Bioinformatics in next generation sequencing projects

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1 Bioinformatics in next generation sequencing projects Rickard Sandberg Assistant Professor Department of Cell and Molecular Biology Karolinska Institutet May 2013

2 Standard sequence library generation

3 Illumina Sequencing Technology

4 Illumina (Solexa) Sequencing

5 Illumina paired-end and index-read sequencing

6 Once sequenced the problem becomes computational Computational analyses is the bottleneck Rapid improvement in sequencing Still need for customized analysis for most projects

7 Overview of computational analyses genome sequence assembled contig RNA-Seq expression levels ChIP-Seq peak calling Primary Analyses: Image analysis Base calling Mapping (Assembly) Data type specific analyses (e.g. peak calling, calculate expression) Custom project specific analyses

8 Preliminary Analyses Sequences and Real Time Analysis Quality scores Raw Image (TB) Text File (GB) Platform-specific analysis using the vendors programs

9 Sequenced reads Fasta file: >EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC Read identifier Fastq file: - EAS269:1:120:1786:18#0/1 GAACTCTGCCTTTTTCAGTGATGAGGAAAGGAGTTCTCTCTGGTCCCCAG +HWI - EAS269:1:120:1786:18#0/1 aaab^_u_aa [ U [ _Z ] a `WU_^X `GT^_ \ TM^ ^ \ \ Z \ YQVVXUBBBB Quality scores csfasta file >1_39_146_F3 T >1_39_194_F3 T SOLiD, QV file >1_39_146_F >1_39_194_F

10 Phred Quality Score, Q Each base call has an estimate of the probability of being wrong (error probability, p) Q = -10 * log 10 (p) Phred Quality Score Probability of incorrect base call Base call accuracy 10 1 in % 20 1 in % 30 1 in % 40 1 in % 50 1 in %

11 FastQ encodings

12 Fastq quality control (FastQC) Video tutorial:

13 Quality scores for each sequence position

14 Quality scores for each sequence position: A good run

15 GC for reads

16 Percent A,C,G,T at each position

17 Relative enrichment of kmers

18 Overview of computational analyses genome sequence assembled contig RNA-Seq expression levels ChIP-Seq peak calling Primary Analyses: Image analysis Base calling Mapping Assembly Data type specific analyses (e.g. peak calling, calculate expression) Custom project specific analyses

19 Short Read Assembly Velvet and SOAPdenovo de novo genomic assembler specially designed for short read sequencing technologies Nature 2009

20 Two principal approaches for transcriptome reconstruction

21 Genome-independent transcriptome reconstruction Default k = 25 Garbherr et al. Nature Biotechnology, July 2011

22 Finding novel non-annotated genes or transcript variants

23 Mapping of millions of short reads Task: Map millions of short sequences ( nt) onto a genome (3 000 Mbp ) or transcriptome Mismatches (sequencing errors and SNPs) Unique / Repetitive matches Indels (Normal variation, CNVs) Large rearrangements (translocations) BLAST, BLAT tools not designed for these tasks

24 Mapping of RNA-Seq reads STAR Garber et al Nat Methods

25 Mapping of splice junctions Exon n GTAAGT AG Exon n+1 1. compile sets of junctions 2. map reads towards genome + junction compilation + Genome Chromosome Fasta Files Known and putative splice junctions Fasta File

26 Tophat first Method A B C identify candidate exons via genomic mapping A B A C B C Generate possible pairings of exons A B A C B C Align unmappable reads to possible junctions

27 Longer reads By segmenting the long reads, and mapping the segments independently, we can look harder for junctions we might have missed with shorter reads >HWI-EAS229_75_30DY0AAXX:7:1:0:949 GATGTTCTCAGTGTCC GATGTAATCAGTGTCC AACCCTCTCAGTGTCC Running time independent of intron size Very long (100Kb+) intron

28 Mapping to transcriptome Gene: 5 UTR Exons Introns 3 UTR W C DNA (genome) Transcription pre-mrna AAAAA RNA processing (splicing, polyadenylation) mrna AAAAA

29 Microexons and junction coverage 2 or more splice junctions within the same read in-house mapping tophat mapping

30 Microexons and junction coverage 2 or more splice junctions within the same read in-house mapping tophat mapping Different read length will have different problems!

