The QTLdb accepts either curated public data from journal papers or private laboratory reports subject to publication. More than 50 parameters/data types are subject to collection to describe a QTL, as reported earlier (3 ). We have recently added a number of new data types to enhance our ability to be more inclusive in QTL/association data collection. These new types include ‘association’ data for candidate gene or single marker associations; ‘eQTL’ from microarray-based QTL scan analysis; ‘test scale’ to differentiate genome-wise, chromosome-wise, comparison-wise and experiment-wise QTL/association reports; ‘test model’ to indicate epistatic or maternally or paternally imprinted QTL; new test statistics such as Bayes value and likelihood ratio, etc. We have also added animal breed information for future breed-associated QTL analysis. The backbone maps to record QTL are from USDA-Meat Animal Research Center (MARC; for pigs and cattle), Wageningen University (for chicken), University of Melbourne (for sheep) and the National Center for Cool and Cold Water Aquaculture (NCCCWA; for rainbow trout). Reported QTL genome locations were obtained by interpolating their linkage map positions via anchor markers.
QTL are mapping features recorded as linkage distances. In order for GBrowse to display QTL and for users to easily port QTL data for customized analysis, we established a process to convert the QTL linkage map locations (centimorgan, cM) to the corresponding physical locations (megabase pair, Mbp). The data conversion is a mathematical process built in a Perl script, whereby interpolation or extrapolation is performed with reference to the nearest common anchoring marker locations on both maps.
The QTLdb has a three-tiered data curation structure so that curators, editors and database administrators can work together and share responsibilities in a workflow to ensure data quality and smooth process control. In the past few years, a set of new data debugging tools, process control mechanisms and functions for the ease of use of the tools have been developed in response to lessons learned during data curation and debugging.
QTL are mapping features recorded as linkage distances. In order for GBrowse to display QTL and for users to easily port QTL data for customized analysis, we established a process to convert the QTL linkage map locations (centimorgan, cM) to the corresponding physical locations (megabase pair, Mbp). The data conversion is a mathematical process built in a Perl script, whereby interpolation or extrapolation is performed with reference to the nearest common anchoring marker locations on both maps.
The QTLdb has a three-tiered data curation structure so that curators, editors and database administrators can work together and share responsibilities in a workflow to ensure data quality and smooth process control. In the past few years, a set of new data debugging tools, process control mechanisms and functions for the ease of use of the tools have been developed in response to lessons learned during data curation and debugging.
