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This article is part of the supplement: Seventh International Conference on Bioinformatics (InCoB2008)

Open Access Research

Ab-origin: an enhanced tool to identify the sourcing gene segments in germline for rearranged antibodies

Xiaojing Wang12, Di Wu23, Siyuan Zheng12, Jing Sun2, Lin Tao2, Yixue Li12* and Zhiwei Cao23*

Author Affiliations

1 Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences; Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China

2 Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai, 200235, PR China

3 College of Life science and Biotechnology, Tongji University, Shanghai, 200092, PR China

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BMC Bioinformatics 2008, 9(Suppl 12):S20  doi:10.1186/1471-2105-9-S12-S20

Published: 12 December 2008

Abstract

Background

In the adaptive immune system, variable regions of immunoglobulin (IG) are encoded by random recombination of variable (V), diversity (D), and joining (J) gene segments in the germline. Partitioning the functional antibody sequences to their sourcing germline gene segments is vital not only for understanding antibody maturation but also for promoting the potential engineering of the therapeutic antibodies. To date, several tools have been developed to perform such "trace-back" calculations. Yet, the predicting ability and processing volume of those tools vary significantly for different sets of data. Moreover, none of them give a confidence for immunoglobulin heavy diversity (IGHD) identification. Developing fast, efficient and enhanced tools is always needed with the booming of immunological data.

Results

Here, a program named Ab-origin is presented. It is designed by batch query against germline databases based on empirical knowledge, optimized scoring scheme and appropriate parameters. Special efforts have been paid to improve the identification accuracy of the short and volatile region, IGHD. In particular, a threshold score for certain sensitivity and specificity is provided to give the confidence level of the IGHD identification.

Conclusion

When evaluated using different sets of both simulated data and experimental data, Ab-origin outperformed all the other five popular tools in terms of prediction accuracy. The features of batch query and confidence indication of IGHD identification would provide extra help to users. The program is freely available at http://mpsq.biosino.org/ab-origin/supplementary.html webcite.