31 Example of STAR aligned single-cell RNA-Seq data Mapping'speed 308'M'reads'/'hour %'uniquely'mapping 60 %'multimapping 25 %'unmapped splice junctions with GT/AG with GC/AG 215 with AT/AC

32 Storing mapped Alignments Formats for storing alignments should include: genomic coordinates mismatches, insertion, deletions etc. quality information

33 Samtools Sequence Alignment Map (SAM) Generic Alignment format Supports long and short reads Human readable, flexible and compact Emerging standard Li H.*, Handsaker B.*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. BioinformaScs, 25, [PMID: ] h"p://samtools.sourceforge.net/

34 SAM Example Bit field, where 16 means reverse strand Alignment structure. Here: 22 aligned bases, then 731 bases intron, then 28 aligned bases Start position HWI - EAS269:1:114:1242:1582#0 16 chr Y M731N28M * 0 0 ATTTCGACCATGATCATCGAACCTTCCCCTGGATCCACTTCCACGATCAC #9 ; -7 +2@4 : 2=20-14= : ><?< ; : BB? : 4<BB?ABBBBABCBBBBC=BB NM: i : 0 XS: A:-

35 CIGAR Format M, match/ mismatch I, insertion D, deletion S, softclip... Ref: GCATTCAGATGCAGTACGC Read: cctcag--gcagtagtg Pos: 5 CIGAR: 2S4M3D6M3S 50M

36 Samtools for SAM/BAM files Library and software package (C, Java) Creating, sorting, indexing SAM & BAM Visualizing alignments in command SNP calling Short indel detection BAM (Binary representation of SAM) ~25% file size reduction

37 Read mapping statistics e.g. using RSeQC (package) Density of Reads Nucleotide Frequency A T G C GC content (%) Position of Read

38 Read mapping statistics: Read mapping across genes read number percentile of gene body (5' >3')

39 Read mapping statistics splicing junctions complete_novel 9% partial_novel 2% known 89%

40 Read mapping statistics: duplicate and unique reads Frequency Number of Reads (log10) Sequence base Mapping base Reads %

41 Read mapping statistics: q values on mapped reads Phred Quality Score Position of Read

42 Overview of computational analyses genome sequence assembled contig RNA-Seq expression levels ChIP-Seq peak calling Primary Analyses: Image analysis Base calling Mapping Assembly Data type specific analyses (e.g. peak calling, calculate expression) Custom project specific analyses

43 Visualization Integrated Genome Viewer (Broad Inst.) Custom tracks at UCSC Genome Browser

44 Peak characteristics differ with signal

45 Peak characteristics differ with signal H3K4me3: Sharp promoter peaks H3K36me3: Broad transcription elongation signal

46 Important file formats Sequences: FastQ Aligned reads: SAM/BAM Genome annotations: Bed, Gff Coverage: Wig, (Tdf)

47 BED format chrom - The name of the chromosome (e.g. chr3, chry, chr2_random) or scaffold (e.g. scaffold10671). chromstart - The starsng posison of the feature in the chromosome or scaffold. The first base in a chromosome is numbered 0. chromend - The ending posison of the feature in the chromosome or scaffold. The chromend base is not included in the display of the feature. For example, the first 100 bases of a chromosome are defined as chromstart=0, chromend=100, and span the bases numbered track name=pairedreads description="clone Paired Reads" usescore=1 chr

48 BED continued track name=pairedreads description="clone Paired Reads" usescore=1 chr cloneb ,399, 0,3601 strand - Defines the strand - either '+' or '-'. thickstart - The starting position at which the feature is drawn thickly (for example, the start codon in gene displays). thickend - The ending position at which the feature is drawn thickly (for example, the stop codon in gene displays). itemrgb - An RGB value of the form R,G,B (e.g. 255,0,0). If the track line itemrgb attribute is set to "On", this RBG value will determine the display color of the data contained in this BED line. NOTE: It is recommended that a simple color scheme (eight colors or less) be used with this attribute to avoid overwhelming the color resources of the Genome Browser and your Internet browser. blockcount - The number of blocks (exons) in the BED line. blocksizes - A comma-separated list of the block sizes. The number of items in this list should correspond to blockcount. blockstarts - A comma-separated list of block starts. All of the blockstart positions should be calculated relative to chromstart. The number of items in this list should correspond to blockcount.

49 WIG format (coverage format) Wiggle format (WIG) allows the display of continuous-valued data in a track format Variable step variablestep chrom=chr is equivalent to: variablestep chrom=chr2 span= Fixed step fixedstep chrom=chr3 start= step=

50 Data Repositories Short Read Archive (fastq) [discontinued!] European Nucleotide Archive Gene Expression Omnibus (bed, wig, fastq)

51 SEQAnswers, an active forum for discussions on next-generation sequencing methods and bioinformatics

52

53 Genome-independent transcriptome reconstruction: accuracy and coverage Garbherr et al. Nature Biotechnology, July 2011

54 Genome-independent transcriptome reconstruction: accuracy and coverage Garbherr et al. Nature Biotechnology, July 2011