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  <title><![CDATA[ Annotating the 'Dark Matter' of Bacterial Genomes Using Hybrid Experimental/Computational Approaches]]></title>
  <body><![CDATA[<p><strong>Peter Freddolino, </strong><strong>Ph.D</strong><strong>.<br />
Department of Biological Chemistry<br />
Department of Computational Medicine and Bioinformatics<br />
University of Michigan</strong></p>

<p><strong>Abstract</strong><br />
Recent advances in high-throughput sequencing technology have yielded a huge increase in our knowledge of genomic sequences, but DNA sequence&nbsp;information remains meaningless without corresponding functional insight. It is only through a synthesis of computational approaches and high-throughput experiments that any meaningful headway can be made in the task of moving from genome sequence information to functional information at&nbsp;the scales of modern biology.We have recently launched two such initiatives, aimed at completely mapping the transcriptional regulatory logic and&nbsp;functional proteome of&nbsp;Escherichia coli. Using a broadly applicable non-specific method for mapping genome-wide protein occupancy, we have begun to&nbsp;identify the binding motifs, functions, and condition-dependent behavior of many cryptic&nbsp;E. coli&nbsp;transcription factors. In the process, we have also&nbsp;identified the presence of heterochromatin-like silenced regions on bacterial chromosomes, which we have found play a key role in regulating stress-response and virulence genes across several bacterial species. To address the problem of assigning functions to poorly annotated proteins without&nbsp;suitably close homologs for sequence-based annotation methods to be effective, we have recently developed a hybrid pipeline combining structural&nbsp;prediction/alignment, sequence alignment, and protein-protein interaction information to obtain combined structure predictions and functional annotations&nbsp;for entire proteomes. We find that our inclusion of structural information makes our workflow unusually strong in performance on difficult targets with&nbsp;limited sequence identity to annotated proteins. Application of our methods at the scale of entire proteomes yields a rich&nbsp;new source of information to seed&nbsp;detailed investigation of the functions of many previously mysterious protein-coding genes.</p>

<p><a href="http://freddolino-lab.med.umich.edu/">More about the Freddolino Lab</a></p>
]]></body>
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      <value><![CDATA[<p><a href="mailto:jasmine.martin@biosci.gatech.edu">Jasmine Martin</a></p>
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