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Challenges in Computational & Functional GenomicsIgor Ulitsky
“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◦ Eithan Ruppin’s lab – focus on metabolism◦ Tal Pupko’s and Benny Chor’s labs – focus on
phylogeny◦ Roded Sharan’s lab – focus on networks◦ Noam Shomron’s lab – focus on miRNA◦ Eran Halperin’s lab – focus on genetics
Genomics
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
“Solved” 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
“Worked on” 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
“Future” problems
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
Using sequencing to find new antibiotics
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
Using sequencing to uncover structural variation
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.
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
Mutational landscape of human cancer
Predicting ncRNAs Using histone
modifications and sequence conservation to uncover long non-coding RNAs (lincRNA)
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
Using conservation to uncover regulatory elements