID 7265238
Click to edit Master title styleLocal Realignment DePristo MA, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011 May;43(5):491-8. PMID: 21478889 Post re-alignment at indelsRaw BWA alignment
Click to edit Master title style • Covered in genotyping presentation • Note that this is done after alignment Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Duplicate Marking
Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads STEP 1: Find covariates at non-dbSNP sites using: Reported quality score The position within the read The preceding and current nucleotide (sequencer properties) java -Xmx4g -jar GenomeAnalysisTK.jar -T BaseRecalibrator -I alignment.bam -R hg19/ucsc.hg19.fasta -knownSites hg19/dbsnp_135.hg19.vcf -o alignment.recal_data.grp STEP 2: Generate BAM with recalibrated base scores: java -Xmx4g -jar GenomeAnalysisTK.jar -T PrintReads -R hg19/ucsc.hg19.fasta -I alignment.bam -BQSR alignment.recal_data.grp -o alignment.recalibrated.bam Base Quality Recalibration
Click to edit Master title styleBase Quality Recalibration (Cont’d)
Click to edit Master title styleGetting Started Is there an easier way to get started?!!
Click to edit Master title styleGetting Started http://galaxy.psu.edu/ Click “Use Galaxy”
Click to edit Master title styleGetting Started http://galaxy.psu.edu/ Click “Use Galaxy”
Click to edit Master title styleQ&A

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Mapping Raw Reads to a Reference Genome

  • 1. Read Processing and Mapping: From Raw to Analysis-ready Reads Ben Passarelli Stem Cell Institute Genome Center NGS Workshop 12 September 2012
  • 2. Click to edit Master title styleSamples to Information Variant calling Gene expression Chromatin structure Methylome Immunorepertoires De novo assembly …
  • 3. Click to edit Master title style http://www.broadinstitute.org/gsa/wiki/images/7/7a/Overall_flow.jpg http://www.broadinstitute.org/gatk/guide/topic?name=intro Many Analysis Pipelines Start with Read Mapping http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html Genotyping (GATK) RNA-seq (Tuxedo)
  • 4. Click to edit Master title styleFrom Raw to Analysis-ready Reads Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Session Topics • Understand read data formats and quality scores • Identify and fix some common read data problems • Find and prepare a genomic reference for mapping • Map reads to a genome reference • Understand alignment output • Sort, merge, index alignment for further analysis • Locally realign at indels to reduce alignment artifacts • Mark/eliminate duplicate reads • Recalibrate base quality scores • An easy way to get started
  • 5. Click to edit Master title styleInstrument Output Illumina MiSeq Illumina HiSeq IonTorrent PGM Roche 454 Pacific Biosciences RS Images (.tiff) Cluster intensity file (.cif) Base call file (.bcl) Standard flowgram file (.sff) Movie Trace (.trc.h5) Pulse (.pls.h5) Base (.bas.h5) Sequence Data (FASTQ Format)
  • 6. Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads FASTQ Format (Illumina Example) @DJG84KN1:272:D17DBACXX:2:1101:12432:5554 1:N:0:AGTCAA CAGGAGTCTTCGTACTGCTTCTCGGCCTCAGCCTGATCAGTCACACCGTT + BCCFFFDFHHHHHIJJIJJJJJJJIJJJJJJJJJJIJJJJJJJJJIJJJJ @DJG84KN1:272:D17DBACXX:2:1101:12454:5610 1:N:0:AG AAAACTCTTACTACATCAGTATGGCTTTTAAAACCTCTGTTTGGAGCCAG + @@@DD?DDHFDFHEHIIIHIIIIIBBGEBHIEDH=EEHI>FDABHHFGH2 @DJG84KN1:272:D17DBACXX:2:1101:12438:5704 1:N:0:AG CCTCCTGCTTAAAACCCAAAAGGTCAGAAGGATCGTGAGGCCCCGCTTTC + CCCFFFFFHHGHHJIJJJJJJJI@HGIJJJJIIIJGIGIHIJJJIIIIJJ @DJG84KN1:272:D17DBACXX:2:1101:12340:5711 1:N:0:AG GAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGG + CCCFFFFFHHHHHGGIJJJIJJJJJJIJJIJJJJJGIJJJHIIJJJIJJJ Read Record Header Read Bases Separator (with optional repeated header) Read Quality Scores Flow Cell ID Lane Tile Tile Coordinates Barcode NOTE: for paired-end runs, there is a second file with one-to-one corresponding headers and reads
