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Challenges in Computational & Functional Genomics. Igor Ulitsky. 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

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genomics
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
solved problems
“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
worked on problems
“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
future problems
“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
using sequencing to find new antibiotics
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
using sequencing to uncover structural variation
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
mutational landscape of human cancer
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.
mutational landscape of human cancer1
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
predicting ncrnas
Predicting ncRNAs
  • Using histone modifications and sequence conservation to uncover long non-coding RNAs (lincRNA)
using conservation to uncover regulatory elements
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