Browsing by Subject "functional genomics"

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    Characterization of Tissue-Specific Functional Networks and Genome-Wide Association Study Genes
    (2016-01) Kuriger-Laber, Jacquelyn
    Present-day biological research has generated a vast body of data related to variation in the human genome, but in many cases the biological role of this variation is unknown or only partially understood. In order to integrate the diverse body of experimental genetic and genomic data, systems biologists pioneered computational approaches to infer functional networks. These networks provide a powerful platform to investigate genomic findings at a functional level. Recently, systems biologists designed a second generation of functional networks that reflect tissue-specificity in gene functional interactions. We examine both characteristics of these tissue-specific functional networks and the topology of genome-wide association study (GWAS) variant-related genes in these networks. We find significant variation in network quality and suggest metrics to identify well-performing networks. Finally, we show GWAS trait-associated genes have non-random topology in tissue-specific networks and that this must be taken into account when applying network-enabled methods to genomic data.
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    Molecular multiplexing methods for genome-scale measurements.
    (2018-06) Palani, Nagendra Prasad
    I present the utility of unique DNA barcodes to tag distinct genotypes and subsequently link them to phenotypes. Such molecular tagging allowed us to perform multiplexed phenotype analysis of thousands of genotypes using next-generation sequencing (NGS) technologies. Four projects are discussed in this thesis - 1) 2D Tn-Seq, a massively multiplexed experimental approach to interrogate genetic interactions of a microbe at the genome scale 2) pFluxSeq, a molecular tool that will enable peptide-based 13C metabolic flux analysis (MFA) of a mixed population of microbial cells 3) Deep mutational scanning of phenotype arrays and 4) Barcode-based lineage tracking to measure CRISPR/dCas9 RNA interference efficacy in bacteria.