This article is part of the supplement: Quantitative inference of gene function from diverse large-scale datasets
Research
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
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* Corresponding authors: Timothy R Hughes t.hughes@utoronto.ca - Frederick P Roth fritz_roth@hms.harvard.edu
1 Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S3E1, Canada
2 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
3 Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
4 Department of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, 500-712 Republic of Korea
5 Digital Biology Laboratory, Computer Science Department and Christopher S Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
6 ISI Foundation, Torino, 10133, Italy
7 Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
8 Department of Electrical and Computer Engineering, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
9 Department of Statistics, UC Berkeley, Berkeley, CA 94720-3860, USA
10 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
11 Department of Computer Science, University of Toronto, Toronto, ON M5S3G4, Canada
12 Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA 92093-0407, USA
13 Department of Genome Sciences, University of Washington, Seattle, WA 98195-5065, USA
14 Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
15 Bioinformatics and Computational Biology, The Jackson Laboratory, Bar Harbor, ME 04609, USA
16 Gatsby Computational Neuroscience Unit, London, WC1N 3AR, UK
17 School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PRC
18 Department of Electrical Engineering and Computer Science, and Department of Statistics, UC Berkeley, Berkeley, CA 94720-1776, USA
19 Department of Genome Sciences, and Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
20 Banting and Best Department of Medical Research, University of Toronto, Toronto, ON M5S 3E1, Canada
21 Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
22 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
23 Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
Genome Biology 2008, 9(Suppl 1):S2 doi:10.1186/gb-2008-9-s1-s2
Published: 27 June 2008Additional files
Additional data file 1:
Bar graphs of pairwise comparisons of AUC within each evaluation category.
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Additional data file 2:
Bar graphs of mean P20R values within each evaluation category
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Additional data file 3:
Bar graphs comparing properties of GO annotations in the held-out gene set, in the newly annotated gene set and in the training set.
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Additional data file 4:
Clustergram indicating Pearson correlation coefficients of the P20R performance measure among different submissions.
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Additional data file 5:
Heatmaps of precision at several recall values evaluated using held-out annotations on all GO terms within each of the 12 evaluation categories for each submission.
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Additional data file 6:
Heatmap of median precision at several recall values evaluated using held-out annotations within each of the 12 evaluation categories per submission
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Additional data file 7:
Performance measures for the initial round of GO term predictions within each evaluation category evaluated using held-out genes.
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Additional data file 8:
Performance measures for the initial round of GO term predictions within each evaluation category evaluated using the newly annotated genes (prospective evaluation).
Format: XLS Size: 122KB Download file
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Additional data file 9:
Performance measures for the second round of GO term predictions within each evaluation category evaluated using held-out genes.
Format: XLS Size: 110KB Download file
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Additional data file 10:
Performance measures for the second round of GO term predictions within each evaluation category evaluated using the newly annotated genes (prospective evaluation).
Format: XLS Size: 118KB Download file
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Additional data file 11:
Results of the analysis of variance in prediction performance.
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Additional data file 12:
Performance and variance on five subsets of the training data.
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Additional data file 13:
Performance measures of the unified predictions for each GO term.
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Additional data file 14:
High-scoring predictions evaluated against existing literature.
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Additional data file 15:
Mitochondrial part predictions with data from a previous study [38].
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Additional data file 16:
Data underlying Figure 6.
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Additional data file 17:
Data underlying Figure 7.
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Additional data file 18:
Performance measures for various individual evidence sources within each evaluation category evaluated using held-out genes.
Format: XLS Size: 146KB Download file
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Fraction of GO terms with higher precision and recall than a given precision/recall point for the unified predictions.
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Additional data file 20:
Description of the function prediction method used in each submission.
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Additional data file 21:
Detailed description of the submission methods and the straw man classifier.
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