MOTIVATION: Immunoglobulin heavy chain genes are formed by recombination of genes randomly selected from sets of IGHV, IGHD and IGHJ genes. Utilities have been developed to identify genes that contribute to observed VDJ rearrangements, but in the absence of datasets of known rearrangements, the evaluation of these utilities is problematic. We have analyzed thousands of VDJ rearrangements from an individual (S22) whose IGHV, IGHD and IGHJ genotype can be inferred from the dataset. Knowledge of this genotype means that the Stanford_S22 dataset can serve to benchmark the performance of IGH alignment utilities. RESULTS: We evaluated the performance of seven utilities. Failure to partition a sequence into genes present in the S22 genome was considered an error, and error rates for different utilities ranged from 7.1% to 13.7%. AVAILABILITY: Supplementary data includes the S22 genotypes and alignments. The Stanford_S22 dataset and an evaluation tool is available at http://www.emi.unsw.edu.au/~ihmmune/IGHUtilityEval/.
MOTIVATION: Immunoglobulin heavy chain genes are formed by recombination of genes randomly selected from sets of IGHV, IGHD and IGHJ genes. Utilities have been developed to identify genes that contribute to observed VDJ rearrangements, but in the absence of datasets of known rearrangements, the evaluation of these utilities is problematic. We have analyzed thousands of VDJ rearrangements from an individual (S22) whose IGHV, IGHD and IGHJ genotype can be inferred from the dataset. Knowledge of this genotype means that the Stanford_S22 dataset can serve to benchmark the performance of IGH alignment utilities. RESULTS: We evaluated the performance of seven utilities. Failure to partition a sequence into genes present in the S22 genome was considered an error, and error rates for different utilities ranged from 7.1% to 13.7%. AVAILABILITY: Supplementary data includes the S22 genotypes and alignments. The Stanford_S22 dataset and an evaluation tool is available at http://www.emi.unsw.edu.au/~ihmmune/IGHUtilityEval/.
Authors: Chen Wang; Yi Liu; Lan T Xu; Katherine J L Jackson; Krishna M Roskin; Tho D Pham; Jonathan Laserson; Eleanor L Marshall; Katie Seo; Ji-Yeun Lee; David Furman; Daphne Koller; Cornelia L Dekker; Mark M Davis; Andrew Z Fire; Scott D Boyd Journal: J Immunol Date: 2013-12-11 Impact factor: 5.422
Authors: Marie J Kidd; Zhiliang Chen; Yan Wang; Katherine J Jackson; Lyndon Zhang; Scott D Boyd; Andrew Z Fire; Mark M Tanaka; Bruno A Gaëta; Andrew M Collins Journal: J Immunol Date: 2011-12-28 Impact factor: 5.422
Authors: Chen Wang; Yi Liu; Mary M Cavanagh; Sabine Le Saux; Qian Qi; Krishna M Roskin; Timothy J Looney; Ji-Yeun Lee; Vaishali Dixit; Cornelia L Dekker; Gary E Swan; Jörg J Goronzy; Scott D Boyd Journal: Proc Natl Acad Sci U S A Date: 2014-12-22 Impact factor: 11.205
Authors: Poornima Parameswaran; Yi Liu; Krishna M Roskin; Katherine K L Jackson; Vaishali P Dixit; Ji-Yeun Lee; Karen L Artiles; Simona Zompi; Maria José Vargas; Birgitte B Simen; Bozena Hanczaruk; Kim R McGowan; Muhammad A Tariq; Nader Pourmand; Daphne Koller; Angel Balmaseda; Scott D Boyd; Eva Harris; Andrew Z Fire Journal: Cell Host Microbe Date: 2013-06-12 Impact factor: 21.023
Authors: Simon D W Frost; Ben Murrell; A S Md Mukarram Hossain; Gregg J Silverman; Sergei L Kosakovsky Pond Journal: Philos Trans R Soc Lond B Biol Sci Date: 2015-09-05 Impact factor: 6.237