Introduc)on to Bioinforma)cs of next- genera)on sequencing. Sequence acquisi)on and processing; genome mapping and alignment manipula)on
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1 Introduc)on to Bioinforma)cs of next- genera)on sequencing Sequence acquisi)on and processing; genome mapping and alignment manipula)on Ruslan Sadreyev Director of Bioinformatics Department of Molecular Biology, MGH Department of Pathology, MGH, HMS
2 Next-Generation Sequencing Core and Bioinformatics group at Molecular Biology Next- genera)on sequencing core Website: nextgen.mgh.harvard.edu Bring your DNA/RNA! Bioinforma)cs team Website: molbio.mgh.harvard.edu/department/bioinformadcs Bring your data! 5/18/17 2
3 Founding members of MGH Bioinformatics Consortium AnalyDc and TranslaDonal GeneDcs Unit Biomedical InformaDcs Core Center for Integrated DiagnosDcs BioinformaDcs Group BioinformaDcs at the MGH Cancer Center and Department of Pathology Genomics and Technology Core ITN/PHS InformaDon Systems/Immune Tolerance Network BioinformaDcs group at the Department of Molecular Biology MGH next- generadon sequencing core 5/18/17 3
4 Missions of Bioinformatics Consortium Support and collaboration Develop and support the informadcs component of fundamental, transladonal, and clinical research projects Consulting Plan experimental design and execudon of data generadon and analysis; advise on best pracdces Educational outreach EducaDon in general BioinformaDcs concepts and methods; helping researchers think about their data in quandtadvely rigorous terms Training Teach hands- on computadonal skills in stadsdcs and BioinformaDcs workflows 5/18/17 4
5 About this mini-course You can (a) get oriented in basic NGS BioinformaDcs concepts, approaches, and tools. (b) start asking right quesdons about your data. You cannot get (a) very deep coverage of a specific area/method/applicadon (b) hands- on computadonal experience Two seminars: Sequence acquisi)on and processing; genome mapping and alignment manipula)on Thurs May 18 at 2 pm Specific NGS applica)ons and public datasets Thurs May 25 at 2 pm 5/18/17 5
6 I used slides/images/material by BF Francis Ouelleae Istvan Albert Mik Black & ChrisDn Print Thomas Keane Illumina 5/18/17 6
7 A variety of existing NGS technologies Metzker (2010) Nature Rev GeneDcs 11 5/18/17 7
8 Sequencing by synthesis (SBS) Metzker (2010) Nature Rev GeneDcs 11 5/18/17 8
9 Paired-end sequencing: looking at both ends of the fragment
10 Paired-end sequencing: better mapping to genome Illumina, Inc
11 Indexing (barcoding) of multiple samples in a single lane
12 Major NGS applications: examples Whole Genome Shotgun Sequencing (WGS) Targeted/exome sequencing RNA- seq ChiP- seq Metagenomics (targeted region/ whole genome sequencing) MANY more 5/18/17 12
13 Basic workflow Randomly shear DNA + end repair + size select Append sequencing adapters Layout of library on sequencing slide or wells (e.g. C- Bot) For each library fragment, determine the first N bases at one or both ends of the fragment Image processing + base calling - > bases and quality (FASTQ) From: Thomas Keane 5/18/17 13
14 Illumina HiSeq Our current output: 8 lanes, M reads per lane From hap://
15 Illumina MiSeq: desktop device Fast Flexible (can do longer reads, up to 500 bp) ~10x fewer reads than HiSeq Cheaper per run (but not per read) ApplicaDons: Amplicon sequencing QC before large- scale runs Bacterial genomes.. Our current output: 1 lane, M reads per lane
16 Bioinformatics tools 5/18/17 From: istvan Albert 16
17 Base quality calibration Phred score: measure of p = Prob(erroneous base call): - 10log 10 (p) Q10 = 1 in 10 chance of incorrect base call Q20 = 1 in 100 chance of incorrect base call Q30 = 1 in 1000 chance of incorrect base call Rule of thumb: not useful data If <Q20 Standard assessment: propordon of bases with score Q30 Highest Phred scores are typically around Q /18/17 17
18 FASTQ format for NGS sequences 5/18/17 hap://en.wikipedia.org/wiki/fastq_format 18
19 Quality control of raw sequences: FASTQC hap:// 5/18/17 19
20 Good example: per base sequence quality hap:// 5/18/17 20
21 Good example: per base sequence content hap:// 5/18/17 21
22 Bad example: per base sequence quality hap:// 5/18/17 22
23 Bad example: per base sequence content hap:// 5/18/17 23
24 Mapping your reads 5/18/17 24
25 Short read alignment methods (mappers) hap://wwwdev.ebi.ac.uk/fg/hts_mappers/ 5/18/17 25 Fonseca N A et al. (2012) Bioinformatics 28:3169
