Gramene 2018: unifying comparative genomics and pathway resources for plant research.

Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species.

Gramene Database: Navigating Plant Comparative Genomics Resources.

Gramene (http://www.gramene.org) is an online, open source, curated resource for plant comparative genomics and pathway analysis designed to support researchers working in plant genomics, breeding, evolutionary biology, system biology, and metabolic engineering.

Gramene 2016: comparative plant genomics and pathway resources.

Gramene (http://www.gramene.org) is an online resource for comparative functional genomics in crops and model plant species.

Diverse inhibitor chemotypes targeting Trypanosoma cruzi CYP51.

Background: Chagas Disease, a WHO- and NIH-designated neglected tropical disease, is endemic in Latin America and an emerging infection in North America and Europe as a result of population moves. Although a major cause of morbidity and mortality due to heart failure, as well as inflicting a heavy economic burden in affected regions, Chagas Disease elicits scant notice from the pharmaceutical industry because of adverse economic incentives. The discovery and development of new routes to chemotherapy for Chagas Disease is a clear priority.

Analysis of high-throughput screening assays using cluster enrichment.

In this paper, we describe the implementation and evaluation of a cluster-based enrichment strategy to call hits from a high-throughput screen using a typical cell-based assay of 160,000 chemical compounds. Our focus is on statistical properties of the prospective design choices throughout the analysis, including how to choose the number of clusters for optimal power, the choice of test statistic, the significance thresholds for clusters and the activity threshold for candidate hits, how to rank selected hits for carry-forward to the confirmation screen, and how to identify confirmed hits in a data-driven manner. Whereas previously the literature has focused on choice of test statistic or chemical descriptors, our studies suggest that cluster size is the more important design choice.