  • 7. Click to edit Master title style Phred* quality score Q with base-calling error probability P Q = -10 log10P * Name of first program to assign accurate base quality scores. From the Human Genome Project. SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS..................................................... ...............................IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII...................... LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.................................................... !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]^_`abcdefghijklmnopqrstuvwxyz{|}~ | | | | | | 33 59 64 73 104 126 S - Sanger Phred+33 range: 0 to 40 I - Illumina 1.3+ Phred+64 range: 0 to 40 L - Illumina 1.8+ Phred+33 range: 0 to 41 Q score Probability of base error Base confidence Sanger-encoded (Q Score + 33) ASCII character 10 0.1 90% “+” 20 0.01 99% “5” 30 0.001 99.9% “?” 40 0.0001 99.99% “I” Base Call Quality: Phred Quality Scores
  • 8. Click to edit Master title style [benpass@solexalign]$ ls Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads File Organization [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Barcode [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Read [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz Format [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed [benpass@solexalign]$ ls Sample_FS53_EPCAM+_CD10-_IL2270-18 Sample_FS53_EPCAM+_CD10+_IL2269-19 Sample_COH77_CD49F-_IL2275-13 Sample_COH77_CD49F+_CD66-_IL2274-14 Sample_COH77_CD49F+_CD66+_IL2273-15 Sample_COH74_EPCAM+_CD10-_IL2272-16 Sample_COH74_EPCAM+_CD10+_IL2271-17 Sample_COH69_EPCAM+_CD10-_IL2268-20 Sample_COH69_EPCAM+_CD10+_IL2267-21 [benpass@solexalign]$ ls Sample_COH77_CD49F-_IL2275-13 COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R1_004.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_001.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_002.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_003.fastq.gz COH77_CD49F-_IL2275-13_AGTCAA_L002_R2_004.fastq.gz gzip compressed
  • 9. Click to edit Master title styleInitial Read Assessment Common problems that can affect analysis • Low confidence base calls – typically toward ends of reads – criteria vary by application • Presence of adapter sequence in reads – poor fragment size selection – protocol execution or artifacts • Over-abundant sequence duplicates • Library contamination Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
  • 10. Click to edit Master title styleInitial Read Assessment: FastQC • Free Download Download: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Tutorial : http://www.youtube.com/watch?v=bz93ReOv87Y • Samples reads (200K default): fast, low resource use Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
  • 11. Click to edit Master title style http://proteo.me.uk/2011/05/interpreting-the-duplicate-sequence-plot-in-fastqc Read Duplication Read Assessment Examples ~8% of sampled sequences occur twice ~6% of sequences occur more than 10x ~71.48% of sequences are duplicates Sanger Quality Score by Cycle Median, Inner Quartile Range, 10-90 percentile range, Mean Note: Duplication based on read identity, not alignment at this point
  • 12. Click to edit Master title style Per base sequence content should resemble this… Read Assessment Example (Cont’d)
