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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach
Genome Research, Volume 23, No. 11, Year 2013
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Description
The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites. © 2013 Meyer et al.
Authors & Co-Authors
Meyer, Pablo
United States, Yorktown Heights
Ibm Thomas J. Watson Research Center
Siwo, Geoffrey Henry
United States, Notre Dame
University of Notre Dame
Stolovitzky, Gustavo A.
United States, Yorktown Heights
Ibm Thomas J. Watson Research Center
Tan, Asako
United States, Notre Dame
University of Notre Dame
Emrich, Scott J.
United States, Notre Dame
University of Notre Dame
Ferdig, Michael T.
United States, Notre Dame
University of Notre Dame
Chen, Mei Ju May
Taiwan, Nankang
Academia Sinica Taiwan
Dréos, Renè
Switzerland, Epalinges
Schweizerisches Institut Für Experimentelle Krebsforschung
Bucher, Philipp
Switzerland, Epalinges
Schweizerisches Institut Für Experimentelle Krebsforschung
Saeys, Yvan
Belgium, Ghent
Vlaams Instituut Voor Biotechnologie
Belgium, Ghent
Universiteit Gent
Vingron, Martin
Germany, Berlin
Max Planck Institute for Molecular Genetics
Knisley, Debra J.
United States, Johnson
East Tennessee State University
Kursa, Miron Bartosz
Poland, Warsaw
University of Warsaw
Sillanpää, Mikko J.
Finland, Oulu
Oulun Yliopisto
Meysman, Pieter
Belgium, Leuven
Ku Leuven
Belgium, Antwerpen
Universiteit Antwerpen
Engelen, Kristof
Belgium, Leuven
Ku Leuven
Italy, San Michele All'adige
Fondazione Edmund Mach
Marchal, Kathleen
Belgium, Ghent
Vlaams Instituut Voor Biotechnologie
Belgium, Leuven
Ku Leuven
Statistics
Citations: 13
Authors: 17
Affiliations: 23
Identifiers
Doi:
10.1101/gr.157420.113
ISSN:
10889051
Research Areas
Cancer
Genetics And Genomics