Bioinformatics for highthroughput DNA sequencing Gabor Marth Boston
Bioinformatics for high-throughput DNA sequencing Gabor Marth Boston College Biology New grad student orientation Boston College September 8, 2009
DNA sequence variations The Human Genome Project has determined a reference sequence of the human genome However, every individual is unique, and is different from others at millions of nucleotide locations
Why do we care about variations? underlie phenotypic differences cause inherited diseases allow tracking ancestral human history
Human genetic variation 4
The first “famous” genomes
Genome sequencing ~1 Mb ~100 Mb >100 Mb ~3, 000 Mb
New sequencing technologies…
Next-gen sequencing – a revolution 100 Gb Illumina/Solexa, AB/SOLi. D sequencers (10 -30 Gb in 25 -100 bp reads) bases per machine run 10 Gb 1 Gb Roche/454 pyrosequencer (100 -400 Mb in 200 -450 bp reads) 100 Mb 10 Mb ABI capillary sequencer 1 Mb 10 bp 100 bp read length 1, 000 bp
The re-sequencing informatics pipeline REF IND (ii) read mapping (iv) SV calling IND (iii) SNP and short INDEL calling (i) base calling (v) data viewing, hypothesis generation
Tools
Read mapping is like a jigsaw puzzle… …you get the pieces… 2. Read mapping … and they give you the picture on the box Big and Unique pieces are easier to place than others…
The MOSAIK read mapping program • Reads from repeats cannot be uniquely mapped back to their true region of origin Michael Strömberg (Wan-Ping Lee)
SNP discovery Marth et al. Nature Genetics 1999 Quinlan et al. in prep. (Amit Indap, Wen Fung Leong)
Structural variation discovery Navigation bar Fragment lengths in selected region Depth of coverage in selected region Stewart et al. in prep. (Deniz Kural, Jiantao Wu)
Sequence alignment viewers Huang et al. Genome Research 2008 (Derek Barnett)
Data mining
Mutational profiling in deep 454 data Pichia stipitis reference sequence Image from JGI web site • Pichia stipitis is a yeast that efficiently converts xylose to ethanol (bio-fuel production) • one specific mutagenized strain had especially high conversion efficiency • goal was to determine where the mutations were that caused this phenotype • we analyzed 10 runs (~3 million reads) of 454 reads (~20 x coverage of the 15 MB genome) • found 39 mutations • informatics analysis in < 24 hours (including manual checking of all candidates) Smith et al. Genome Research 2008
SNP calling in short-read coverage C. elegans reference genome (Bristol, N 2 strain) Bristol, N 2 strain (3 ½ machine runs) Pasadena, CB 4858 (1 ½ machine runs) • goal was to evaluate the Solexa/Illumina technology for the complete resequencing of large model-organism genomes • 5 runs (~120 million) Illumina reads were collected by Washington Univ. • we found 45, 000 SNP with very high validation rate SNP Hillier et al. Nature Methods 2008
Current focus
1000 Genomes Project • • • data quality assessment project design (# samples depth of read coverage) read mapping SNP calling structural variation discovery
SV discovery in autism deletion amplification
Lab
People
Resources • computer cluster (72 servers) • 128 GB RAM server • ~200 TB disk space • 2 R 01 grants (NHGRI/NIH) • 1 R 21 grant (NIAID/NIH) • a BC RIG grant • 2 RC 2 grants (NHGRI/NIH) starting September 2009
Collaborations Genome Canada Baylor HGSC Wash. U. GSC UC Davis UBC GSC UCSF UCLA Cornell NCBI @ NIH NCI @ NIH Marshfield Clinic Pfizer
Graduate student rotations • Looking for new graduate students • Spots are available for all three rotations • Lots or projects • Caveat: you need to be able to program… • Check us out at: http: //bioinformatics. bc. edu/marthlab/ • If you are interested, please talk to me
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