Towards precise classification of cancers based on robust gene functional expression profiles |
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Authors: | Zheng Guo Tianwen Zhang Xia Li Qi Wang Jianzhen Xu Hui Yu Jing Zhu Haiyun Wang Chenguang Wang Eric J Topol Qing Wang Shaoqi Rao |
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Institution: | 1. Research Group in Biomedical Informatics, Institut Municipal d'Investigacio Medica/Universitat Pompeu Fabra/Centre de Regulacio Genomica, E08003, Barcelona, Catalonia, Spain 2. Department of Genetic Medicine and Development, University of Geneva, Medical School and University Hospital of Geneva, CMU, 1, rue Michel Servet, 1211, Geneva, Switzerland 3. Center for Integrative Genomics, University of Lausanne, 1015, Lausanne, Switzerland 4. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK 5. Laboratory for Computational Genomics and Department of Computer Science, Washington University, One Brookings Drive, Campus Box 1045, St Louis, Missouri, 63130, USA 6. EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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Abstract: | Background Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method. Results We performed experimental evaluation by RT-PCR of three different computational gene finders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram was computed and each component of it was evaluated. The results showed that de novo comparative methods can identify up to about 700 chicken genes with no previous evidence of expression, and can correctly extend about 40% of homology-based predictions at the 5' end. Conclusions De novo comparative gene prediction followed by experimental verification is effective at enhancing the annotation of the newly sequenced genomes provided by standard homology-based methods. |
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