Challenges in Computational & Functional Genomics

1. Challenges in Computational & Functional Genomics Igor Ulitsky

2. Genomics • “the branch of genetics that studies organisms in terms of their genomes (their full DNA sequences)” • Computational genomics in TAU • Ron Shamir’s lab – focus on gene expression and regulatory networks • EithanRuppin’s lab – focus on metabolism • Tal Pupko’s and Benny Chor’s labs – focus on phylogeny • RodedSharan’s lab – focus on networks • Noam Shomron’s lab – focus on miRNA • EranHalperin’s lab – focus on genetics

3. “Solved” problems • Alignment • Protein coding gene finding • Assembly of long reads • Basic microarray data analysis • Mapping of transcriptional regulation in simple organisms • Functional profiling in simple organisms

4. “Worked on” problems • Determining protein abundance • Assembly of short reads • Transcriptional regulation in higher eukaryotes • “Histone code”: Chromatin modifications, their function and regulation • Functional profiling of mammalian cells • Association studies for single-gene effects • Construction and modeling of synthetic circuits

5. “Future” problems • Digital gene expression from RNA-seq studies • Prediction of ncRNAs and their function • Global mapping of alternative splicing regulation • Integration of multi-level signaling (TFs, miRNA, chromatin) • Association studies for combinations of alleles

6. Using sequencing to find new antibiotics • All microbial genomes are sequenced in E. coli • Each sequencing efforts basically introduces genes (3-8Kb fragments) into E. coli • Sometimes sequencing fails • Idea: sequencing fails  barrier to horizontal gene transfer

8. Using sequencing to uncover structural variation • Even sequencing of reads with 100s of bp will no identify many indels • Idea: sequence pairs of sequences at some distance apart from each other

10. Mutational landscape of human cancer • High-throughput sequencing can identify all the mutations in different cancers • 20,857 transcripts from 18,191 human genes sequenced in 11 breast and 11 colorectal cancers.

11. Mutational landscape of human cancer • Problems: few mutations are drivers most are passangers • Most studies did not identify high frequent risk allels • But: members of some pathways are affected in almost any tumour • Network biology needed

12. Predicting ncRNAs • Using histone modifications and sequence conservation to uncover long non-coding RNAs (lincRNA)

13. Using conservation to uncover regulatory elements • 12 fly species were sequenced to identify • Evolution of genes and chromosome • Evolutionary constrained sequence elements in promoters and 3’ UTRs • Starting point – genome-wide alignment of the genomes