Computational Biology and Genomics at Boston College Biology Gabor T. Marth Department of Biology, Boston College

Computational research labs Prof. Peter Clote RNA secondary structure and energy landscape Protein motif recognition Prof. Jeffrey Cheung Human mutation landscape Regulatory networks Prof. Gabor Marth Genetic polymorphism discovery Population Genetics Medical Genetics

Resources CLAVIUS – a multi-CPU UNIX computer cluster UNIX development servers A teaching laboratory equipped with PC laptop computers running LINUX over VMWARE A professional new server room under construction

The CompBio teaching program Currently part of the Biology graduate program (PhD only) We have 2 Bioinformatics graduate students with a larger class expected for Fall 2006 Curriculum combines Biology, Computer Science, Math and Statistics courses We are working towards an inter-departmental Bioinformatics / Computational Biology PhD program

The Computational Genetics Lab

Sequence variations (polymorphisms) 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 sequence polymorphisms

Why are sequence variations important? cause inherited diseases allow tracking ancestral human history source of phenotypic difference

1. Polymorphism discovery tools Polymorphism discovery in clonal sequences Homozygous T Homozygous C Heterozygous C/T Automated detection of somatic mutations in diploid individual samples Marth et al. Nature Genetics 1999

2. Mining genetic variation data Cataloguing all naturally occurring normal sequence polymorphisms Marth et al. Nature Genetics 2001

Genetic and epigenetic changes in cancer DNA methilation, histone modification copy number changes, chromosomal rearrangements nucleotide changes, short insertions / deletions

3. Demographic inference

1. marker density (MD): distribution of number of SNPs in pairs of sequences Data – statistical distributions “rare” “common” 2. allele frequency spectrum (AFS): distribution of SNPs according to allele frequency in a set of samples Clone 1 Clone 2# SNPs AL00675AL AS81034AK CB00341AL SNPMinor alleleAllele count A/GA1 C/TT9 A/GG3

Models – mathematical and simulation past present stationaryexpansioncollapse MD (simulation) AFS (direct form) history bottleneck Marth et al. PNAS 2003

Conclusions based on model fitting European data African data bottleneck modest but uninterrupted expansion Marth et al. Genetics 2004

4. Medical Genetics The polymorphism structure of individuals follow strong patterns

3. An international project is under way to map out human polymorphism structure… However, the variation structure observed in the reference DNA samples… … often does not match the structure in another set of samples such as those used in a clinical case-control association study to find disease genes and disease- causing genetic variants

… we build computational tools to test sample- to-sample variability for clinical studies Instead of genotyping additional sets of (clinical) samples with costly experimentation, and comparing the variation structure of these consecutive sets directly… … we generate additional samples with computational means, based on our Population Genetic models of demographic history. We then use these samples to test the efficacy of gene-mapping approaches for clinical research.

5. We develop methods to connect genotype and clinical outcome in simple gene systems clinical endpoint (adverse drug reaction) computational prediction based on haplotype structure genetic marker (haplotype) in genome regions of drug metabolizing enzyme (DME) genes functional allele (known metabolic polymorphism) molecular phenotype (drug concentration measured in blood plasma)