<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
  <identifier identifierType="DOI">10.7923/41FB-MC86</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Wojahn, John Michael Adrian</creatorName>
      <givenName>John Michael Adrian</givenName>
      <familyName>Wojahn</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org">https://orcid.org/0000-0002-5060-0798</nameIdentifier>
    </creator>
    <creator>
      <creatorName nameType="Personal">Buerki, Sven</creatorName>
      <givenName>Sven</givenName>
      <familyName>Buerki</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org">https://orcid.org/0000-0002-8299-6539</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title xml:lang="en">G2PMineR</title>
  </titles>
  <publisher>GitHub</publisher>
  <publicationYear>2021</publicationYear>
  <resourceType resourceTypeGeneral="Other">R Package</resourceType>
  <subjects>
    <subject subjectScheme="Fields of Science and Technology (FOS)" schemeURI="http://www.oecd.org/science/inno" valueURI="http://www.oecd.org/science/inno/38235147.pdf">FOS: Computer and information sciences</subject>
    <subject subjectScheme="Fields of Science and Technology (FOS)" schemeURI="http://www.oecd.org/science/inno" valueURI="http://www.oecd.org/science/inno/38235147.pdf">FOS: Biological sciences</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Researcher">
      <contributorName nameType="Personal">Galla, Stephanie</contributorName>
      <givenName>Stephanie</givenName>
      <familyName>Galla</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org">https://orcid.org/0000-0002-4650-8067</nameIdentifier>
    </contributor>
    <contributor contributorType="ProjectMember">
      <contributorName nameType="Personal">Melton, Anthony</contributorName>
      <givenName>Anthony</givenName>
      <familyName>Melton</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="https://orcid.org">https://orcid.org/0000-0003-4882-6664</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/02e3zdp86" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Boise State University</affiliation>
    </contributor>
  </contributors>
  <language>en</language>
  <sizes/>
  <formats/>
  <version>0.1.0</version>
  <rightsList>
    <rights rightsURI="https://www.gnu.org/licenses/agpl.txt">GNU Affero General Public License v3.0</rights>
  </rightsList>
  <descriptions>
    <description xml:lang="en" descriptionType="Abstract">v0.1.0 release of the G2PMineR Package as described in Genes manuscript "G2PMineR: A Genome to Phenome Literature Review Approach" (Wojahn et al., 2021). For further information and details about the package release visit the G2PMineR Project Webpage.

There is a gap in the conceptual framework linking genes to phenotypes (G2P) for non-model organisms, as most non-model organisms do not yet have genomic resources readily available. To address this, researchers often perform literature reviews to understand G2P linkages by curating a list of likely gene candidates, hinging upon other studies already conducted in closely related systems. Sifting through hundreds to thousands of articles is a cumbersome task that slows down the scientific process and may introduce bias into a study. To fill this gap, we created G2PMineR, a free and open-source literature mining tool developed specifically for G2P research. This package uses automation to make the G2P review process efficient and unbiased, while also generating hypothesized associations between genes and phenotypes within a taxonomical framework. We applied the package to a literature review for drought-tolerance in plants. The analysis provides biologically meaningful the results within the known framework of drought tolerance in plants. Overall, the package is useful for conducting literature reviews for genome-to-phenome projects and also has broad appeal to scientists investigating a wide range of study systems as it can conduct analyses under the auspices of three different kingdoms (Plantae, Animalia, and Fungi).</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>National Science Foundation</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/100000001</funderIdentifier>
      <awardNumber awardURI="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757324">OIA-1757324</awardNumber>
      <awardTitle>RII Track-1: Linking Genome to Phenome to Predict Adaptive Responses of Organisms to Changing Landscapes</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>