  • 13. Click to edit Master title styleRead Assessment Example (Cont’d)
  • 14. Click to edit Master title styleRead Assessment Example (Cont’d) TruSeq Adapter, Index 9 5’ GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG
  • 15. Click to edit Master title styleRead Assessment Example (Cont’d) Trim for base quality or adapters (run or library issue) Trim leading bases (library artifact)
  • 16. Click to edit Master title style Fastx toolkit* http://hannonlab.cshl.edu/fastx_toolkit/ (partial list) FASTQ Information: Chart Quality Statistics and Nucleotide Distribution FASTQ Trimmer: Shortening FASTQ/FASTA reads (removing barcodes or noise). FASTQ Clipper: Removing sequencing adapters FASTQ Quality Filter: Filters sequences based on quality FASTQ Quality Trimmer: Trims (cuts) sequences based on quality FASTQ Masker: Masks nucleotides with 'N' (or other character) based on quality *defaults to old Illumina fastq (ASCII offset 64). Use –Q33 option. SepPrep https://github.com/jstjohn/SeqPrep Adapter trimming Merge overlapping paired-end read Biopython http://biopython.org, http://biopython.org/DIST/docs/tutorial/Tutorial.html (for python programmers) Especially useful for implementing custom/complex sequence analysis/manipulation Galaxy http://galaxy.psu.edu Great for beginners: upload data, point and click Just about everything you’ll see in today’s presentations Selected Tools to Process Reads
  • 17. Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Read Mapping http://www.broadinstitute.org/igv/
  • 18. Click to edit Master title style SOAP2 (2.20) Bowtie (0.12.8) BWA (0.6.2) Novoalign (2.07.00) License GPL v3 LGPL v3 GPL v3 Commercial Mismatch allowed exactly 0,1,2 0-3 max in read user specified. max is function of read length and error rate up to 8 or more Alignments reported per read random/all/none user selected user selected random/all/none Gapped alignment 1-3bp gap no yes up to 7bp Pair-end reads yes yes yes yes Best alignment minimal number of mismatches minimal number of mismatches minimal number of mismatches highest alignment score Trim bases 3’ end 3’ and 5’ end 3’ and 5’ end 3’ end Read Mapping: Aligning to a Reference Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
  • 19. Click to edit Master title style BWA Features • Uses Burrows Wheeler Transform — fast — modest memory footprint (<4GB) • Accurate • Tolerates base mismatches — increased sensitivity — reduces allele bias • Gapped alignment for both single- and paired-ended reads • Automatically adjusts parameters based on read lengths and error rates • Native BAM/SAM output (the de facto standard) • Large installed base, well-supported • Open-source (no charge) Read Mapping: BWA Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
  • 20. Click to edit Master title style Sequence References and Annotations http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/data.shtml http://www.ncbi.nlm.nih.gov/guide/howto/dwn-genome Comprehensive reference information http://hgdownload.cse.ucsc.edu/downloads.html Comprehensive reference, annotation, and translation information ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle References and SNP information data by GATK Human only http://cufflinks.cbcb.umd.edu/igenomes.html Pre-indexed references and gene annotations for Tuxedo suite Human, Mouse, Rat , Cow, Dog, Chicken, Drosophila, C. elegans, Yeast http://www.repeatmasker.org/
