Bioinformatics Methods and Computer Programs for Next-Generation Sequencing Data Analysis Gabor Marth Boston College Biology Next Generation Sequencing Technologies for Antibody Clone Screening April 6, 2009

New sequencing technologies…

… offer vast throughput read length bases per machine run 10 bp1,000 bp100 bp 1 Gb 100 Mb 10 Mb 10 Gb Illumina/Solexa, AB/SOLiD sequencers ABI capillary sequencer Roche/454 pyrosequencer ( Mb in bp reads) (10-30Gb in bp reads) 1 Mb 100 Gb

Roche / 454 pyrosequencing technology variable read-length the only new technology with >100bp reads

Illumina / Solexa fixed-length short-read sequencer very high throughput read properties are very close to traditional capillary sequences

AB / SOLiD 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 short-read sequencer single molecule sequencing no amplification variable read-length

Many applications organismal resequencing & de novo sequencing Ruby et al. Cell, 2006 Jones-Rhoades et al. PLoS Genetics, 2007 transcriptome sequencing for transcript discovery and expression profiling Meissner et al. Nature 2008 epigenetic analysis (e.g. DNA methylation)

Data characteristics

Read length read length [bp] ~ (variable) (fixed) (fixed) (variable) 400

Error characteristics (Illumina)

Error characteristics (454)

Coverage bias ~2X read genome read coverage ~20X read genome read coverage

Genome re- sequencing

Complete human genomes

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

Read mapping

… is like a jigsaw puzzle … and they give you the picture on the box 2. Read mapping …you get the pieces… Big and Unique pieces are easier to place than others…

Challenge: non-uniqueness Reads from repeats cannot be uniquely mapped back to their true region of origin RepeatMasker does not capture all micro-repeats, i.e. repeats at the scale of the read length

Non-unique mapping

SE short-read alignments are error-prone 0.35%

Paired-end (PE) reads fragment length: 100 – 600bp Korbel et al. Science 2007 fragment length: 1 – 10kb

PE alignment statistics (simulated data) 0.00% 7.6% 0.09% 0.35% 0.03%

The MOSAIK read mapper/aligner Michael Strömberg

Gapped alignments

Aligning multiple read types together ABI/capillary 454 FLX 454 GS20 Illumina

SNP / short-INDEL discovery

Polymorphism detection sequencing errorpolymorphism

Allele calling in multi-individual data P(G 1 =aa|B 1 =aacc; B i =aaaac; B n = cccc) P(G 1 =cc|B 1 =aacc; B i =aaaac; B n = cccc) P(G 1 =ac|B 1 =aacc; Bi=aaaac; B n = cccc) P(G i =aa|B 1 =aacc; B i =aaaac; B n = cccc) P(G i =cc|B 1 =aacc; B i =aaaac; B n = cccc) P(G i =ac|B 1 =aacc; Bi=aaaac; B n = cccc) P(G n =aa|B 1 =aacc; B i =aaaac; B n = cccc) P(G n =cc|B 1 =aacc; B i =aaaac; B n = cccc) P(G n =ac|B 1 =aacc; Bi=aaaac; B n = cccc) P(SNP) “genotype probabilities” P(B 1 =aacc|G 1 =aa) P(B 1 =aacc|G 1 =cc) P(B 1 =aacc|G 1 =ac) P(B i =aaaac|G i =aa) P(B i =aaaac|G i =cc) P(B i =aaaac|G i =ac) P(B n =cccc|G n =aa) P(B n =cccc|G n =cc) P(B n =cccc|G n =ac) “genotype likelihoods” Prior(G 1,..,G i,.., G n ) -----a c a c-----

SNP calling in deep sample sets Population SamplesReads Allele detection

Capturing the allele in the samples

The ability to call rare alleles reads Q30Q40Q50Q aatgtagtaAgtacctac aatgtagtaCgtacctac aatgtagtaAgtacctac

Allele calling in 400 samples

Detecting de novo mutations the child inherits one chromosome from each parent there is a small probability for a de novo (germ-line or somatic) mutation in the child

Capture sequencing

Targeted mammalian re-sequencing Deep sequencing of complete human genomes is still too expensive There is a need to sequence target regions, typically genes, to follow up on GWAS studies Targeted re-sequencing with DNA fragment capture offers a potentially cost-effective alternative Solid phase or liquid phase capture 454 or Illumina sequencing Informatics pipeline must account for the peculiarities of capture data

On/off target capture ref allele*:45% non-ref allele*:54% Target region SNP (outside target region)

Reference allele bias (*) measured at 450 het HapMap 3 sites overlapping capture target regions in sample NA07346 ref allele*:54% non-ref allele*:45% ref allele*:54% non-ref allele*:45%

SNP example Amit Indap

Structural Variation discovery

Structural variations

SV/CNV detection – SNP chips Tiling arrays and SNP-chips made whole-genome CNV scans possible Probe density and placement limits resolution Balanced events cannot be detected

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

44 Read depth

Chromosome 2 Position [Mb] CNV events found using RD

PE read mapping positions

47 The SV/CNV “event display” Chip Stewart

Spanner – specificity

Data standards

Data types with standard formats SRF/FASTQ SAM/BAM GLF

Transcriptome sequencing

Data highly reproducible Michele Busby

Comparative data Michele Busby

Biological questions Michele Busby

Our software tools for next-gen data

Credits Elaine Mardis Andy Clark Aravinda Chakravarti Doug Smith Michael Egholm Scott Kahn Francisco de la Vega Patrice Milos John Thompson

Lab Several postdoc positions are available!

Mutational profiling

Chemical mutagenesis

Mutational profiling: deep 454/Illumina/SOLiD data Pichia stipitis converts xylose to ethanol (bio-fuel production) one mutagenized strain had high conversion efficiency determine which mutations caused this phenotype 15MB genome: 454, Illumina, and SOLiD reads 14 true point mutations in the entire genome Pichia stipitis reference sequence Image from JGI web site 10-15X genome coverage required