De novo assembly of transcriptomes and genomes
Adrian Reich (Wessel Lab, MCB)
Echinodermata is a diverse phylum that spans 500 million years of evolution but the vast majority of known genetic information within the phylum is from a single species of sea urchin, S. purpuratus. In the current study we have sequenced and assembled de novo transcriptomes of ovaries of twenty different species, representing all five families of echinoderms. The goals of this dataset are to identify orthologous genes, resolve the phylogenetic relationships of extant echinoderms, and identify rapidly evolving genes. In addition we have sequenced the genomes of two echinoderms and are attempting to assemble these de novo.
SOWHAT? The Swofford-Olsen-Waddell-Hillis (SOWH) Test of Topologies
Samuel Church (Dunn Lab, EEB)
The Swofford-Olsen-Waddell-Hillis (SOWH) test is a probabilistic evaluation of competing phylogenetic topologies. Using a parametric resampling approach, the SOWH test creates a null distribution of differences in likelihood values and tests the observed data against this distribution. This test is currently implemented using a complicated set of instructions which require manual manipulation of data. It also appears to be unreliable for some data sets. We present a program which automates the complicated steps of the SOWH test, along with a more reliable alternative called SOWHat.
Analysis of genomes for drug targets
Bob Campbell (Brown/MPPB and MBL)
The initial sequencing of parasite genomes revealed numerous homologs of human drug targets. This led to comparative genomics studies to comprehensively identify candidate drug targets from Neglected Tropical Disease pathogens, contributing to the creation of the WHO-sponsored TDR Targets Database. This also fostered projects to mobilize “old” medicinal chemistry from industry for "new" drug discovery against neglected diseases. Despite this progress, it has remained difficult to produce high-quality drug leads. This short talk will briefly review the work to date, and considerations for integrating bioinformatics approaches with other tools and knowledge to improve outcomes of parasite drug discovery programs.