  • 21. Click to edit Master title style Fasta Sequence Format >chr1 … TGGACTTGTGGCAGGAATgaaatccttagacctgtgctgtccaatatggt agccaccaggcacatgcagccactgagcacttgaaatgtggatagtctga attgagatgtgccataagtgtaaaatatgcaccaaatttcaaaggctaga aaaaaagaatgtaaaatatcttattattttatattgattacgtgctaaaa taaccatatttgggatatactggattttaaaaatatatcactaatttcat … >chr2 … >chr3 … • One or more sequences per file • “>” denotes beginning of sequence or contig • Subsequent lines up to the next “>” define sequence • Lowercase base denotes repeat masked base • Contig ID may have comments delimited by “|”
  • 22. Click to edit Master title style Input files: reference.fasta, read1.fastq.gz, read2.fastq.gz Step 1: Index the genome (~3 CPU hours for a human genome reference): bwa index -a bwtsw reference.fasta Step 2: Generate alignments in Burrows-Wheeler transform suffix array coordinates: bwa aln reference.fasta read1.fastq.gz > read1.sai bwa aln reference.fasta read2.fastq.gz > read2.sai Apply option –q<quality threshold> to trim poor quality bases at 3'-ends of reads Step 3: Generate alignments in the SAM format (paired-end): bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz > alignment_ouput.sam http://bio-bwa.sourceforge.net/bwa.shtml Running BWA
  • 23. Click to edit Master title style Simple Form: bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz > alignment.sam Output to BAM: bwa sampe reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz | samtools view -Sbh - > alignment.bam With Read Group Information: bwa sampe -r "@RGtID:readgroupIDtLB:librarynametSM:samplenametPL:ILLUMINA“ reference.fasta read1.sai read2.sai read1.fastq.gz read2.fastq.gz | samtools view -Sbh - > alignment.bam Running BWA (Cont’d)
  • 24. Click to edit Master title styleSAM (BAM) Format Sequence Alignment/Map format – Universal standard – Human-readable (SAM) and compact (BAM) forms Structure – Header version, sort order, reference sequences, read groups, program/processing history – Alignment records
  • 25. Click to edit Master title style [benpass align_genotype]$ samtools view -H allY.recalibrated.merge.bam @HD VN:1.0 GO:none SO:coordinate @SQ SN:chrM LN:16571 @SQ SN:chr1 LN:249250621 @SQ SN:chr2 LN:243199373 @SQ SN:chr3 LN:198022430 … @SQ SN:chr19 LN:59128983 @SQ SN:chr20 LN:63025520 @SQ SN:chr21 LN:48129895 @SQ SN:chr22 LN:51304566 @SQ SN:chrX LN:155270560 @SQ SN:chrY LN:59373566 … @RG ID:86-191 PL:ILLUMINA LB:IL500 SM:86-191-1 @RG ID:BsK010 PL:ILLUMINA LB:IL501 SM:BsK010-1 @RG ID:Bsk136 PL:ILLUMINA LB:IL502 SM:Bsk136-1 @RG ID:MAK001 PL:ILLUMINA LB:IL503 SM:MAK001-1 @RG ID:NG87 PL:ILLUMINA LB:IL504 SM:NG87-1 … @RG ID:SDH023 PL:ILLUMINA LB:IL508 SM:SDH023 @PG ID:GATK IndelRealigner VN:2.0-39-gd091f72 CL:knownAlleles=[] targetIntervals=tmp.intervals.list LODThresholdForCleaning=5.0 consensusDeterminationModel=USE_READS entropyThreshold=0.15 maxReadsInMemory=150000 maxIsizeForMovement=3000 maxPositionalMoveAllowed=200 maxConsensuses=30 maxReadsForConsensuses=120 maxReadsForRealignment=20000 noOriginalAlignmentTags=false nWayOut=null generate_nWayOut_md5s=false check_early=false noPGTag=false keepPGTags=false indelsFileForDebugging=null statisticsFileForDebugging=null SNPsFileForDebugging=null @PG ID:bwa PN:bwa VN:0.6.2-r126 samtools to view bam headersort order reference sequence names with lengths read groups with platform, library and sample information program (analysis) history SAM/BAM Format: Header
