Bioinformatics for next-generation DNA sequencing

Gabor T. MarthBoston College Biology Department

BC Biology new graduate student orientationSeptember 2, 2008

Genetic code (DNA)

AGCGTGGTAGCGCGAGTTTGCGAGCTAGCTAGGCTCCGGATGCGACCAGCTTTGATAGATGAATATAGTGTGCGCGACTAGCTGTGTGTTGAATATATAGTGTGTCTCTCGATATGTAGTCTGGATCTAGTGTTGGTGTAGATGGAGATCGCGTAGCGTGGTAGCGCGAGTTTGCGAGCTAGCTAGGCTCCGGATGCGACCAGCTTTGATAGATGAATATAGTGTGCGCGACTAGCTGTGTGTTGAATATATAGTGTGTCTCTCGATATGTAGTCTGGATCTAGTGTTGGTGTAGATGGAGATCGCGTGCTTGAGTCGTTCGTTTTTTTATGCTGATGATATAAATATATAGTGTTGGTGGGGGGTACTCTACTCTCTCTAGAGAGAGCCTCTCAAAAAAAAAGCTCGGGGATCGGGTTCGAAGAAGTGAGATGTACGCGCTAGXTAGTATATCTCTTTCTCTGTCGTGCTGCTTGAGATCGTTCGTTTTTTTATGCTGATGATATAAATATATAGTGTTGGTGGGGGGTACTCTACTCTCTCTAGAGAGAGCCTCTCAAAAAAAAAGCTCGGGGATCGGGTTCGAAGAAGTGAGATGTACGCGCTAGXTAGTATATCTCTTTCTCTGTCGTGCT

The genome

Genome sequencing

~1 Mb ~100 Mb >100 Mb ~3,000 Mb

Next-generation sequencing machines

read length

base

s p

er

mach

ine r

un

10 bp 1,000 bp100 bp

100 Mb

10 Mb

1Mb

1Gb

Illumina, AB/SOLiD short-read sequencers

ABI capillary sequencer

454 pyrosequencer(20-100 Mb in 100-250 bp reads)

(1Gb in 25-50 bp reads)

Individual human resequencing

Variations at every scale of genome organization

Single-base substitutions (SNPs) Insertion-deletion polymorphisms

Structural variations including large-scale chromosomal rearrangements

Epigenetic variations (e.g. changes in methylation / chromatic structure)

We care about genetic variations because…

… they underlie phenotypic differences

… cause heritable diseases and determine responses to drugs

… allow tracking ancestral human history

Individual resequencing / SNP discovery

(iv) read assembly

REF

(iii) read mapping

IND

(i) base calling

IND(v) SNP and short INDEL calling

(ii) micro-repeat analysis

(vii) data validation, hypothesis generation

Tools

The variation discovery “toolbox”

• base callers

• read mappers

• SNP callers

• SV callers

• assembly viewers

GigaBayesGigaBayes

Base calling

Quinlan et al.Nature Methods 2008

… and they give you the picture on the box

Read mapping

Read mapping is like doing a jigsaw puzzle…

…you get the pieces…

Problem is, some pieces are easier to place than others…

Read mapping

Michael Strombergin prep.

SNP discovery

GigaBayesGigaBayes

Marth et al. Nature Genetics 1999Quinlan et al. in prep.

Structural variation discovery

Navigation bar

Fragment lengths in selected region

Depth of coverage in selected region

Stewart et al. in prep.

Assembly viewers

Huang and MarthGenome Research 2008

Data mining

SNP calling in single-read 454 coverage

• collaborative project with Andy Clark (Cornell) and Elaine Mardis (Wash. U.)• goal was to assess polymorphism rates between 10 different African and American melanogaster isolates• 10 runs of 454 reads (~300,000 reads per isolate) were collected

DNA courtesy of Chuck Langley, UC Davis

Mutational profiling in deep 454 data

• collaboration with Doug Smith at Agencourt

• 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 (~20x coverage of the

15MB genome)

Pichia stipitis reference sequence

• processed the sequences with our 454 pipeline

• found 39 mutations (in as many reads in which we found 650K SNP in

melanogaster)

• informatics analysis in < 24 hours (including manual checking of all

candidates)

Image from JGI web site

Smith et al. Genome Research 2008

SNP calling in short-read coverage

C. elegans reference genome (Bristol, N2 strain)

Pasadena, CB4858(1 ½ machine runs)

Bristol, N2 strain(3 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 from the Wash. U. Genome Center, as part of a collaborative project lead by Elaine Mardis, at Washington University

SNP

• we found 45,000 SNP with very high validation rate

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

Transcriptome sequencing

(from: Mortazavi et al. Nature Methods 2008)

Lab

The team

Derek BarnettEric Tsung

Aaron QuinlanDamien Croteau-Chonka

Weichun Huang

Michael Stromberg

Chip Stewart

Michele Busby

Resources

• computer cluster• 128 GB RAM server• 20TB disk space

• 2 large R01 grants from the NIH• a BC RIG grant

Collaborations

Baylor HGSC

Wash. U. GSC

Genome Canada

UBC GSC

Cornell

UC Davis UCSF

NCBI @ NIH NCI @ NIH Marshfield Clinic

UCLA

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