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Open Access Methodology article

AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

Fahim Mohammad14, Robert M Flight2, Benjamin J Harrison23, Jeffrey C Petruska23 and Eric C Rouchka1*

Author Affiliations

1 Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, 40292, USA

2 Department of Anatomical Sciences & Neurobiology, School of Medicine, University of Louisville, Louisville, KY, 40292, USA

3 Kentucky Spinal Cord Injury Research Center, Department of Neurological Surgery, University of Louisville, Louisville, KY, 40292, USA

4 Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA

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BMC Bioinformatics 2012, 13:229  doi:10.1186/1471-2105-13-229

Published: 12 September 2012

Abstract

Background

High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.

Results

All annotations, no matter the granularity, can be aligned to genomic sequences and therefore annotated by genomic intervals. We have developed AbsIDconvert, a methodology for converting between genomic identifiers by first mapping them onto a common universal coordinate system using an interval tree which is subsequently queried for overlapping identifiers. AbsIDconvert has many potential uses, including gene identifier conversion, identification of features within a genomic region, and cross-species comparisons. The utility is demonstrated in three case studies: 1) comparative genomic study mapping plasmodium gene sequences to corresponding human and mosquito transcriptional regions; 2) cross-species study of Incyte clone sequences; and 3) analysis of human Ensembl transcripts mapped by Affymetrix®; and Agilent microarray probes. AbsIDconvert currently supports ID conversion of 53 species for a given list of input identifiers, genomic sequence, or genome intervals.

Conclusion

AbsIDconvert provides an efficient and reliable mechanism for conversion between identifier domains of interest. The flexibility of this tool allows for custom definition identifier domains contingent upon the availability and determination of a genomic mapping interval. As the genomes and the sequences for genetic elements are further refined, this tool will become increasingly useful and accurate. AbsIDconvert is freely available as a web application or downloadable as a virtual machine at: http://bioinformatics.louisville.edu/abid/ webcite.

Keywords:
Annotation; Gene ID conversion; Meta-analysis; Genomic range; Interval trees; Comparative analysis; Granularity; Universal identifier; AbsIDconvert