High throughput sequencing : informatics & software aspects

High throughput sequencing: informatics & software aspects Gabor T. Marth Boston College Biology Department BI543 Fall 2013 January 29, 2013

Traditional DNA sequencing

Genetics of living organisms Chromosomes DNA

Radioactive label gel sequencing

Four-color capillary sequencing ~1 Mb ~100 Mb >100 Mb ~3,000 Mb ABI 3700 four-color sequence trace

Individual human resequencing

Next-generation DNA sequencing

New sequencing technologies…

… vast throughput, many applications Illumina, SOLiD 1 Tb 100 Gb 10 Gb 454 1 Gb bases per machine run 100 Mb 10 Mb ABI / capillary 1 Mb 10 bp 100 bp 1,000 bp read length

Sequencing chemistries DNA base extension DNA ligation Church, 2005

Template clonal amplification Church, 2005

Massively parallel sequencing Church, 2005

Chemistry of paired-end sequencing Double strand DNA is folded into a bridge shape then separated into single strands. The end of each strand is then sequenced. (Figure courtesy of Illumina)

Paired-end reads • circularization: 500bp - 10kb (sweet spot ~3kb) • fragment length limited by library complexity Korbel et al. Science 2007 • fragment amplification: fragment length 100 - 600 bp • fragment length limited by amplification efficiency

Features of NGS data • Short sequence reads • 100-200bp • 25-35bp (micro-reads) • Huge amount of sequence per run • Up to gigabases per run • Huge number of reads per run • Up to 100’s of millions • Higher error as compared with Sanger sequencing • Error profile different to Sanger

Application areas of next-gen sequencing

Application areas • Genome resequencing • variant discovery • somatic mutation detection • mutational profiling • De novo assembly • Identification of protein-bound DNA • chromatin structure • methylation • transcription binding sites • RNA-Seq • expression • transcript discovery Mikkelsenet al. Nature 2007 Cloonanet al. Nature Methods, 2008

SNP and short-INDEL discovery

Structural variation detection • copy number (for amplifications, deletions) from depth of read coverage • structural variations (deletions, insertions, inversions and translocations) from paired-end read map locations

Identification of protein-bound DNA genome sequence aligned reads Chromatin structure (CHIP-SEQ) (Mikkelsen et al. Nature 2007) Transcription binding sites. (Robertson et al. Nature Methods, 2007)

Novel transcript discovery (genes) Mortazavi et al. Nature Methods • novel exons • novel transcripts containing known exons

Novel transcript discovery (miRNAs) Ruby et al. Cell, 2006

Expression profiling gene gene aligned reads aligned reads Jones-Rhoads et al. PLoS Genetics, 2007 • tag counting (e.g. SAGE, CAGE) • shotgun transcript sequencing

De novo genome sequencing Lander et al. Nature 2001 short reads read pairs longer reads assembled sequence contigs

The informatics of sequencing

IND (ii) read mapping (iv) SV calling (iii) SNP and short INDEL calling IND (i) base calling (v) data viewing, hypothesis generation Re-sequencing informatics pipeline REF

The variation discovery toolbox • base callers • read mappers • SNP callers • SV callers • assembly viewers

Raw data processing / base calling • These steps are usually handled well by the machine manufacturers’ software • What most analysts want to see is base calls and well-calibrated base quality values Trace extraction Base calling

Sequence traces are machine-specific Base calling is increasingly left to machine manufacturers

…where they give you the cover on the box Read mapping… Is like a jigsaw puzzle…

pieces that look like each other… …pieces with unique features Some pieces are easier to place than others…

Repeats  multiple mapping problem Lander et al. 2001

Paired-end (PE) reads fragment length: 1 – 10kb fragment length: 100 – 600bp PE reads are now the standard for whole-genome short-read sequencing Korbelet al. Science 2007

0.8 0.19 0.01 Mapping quality values

SNP calling

SNP calling: what goes into it? Base qualities sequencing error true polymorphism Base coverage Prior expectation

A A A A A C C C C C G G G G G T T T T T polymorphic permutation monomorphic permutation Bayesian posterior probability Base call + Base quality Expected polymorphism rate Base composition Depth of coverage Bayesian SNP calling

The PolyBayes software http://bioinformatics.bc.edu/~marth/PolyBayes • First statistically rigorous SNP discovery tool • Correctly analyzes alternative cDNA splice forms Marth et al., Nature Genetics, 1999

Insertion/deletion (INDEL) variants • These variants have been on the “radar screen” for decades • Accurate automated detection is difficult • Different mutation mechanisms • Often appear in repetitive sequence and therefore difficult to align • Often multi-allelic • Deleted allele has no base quality values

Alignment methods became more refined Original alignment After left realignment After haplotype-aware realignment

Medium length INDELs still a problem Guillermo Angel

Structural variation detection Feuket al. Nature Reviews Genetics, 2006

Structural variant detection (cont’d)

Read Depth: good for big CNVs Detection Approaches Reference Sample • Paired-end: all types of SV Lmap • Split-Readsgood break-point resolution read contig • deNovo Assembly~ the future SV slides courtesy of Chip Stewart, Boston College

SV detection – resolution Expected CNVs Karyotype Micro-array Sequencing Relative numbers of events CNV event length [bp]

Standard data formats Reads: FASTQ Alignments: SAM/BAM Variants: VCF

Tools for analyzing & manipulating 1000G data Alignments: SAM/BAM • samtools: http://samtools.sourceforge.net/ • BamTools: http://sourceforge.net/projects/bamtools/ • GATK: http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Variants: VCF • VCFTools: http://vcftools.sourceforge.net/ • VcfCTools: https://github.com/AlistairNWard/vcfCTools

High throughput sequencing : informatics & software aspects