Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Software

Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis

Bahar Yilmazel1, Yanhui Hu1, Frederic Sigoillot2, Jennifer A Smith3, Caroline E Shamu3, Norbert Perrimon14 and Stephanie E Mohr1*

Author Affiliations

1 Drosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

2 Novartis Institutes for Biomedical Research, Developmental and Molecular Pathways, 250 Massachusetts Avenue, Cambridge, MA 02139, USA

3 ICCB-Longwood Screening Facility, Harvard Medical School, 250 Longwood Ave, Boston, MA 02115, USA

4 Howard Hughes Medical Institute, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

For all author emails, please log on.

BMC Bioinformatics 2014, 15:192  doi:10.1186/1471-2105-15-192

Published: 17 June 2014

Abstract

Background

RNA interference (RNAi) is an effective and important tool used to study gene function. For large-scale screens, RNAi is used to systematically down-regulate genes of interest and analyze their roles in a biological process. However, RNAi is associated with off-target effects (OTEs), including microRNA (miRNA)-like OTEs. The contribution of reagent-specific OTEs to RNAi screen data sets can be significant. In addition, the post-screen validation process is time and labor intensive. Thus, the availability of robust approaches to identify candidate off-targeted transcripts would be beneficial.

Results

Significant efforts have been made to eliminate false positive results attributable to sequence-specific OTEs associated with RNAi. These approaches have included improved algorithms for RNAi reagent design, incorporation of chemical modifications into siRNAs, and the use of various bioinformatics strategies to identify possible OTEs in screen results. Genome-wide Enrichment of Seed Sequence matches (GESS) was developed to identify potential off-targeted transcripts in large-scale screen data by seed-region analysis. Here, we introduce a user-friendly web application that provides researchers a relatively quick and easy way to perform GESS analysis on data from human or mouse cell-based screens using short interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), as well as for Drosophila screens using shRNAs. Online GESS relies on up-to-date transcript sequence annotations for human and mouse genes extracted from NCBI Reference Sequence (RefSeq) and Drosophila genes from FlyBase. The tool also accommodates analysis with user-provided reference sequence files.

Conclusion

Online GESS provides a straightforward user interface for genome-wide seed region analysis for human, mouse and Drosophila RNAi screen data. With the tool, users can either use a built-in database or provide a database of transcripts for analysis. This makes it possible to analyze RNAi data from any organism for which the user can provide transcript sequences.

Keywords:
RNAi; Off-target effects; Data analysis; Seed region; miRNA; siRNA; shRNA; High-throughput screening