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Gavin Sherlock

Researcher at Stanford University

Publications -  177
Citations -  98574

Gavin Sherlock is an academic researcher from Stanford University. The author has contributed to research in topics: Gene & Population. The author has an hindex of 71, co-authored 164 publications receiving 88897 citations. Previous affiliations of Gavin Sherlock include University of Southern California & University of California, Berkeley.

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The Aspergillus Genome Database, a curated comparative genomics resource for gene, protein and sequence information for the Aspergillus research community

TL;DR: The AspGD combines high-quality manual curation of the experimental scientific literature examining the genetics and molecular biology of Aspergilli, cutting-edge comparative genomics approaches to iteratively refine and improve structural gene annotations across multiple As pergillus species, and web-based research tools for accessing and exploring the data.
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Implementation of GenePattern within the Stanford Microarray Database

TL;DR: The GenePattern software package is incorporated directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD.
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The Candida Genome Database (CGD), a community resource for Candida albicans gene and protein information

TL;DR: The Candida Genome Database (CGD) is a new database that contains genomic information about the opportunistic fungal pathogen Candida albicans and provides community resources, including a reservation system for gene names and a colleague registry through which Candida researchers can share contact information and research interests.
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TB database 2010: overview and update.

TL;DR: An overview of TBDB is provided, focusing on the recent release of a Global Genetic Diversity dataset for TB, support for short-read re-sequencing data, new tools for exploring gene expression data in the context of gene regulation, and the integration of a metabolic network reconstruction and BioCyc with TBDB.
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The Longhorn Array Database (LAD): an open-source, MIAME compliant implementation of the Stanford Microarray Database (SMD).

TL;DR: The development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data.