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The GENCODE pseudogene resource

Baikang Pei1, Cristina Sisu12, Adam Frankish3, Cédric Howald4, Lukas Habegger1, Xinmeng Jasmine Mu1, Rachel Harte5, Suganthi Balasubramanian12, Andrea Tanzer6, Mark Diekhans5, Alexandre Reymond4, Tim J Hubbard3, Jennifer Harrow3 and Mark B Gerstein127*

Author Affiliations

1 Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA

2 Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA

3 Wellcome Trust Sanger Institute, Welcome Trust Campus, Hinxton, Cambridge CB10 1SA, UK

4 Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne, 1015, Switzerland

5 Center for Biomolecular Science and Engineering University of California, 1156 High Street, Santa Cruz, CA 95064, USA

6 Centre for Genomic Regulation (CRG) and UPF, Dr, Aiguader, 08003 Barcelona, Catalonia, Spain

7 Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA

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Genome Biology 2012, 13:R51  doi:10.1186/gb-2012-13-9-r51

Published: 5 September 2012

Abstract

Background

Pseudogenes have long been considered as nonfunctional genomic sequences. However, recent evidence suggests that many of them might have some form of biological activity, and the possibility of functionality has increased interest in their accurate annotation and integration with functional genomics data.

Results

As part of the GENCODE annotation of the human genome, we present the first genome-wide pseudogene assignment for protein-coding genes, based on both large-scale manual annotation and in silico pipelines. A key aspect of this coupled approach is that it allows us to identify pseudogenes in an unbiased fashion as well as untangle complex events through manual evaluation. We integrate the pseudogene annotations with the extensive ENCODE functional genomics information. In particular, we determine the expression level, transcription-factor and RNA polymerase II binding, and chromatin marks associated with each pseudogene. Based on their distribution, we develop simple statistical models for each type of activity, which we validate with large-scale RT-PCR-Seq experiments. Finally, we compare our pseudogenes with conservation and variation data from primate alignments and the 1000 Genomes project, producing lists of pseudogenes potentially under selection.

Conclusions

At one extreme, some pseudogenes possess conventional characteristics of functionality; these may represent genes that have recently died. On the other hand, we find interesting patterns of partial activity, which may suggest that dead genes are being resurrected as functioning non-coding RNAs. The activity data of each pseudogene are stored in an associated resource, psiDR, which will be useful for the initial identification of potentially functional pseudogenes.