Next-generation sequencing: the informatics angle Gabor T. Marth Boston College Biology Department CHI Next-Generation Data Analysis meeting Providence, RI September 22, 2008 Sequencing software tools for next-gen data Welcome Providence Next-generation sequencing read length bases per machine run 10 bp1,000 bp100 bp 1 Gb 100 Mb 10 Mb 10 Gb Illumina, AB/SOLiD short-read sequencers ABI capillary sequencer 454 pyrosequencer ( Mb in bp reads) (5-15Gb in bp reads) 1 Mb Individual human resequencing Whole-genome mutational profiling Expression analysis Technologies Roche / 454 system pyrosequencing technology variable read-length the only new technology with >100bp reads Illumina / Solexa Genome Analyzer fixed-length short-read sequencer very high throughput read properties are very close to traditional capillary sequences low INDEL error rate AB / SOLiD system ACGT A C G T 2 nd Base 1 st Base fixed-length short-reads very high throughput 2-base encoding system color-space informatics Helicos / Heliscope system short-read sequencer single molecule sequencing no amplification variable read-length error rate reduced with 2- pass template sequencing Data characteristics Read length read length [bp] ~ (variable) (fixed) (fixed) (variable) 400 Paired fragment-end reads fragment amplification: fragment length bp fragment length limited by amplification efficiency Korbel et al. Science 2007 paired-end read can improve read mapping accuracy (if unique map positions are required for both ends) or efficiency (if fragment length constraint is used to rescue non-uniquely mapping ends) instrumental for structural variation discovery circularization: 500bp - 10kb (sweet spot ~3kb) fragment length limited by library complexity Representational biases this affects genome resequencing (deeper starting read coverage is needed) will have major impact is on counting applications dispersed coverage distribution Amplification errors many reads from clonal copies of a single fragment early PCR errors in clonal read copies lead to false positive allele calls early amplification error gets propagated into every clonal copy Read quality Error rate (Solexa) Error rate (454) Per-read errors (Solexa) Per read errors (454) Applications Genome resequencing for variation discovery SNPs short INDELs structural variations the most immediate application area Genome resequencing for mutational profiling Organismal reference sequence likely to change classical genetics and mutational analysis De novo genome sequencing Lander et al. Nature 2001 difficult problem with short reads promising, especially as reads get longer Identification of protein-bound DNA Chromatin structure (CHIP-SEQ) (Mikkelsen et al. Nature 2007) Transcription binding sites. (Robertson et al. Nature Methods, 2007) DNA methylation. (Meissner et al. Nature 2008) natural applications for next-gen. sequencers Transcriptome sequencing: transcript discovery Mortazavi et al. Nature Methods 2008 Ruby et al. Cell, 2006 high-throughput, but short reads pose challenges Transcriptome sequencing: expression profiling Jones-Rhoads et al. PLoS Genetics, 2007 Cloonan et al. Nature Methods, 2008 high-throughput, short-read sequencing should make a major impact, and potentially replace expression microarrays Analysis software Individual resequencing (iii) read assembly REF (ii) read mapping IND (i) base calling IND (iv) SNP and short INDEL calling (vi) data validation, hypothesis generation (v) SV calling The variation discovery toolbox base callers read mappers SNP callers SV callers assembly viewers 1. Base calling base sequence base quality value sequence Base quality value calibration Recalibrated base quality values (Illumina) and they give you the picture on the box 2. Read mapping Read mapping is like doing a jigsaw puzzle you get the pieces Problem is, some pieces are easier to place than others Strategies to deal with non-unique mapping Mapping probabilities (qualities) read Paired-end read alignments Paired-end read alignments helps unique read placement PE sequences are now the norm for genome sequencing Gapped alignments Gapped alignments: allow mapping reads with insertion or deletion errors, and reads with bona fide INDEL alleles The ability to map reads with INDEL errors also improves the certainty of unique mapping 3. SNP and short-INDEL discovery capillary sequences: either clonal or diploid traces SNP and short-INDEL discovery (II) SNP INS New technologies are perfectly suitable for accurate SNP calling, and some also for short- INDEL detection New demands on SNP calling Rare alleles in 100s / 1,000s of samples More samples or deeper coverage / sample? Determining genotype directly from sequence AACGTTAGCATA AACGTTCGCATA AACGTTAGCATA individual 1 individual 3 individual 2 A/C C/CC/C A/A 4. Structural variation discovery software Navigation bar Fragment lengths in selected region Depth of coverage in selected region 5. Data visualization (assembly viewers) software development data validation hypothesis generation New analysis tools are needed 1.Tailoring existing tools for specialized applications (e.g. read mappers for transcriptome sequencing) 2.Analysis pipelines and viewers that focus on the essential results e.g. the few mutations in a mutant, or compare 1000 genome sequences (but hide most details) 3.Work-bench style tools to support downstream analysis Data storage and data standards What level of data to store? images traces base quality values base-called reads Data standards different data storage needs (archival, transfer, processing) often poses contradictory requirements (e.g. normalized vs. non-normalized storage of assembly, alignment, read, image data) even different analysis goals often call for different optimal storage / data access strategies (e.g. paired-end read analysis for SV detection vs. SNP calling) requirements include binary formats, fast sequential and / or random access, and flexible indexing (e.g. an entire genome assembly can no longer reside in RAM) Data standards (II) Sequence Read Format, SRF (Asim Siddiqui, UBC) Assembly format working groupGenotype Likelihood Format (Richard Durbin, Sanger) Summary Conclusions: next-gen sequencing software Next-generation sequencing is a boon for mass-scale human resequencing, whole-genome mutational profiling, expression analysis and epigenetic studies Informatics tools already effective for basic applications There is a need both for generic analysis tools e.g. flexible read aligners and for specialized tools tailored to specific applications (e.g. expression profiling) Move toward tools that focus on biological analysis Most challenges are technical in nature (e.g. data storage, useful data formats, fast read mapping) many of these will be addressed at this conference Credits Derek Barnett Eric Tsung Aaron Quinlan Damien Croteau-Chonka Weichun Huang Michael Stromberg Chip Stewart Michele Busby Positions Several postdoc positions are available mail Credits Elaine Mardis Andy Clark Aravinda Chakravarti Doug Smith Michael Egholm Scott Kahn Francisco de la Vega Kristen Stoops Ed Thayer