PhyloCommons: Sharing, Annotating and Reusing Phylogenies

<p>Abstract for presentation at the 2013 Bioinformatics Open Source Conference (BOSC), held July 19-20 in Berlin, Germany.</p

An Ontology-Based System for Querying Life in a Post-Taxonomic Age

<p>Grant proposal (project description and references cited) to the US National Science Foundation, Advances in Biological Informatics (ABI) program as Collaborative Research. Funded in 2015. Files include public abstract as submitted to NSF.  </p

Towards ubiquitous OWL computing: Simplifying programmatic authoring of and querying with OWL axioms

<p>Abstract for presentation at the 2014 Bioinformatics Open Source Conference (BOSC), held July 11-12 in Boston, MA.</p

Response to GBIF request for consultation on data licenses

<p>As a data aggregator, the goal of GBIF should be to find policies that benefit both its data providers and data reusers. Clearly, a GBIF that has no or few data will have little value, but so will a GBIF full of data that is encumbered with restrictions to an extent that stifles reuse. Our response follows from the proposition that promoting data reuse should be a shared interest of all the parties: data providers, data users, and GBIF itself. We feel the consultation document missed the opportunity to recognize this shared interest, and that furthering the goal of data reuse should in fact be a primary yardstick by which different licensing options are measured.</p> <p>In short, our recommendations are (1) that all data in GBIF be released under Creative Commons Zero (CC0), which is a public domain dedication that waives copyright rather than asserting it; (2) GBIF should set clear expectations in the form of community norms for how the data that it serves is to be referenced when reused, and (3) GBIF should work with partner organizations in promoting standards and technologies that enable the effective tracking of data reuse.</p

A grassroots approach to software sustainability

<p>A position paper from the National Evolutionary Synthesis Center to the Workshop on Sustainable Software in Science: Practice and Experience</p

The MIAPA ontology: An annotation ontology for validating minimum metadata reporting for phylogenetic analyses

<p>Abstract for a Lightning Talk presented at the 2013 iEvoBio conference, which took place June 25-26, 2013, in Snowbird, UT. The presented slides are at http://www.slideshare.net/hlapp/miapa-i-evobio-2013.</p

BioSQL Reloaded: 1.0 Release, PhyloDB Module, and Future Features

<p>Abstract for presentation at the 2008 Bioinformatics Open Source Conference (BOSC), held July 18-19 in Toronto, ON, Canada.</p

Machine reasoning about phenotypes: enhancing expert knowledge about the genetics of a fossil transition

<div>The Devonian era transition from aquatic fins to terrestrial limbs in tetrapodamorph vertebrates is well-studied in the fossil record, and the genes responsible for the complex suite of anatomical changes have been the topic of much speculation. A recent review by Mastick and Mabee (MM) found evidence for 162 different fin-limb candidate genes in the evo-devo literature. As a test case for the usefulness of machine reasoning about phenotypes, we asked to what extent would an expert system recover the same set of candidate genes using only knowledge about (i) the fin-limb phenotypes from the relevant fossil taxa and (ii) the phenotypes from perturbing individual genes, as catalogued by the relevant model organism (zebrafish, mouse, Xenopus) and human databases. We used the Phenoscape Knowledgebase (kb.phenoscape.org) to compute an information theoretic measure of semantic similarity between ontologically curated phenotypes as an indication of the strength of a candidate gene association. The distribution of phenotypic semantic similarity scores between fossil and gene phenotypes is significantly displaced upwards in the MM candidates relative to the non-candidates. To understand the reasons for genes that performed counter to expectation, we examine the clustering of candidates and non-candidates within protein interaction networks. Our results demonstrate the potential of machine reasoning to accurately rank the strength of evidence for candidate genes when presented with a large volume of descriptive phenotype information. This approach could in principle be used to replace, evaluate and/or enhance candidate gene hypotheses culled from the literature. <br></div