Creating a honey bee consensus gene set

By Christine G. Elsik, Aaron J. Mackey, Justin T. Reese, Natalia Milshina, David S. Roos, George M. Weinstock. Genome Biology, 2007

340 citations recorded by the source corpus.

BackgroundWe wished to produce a single reference gene set for honey bee (Apis mellifera). Our motivation was twofold. First, we wished to obtain an improved set of gene models with increased coverage of known genes, while maintaining gene model quality. Second, we wished to provide a single official gene list that the research community could further utilize for consistent and comparable analyses and functional annotation.ResultsWe created a consensus gene set for honey bee (Apis mellifera) using GLEAN, a new algorithm that uses latent class analysis to automatically combine disparate gene prediction evidence in the absence of known genes. The consensus gene models had increased representation of honey bee genes without sacrificing quality compared with any one of the input gene predictions. When compared with manually annotated gold standards, the consensus set of gene models was similar or superior in quality to each of the input sets.ConclusionMost eukaryotic genome projects produce multiple gene sets because of the variety of gene prediction programs. Each of the gene prediction programs has strengths and weaknesses, and so the multiplicity of gene sets offers users a more comprehensive collection of genes to use than is available from a single program. On the other hand, the availability of multiple gene sets is also a cause for uncertainty among users as regards which set they should use. GLEAN proved to be an effective method to combine gene lists into a single reference set.

Subfields: Genetics, Insect Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ce326339290011ab801b828b55a7daa47354f866

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Citations in the corpus

Assessing Performance of Orthology Detection Strategies Applied to Eukaryotic Genomes (2007, methodology): “In computational biology, we have exploited LCA as a gene model combiner, integrating diverse lines of evidence to significantly improve eukaryotic gene model predictions [38].”
Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments (2007, methodology): “These tools include Combiner [34], JIGSAW [35], GLEAN [36], and Exo-gean [37], among others.”
The genome of the cucumber, Cucumis sativus L. (2009, methodology): “Our strategy for gene prediction was to conduct de novo predictions on the repeat-masked genome and then integrate them with spliced alignments of proteins and transcripts to genome sequences using GLEA”
A beginner's guide to eukaryotic genome annotation (2012, background): “…is the first of its taxonomic order or family to be annotated, it would definitely be preferable to use a pipeline that can use the full repertory of external evidence, especially RNA-seq data, to inform its gene annotations; not doing so will almost certainly result in low-quality annotations 80 .”
The genome of the pear (Pyrus bretschneideri Rehd.) (2013, methodology): “Then predicted transcripts were used to complement the GLEAN gene set or were integrated as isoforms.”