26 Examples of common fast mappers Two popular mappers BowDe: hap://bowde- bio.sourceforge.net BWA: hap://bio- bwa.sourceforge.net Both are based on Burrows- Wheeler Transform (BWT) 5/18/17 26
27 Burrows-Wheeler transform makes the search for genome matches faster and more memory-efficient BWT was originally introduced as method for file compression (bzip2) 5/18/17 27 Trapnell & Salzberg (2009) Nature Biotech 27
28 Using BWA: example Create index for the genome: bwa index [-a bwtsw div is] [-c] <in.fasta> - a STR BWT construc)on algorithm: bwtsw or is bwtsw for human size genome, is for smaller genomes Create index for reads: bwa aln [options] <prefix> <in.fq> Align each single end fastq file individually Various op)ons to change the alignment parameters/scoring matrix/seed length Using sai files produced by aln step, produce alignment For paired- end reads: bwa sampe [options] <prefix> <in1.sai> <in2.sai> <in1.fq> <in2.fq> For unpaired reads: bwa samse [-n max_occ] <prefix> <in.sai> <in.fq> 5/18/17 28
29 Not all reads are alignable Sources of mismatches: 1. Sequencer miscalls 2. Actual differences between sample and reference: (a) Genome variadon (not the reference genome) ; (b) ContaminaDon (adapters, primers, different biological species) Typical good mappability rate > 70%- 80% 5/18/17 29
30 SAM/BAM formats Recent addition: CRAM is a more compact and efficient binary version of SAM 5/18/17 30
31 SAM/BAM formats 5/18/17 31
32 SAM format information at SAMtools website hap://samtools.sourceforge.net 5/18/17 32
33 Example: Two lines of a SAM file 5/18/17 33
34 SAM format specifications hap://samtools.sourceforge.net 5/18/17 34
35 Tools to work with SAM/BAM alignments Samtools - Sanger/C (hap://samtools.sourceforge.net) Convert SAM <- > BAM Sort, index, BAM files Flagstat summary of the mapping flags Merge muldple BAM files Rmdup remove PCR duplicates from the library preparadon Picard - Broad InsDtute/Java (hap://picard.sourceforge.net) MarkDuplicates, CollectAlignmentSummaryMetrics, CreateSequenceDicDonary, SamToFastq, MeanQualityByCycle, FixMateInformaDon. Bio- SamTool Perl (hap://search.cpan.org/~lds/bio- SamTools/) Pysam Python (hap://code.google.com/p/pysam/) 5/18/17 35
36 SAMTools 5/18/17 36
37 Example: generate and manipulate alignment: commands Unix/Linux/MacOS # extract specific region of genome samtools view b output.sorted.bam chr1: > myregion.bam hap://manuals.bioinformadcs.ucr.edu/home/ 5/18/17 ht- seq#toc- Rsamtools 37
38 Visualizing your data 5/18/17 38
39 Viewing data in a rich context on the web: UCSC Genome Browser 5/18/17 39
40 Viewing data in a rich context on the web: Ensemble browser at EBI 5/18/17 40
41 Viewing data on your local machine: IGV (Integrative Genomics Viewer) 5/18/17 41
42 Viewing data on your local machine: IGV 5/18/17 42
43 Viewing data on your local machine: IGB (Integrated Genome Browser) hap://bioviz.org/igb/ 5/18/17 43
44 Wig (Wiggle) format: position-centric data 5/18/17 44 Yildirim et al. (2011) Nat Struct Mol Biol. 19
45 Storing just genomic intervals: low-resolution but economic Genomic feature (interval): peak, gene, exon, etc. Chromosome start end name score(e.g. peak intensity) strand. Coordinates are based only on one strand. Standard representadon of intervals: start < end; even for reverse strand. 5/18/17 45
46 Two de facto standards of coordinate system GFF format (Sanger): BED format (USCS Browser): 5/18/17 46
47 Working with genomic intervals: BedTools hap://code.google.com/p/bedtools High- performance package operadng on genomic intervals in various file formats: BED, GFF, VCF, SAM, BAM Easy to download, install, and use in Unix/Linux/MAcOS 5/18/17 47
48 BedTools: choice of many operations 5/18/17 48
49 BedTools: examples of operations on genomic intervals 5/18/17 49
50 Galaxy hap://galaxy.psu.edu/ 5/18/17 50
51 Galaxy hap://galaxy.psu.edu/ Our Galaxy server at Molbio: hap://galaga.mgh.harvard.edu/galaxy (can be accessed inside Partners network) 5/18/17 51
52 Galaxy From: Mik Black & ChrisDn Print 5/18/17 52
53 Galaxy From: Mik Black & ChrisDn Print 5/18/17 53
54 Galaxy tools 5/18/17 54
55 Galaxy tools 5/18/17 55
56 Schedule Sequence acquisition and processing; genome mapping and alignment manipulation Thurs May 18 at 2 pm Specific NGS applications and public datasets Thurs May 25 at 2 pm 5/18/17 56
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