  • 26. Click to edit Master title style [benpass align_genotype]$ samtools view allY.recalibrated.merge.bam HW-ST605:127:B0568ABXX:2:1201:10933:3739 147 chr1 27675 60 101M = 27588 -188 TCATTTTATGGCCCCTTCTTCCTATATCTGGTAGCTTTTAAATGATGACCATGTAGATAATCTTTATTGTCCCTCTTTCAGCAGACGGTATTTTCTTATGC =7;:;<=??<=BCCEFFEJFCEGGEFFDF?BEA@DEDFEFFDE>EE@E@ADCACB>CCDCBACDCDDDAB@@BCADDCBC@BCBB8@ABCCCDCBDA@>:/ RG:Z:86-191 HW-ST605:127:B0568ABXX:3:1104:21059:173553 83 chr1 27682 60 101M = 27664 -119 ATGGCCCCTTCTTCCTATATCTGGTAGCTTTTAAATGATGACCATGTAGATAATCTTTATTGTCCCTCTTTCAGCAGACGGTATTTTCTTATGCTACAGTA 8;8.7::<?=BDHFHGFFDCGDAACCABHCCBDFBE</BA4//BB@BCAA@CBA@CB@ABA>A??@B@BBACA>?;A@8??CABBBA@AAAA?AA??@BB0 RG:Z:SDH023 * Many fields after column 12 deleted (e.g., recalibrated base scores) have been deleted for improved readability SAM/BAM Format: Alignment Records http://samtools.sourceforge.net/SAM1.pdf 1 3 4 5 6 8 9 10 11
  • 27. Click to edit Master title style • Subsequent steps require sorted and indexed bams – Sort orders: karyotypic, lexicographical – Indexing improves analysis performance • Picard tools: fast, portable, free http://picard.sourceforge.net/command-line-overview.shtml Sort: SortSam.jar Merge: MergeSamFiles.jar Index: BuildBamIndex.jar • Order: sort, merge (optional), index Preparing for Next Steps Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads
  • 28. Click to edit Master title styleLocal Realignment Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads • BWT-based alignment is fast for matching reads to reference • Individual base alignments often sub-optimal at indels • Approach – Fast read mapping with BWT-based aligner – Realign reads at indel sites using gold standard (but much slower) Smith-Waterman1 algorithm • Benefits – Refines location of indels – Reduces erroneous SNP calls – Very high alignment accuracy in significantly less time, with fewer resources 1Smith, Temple F.; and Waterman, Michael S. (1981). "Identification of Common Molecular Subsequences". Journal of Molecular Biology 147: 195–197. doi:10.1016/0022-2836(81)90087-5. PMID 7265238
  • 29. Click to edit Master title styleLocal Realignment DePristo MA, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011 May;43(5):491-8. PMID: 21478889 Post re-alignment at indelsRaw BWA alignment
  • 30. Click to edit Master title style • Covered in genotyping presentation • Note that this is done after alignment Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads Duplicate Marking
  • 31. Click to edit Master title style Raw reads Read assessment and prep Mapping Local realignment Duplicate marking Base quality recalibration Analysis-ready reads STEP 1: Find covariates at non-dbSNP sites using: Reported quality score The position within the read The preceding and current nucleotide (sequencer properties) java -Xmx4g -jar GenomeAnalysisTK.jar -T BaseRecalibrator -I alignment.bam -R hg19/ucsc.hg19.fasta -knownSites hg19/dbsnp_135.hg19.vcf -o alignment.recal_data.grp STEP 2: Generate BAM with recalibrated base scores: java -Xmx4g -jar GenomeAnalysisTK.jar -T PrintReads -R hg19/ucsc.hg19.fasta -I alignment.bam -BQSR alignment.recal_data.grp -o alignment.recalibrated.bam Base Quality Recalibration
  • 32. Click to edit Master title styleBase Quality Recalibration (Cont’d)
  • 33. Click to edit Master title styleGetting Started Is there an easier way to get started?!!
  • 34. Click to edit Master title styleGetting Started http://galaxy.psu.edu/ Click “Use Galaxy”
  • 35. Click to edit Master title styleGetting Started http://galaxy.psu.edu/ Click “Use Galaxy”
  • 36. Click to edit Master title styleQ&A

Editor's Notes

  1. More samples, more data, more runs. And more customers. As the HTS is adopted, computer sophistication of average user is less though amount of data, variety of data types is more complicated than ever.
  2. Whether Genotyping, RNA-seq, ChIPseq, Methylation analysis – data requires processing. These number and makeup of steps is Evolving asynchronously.