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GersteinLab Overview

This overview discusses the use of big data analysis and mining techniques in genomics research, including genome structural variation, human genome annotation, and disease genomics. It also explores the application of network analysis in gene and protein pathways, as well as the simulation and modeling of macromolecular structures and motions.

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GersteinLab Overview

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  1. GersteinLab.org Overview Big Data Analysis & Mining • Biological Knowledge Representation, Literature Mining, Genomic Privacy • Genome Structural Variation &Personal Genomics • Human Genome Annotation & Disease Genomics • Networks of Genes& Protein Pathways • Macromolecular Structures & Motions Simul-ation &Modeling

  2. [Nat. Rev. Genet. (2010) 11: 559] [Science 330:6012] Annotating the Human Genome:Comparative & Functional

  3. Recasting Genome Annotation as Networks:Comparing Proximal & Distal Networks [ Gerstein et al. Nature (in press, '12) ]

  4. 1. Paired ends Methods toFind Variants (SVs) in Personal Genomes Deletion Reference Mapping Genome Reference Sequenced paired-ends 2. Split read 3. Read depth Deletion Deletion Reference Reference Genome Genome Read Reads Mapping Mapping Read count Reference Zero level [Snyder et al. Genes & Dev. ('10)]

  5. Identification of non-coding candidate drivers amongst somatic variants, using genome annotation & patterns of natural variation [Khurana et al., Science (‘13)]

  6. Biological Data Science:Protecting Genomic Privacy from linking attacks [Harmanciet al. Nat. Meth. (in revision)]

  7. Predicting Allosterically-Important Residues at the Surfacevia simulation, to characterize deleterious variants PDB: 3PFK Adapted from Clarke*, Sethi*, et al (2016) Structure

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