Stefano Lonardi1, Hamid Mirebrahim1, Steve Wanamaker1, Matthew Alpert1, Gianfranco Ciardo1, Denisa Duma2, Timothy J Close1. 1. Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA. 2. Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA Department of Computer Science and Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, Department of Computer Science, Iowa State University, Ames, IA 50011 and Baylor College of Medicine, Houston, TX 77030, USA.
Abstract
MOTIVATION: As the invention of DNA sequencing in the 70s, computational biologists have had to deal with the problem of de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing. RESULTS: We explore the effect of ultra-deep sequencing data in two domains: (i) the problem of decoding reads to bacterial artificial chromosome (BAC) clones (in the context of the combinatorial pooling design we have recently proposed), and (ii) the problem of de novo assembly of BAC clones. Using real ultra-deep sequencing data, we show that when the depth of sequencing increases over a certain threshold, sequencing errors make these two problems harder and harder (instead of easier, as one would expect with error-free data), and as a consequence the quality of the solution degrades with more and more data. For the first problem, we propose an effective solution based on 'divide and conquer': we 'slice' a large dataset into smaller samples of optimal size, decode each slice independently, and then merge the results. Experimental results on over 15 000 barley BACs and over 4000 cowpea BACs demonstrate a significant improvement in the quality of the decoding and the final assembly. For the second problem, we show for the first time that modern de novo assemblers cannot take advantage of ultra-deep sequencing data. AVAILABILITY AND IMPLEMENTATION: Python scripts to process slices and resolve decoding conflicts are available from http://goo.gl/YXgdHT; software Hashfilter can be downloaded from http://goo.gl/MIyZHs CONTACT: stelo@cs.ucr.edu or timothy.close@ucr.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: As the invention of DNA sequencing in the 70s, computational biologists have had to deal with the problem of de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing. RESULTS: We explore the effect of ultra-deep sequencing data in two domains: (i) the problem of decoding reads to bacterial artificial chromosome (BAC) clones (in the context of the combinatorial pooling design we have recently proposed), and (ii) the problem of de novo assembly of BAC clones. Using real ultra-deep sequencing data, we show that when the depth of sequencing increases over a certain threshold, sequencing errors make these two problems harder and harder (instead of easier, as one would expect with error-free data), and as a consequence the quality of the solution degrades with more and more data. For the first problem, we propose an effective solution based on 'divide and conquer': we 'slice' a large dataset into smaller samples of optimal size, decode each slice independently, and then merge the results. Experimental results on over 15 000 barley BACs and over 4000 cowpea BACs demonstrate a significant improvement in the quality of the decoding and the final assembly. For the second problem, we show for the first time that modern de novo assemblers cannot take advantage of ultra-deep sequencing data. AVAILABILITY AND IMPLEMENTATION: Python scripts to process slices and resolve decoding conflicts are available from http://goo.gl/YXgdHT; software Hashfilter can be downloaded from http://goo.gl/MIyZHs CONTACT: stelo@cs.ucr.edu or timothy.close@ucr.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Adam G Clooney; Fiona Fouhy; Roy D Sleator; Aisling O' Driscoll; Catherine Stanton; Paul D Cotter; Marcus J Claesson Journal: PLoS One Date: 2016-02-05 Impact factor: 3.240
Authors: María Muñoz-Amatriaín; Stefano Lonardi; MingCheng Luo; Kavitha Madishetty; Jan T Svensson; Matthew J Moscou; Steve Wanamaker; Tao Jiang; Andris Kleinhofs; Gary J Muehlbauer; Roger P Wise; Nils Stein; Yaqin Ma; Edmundo Rodriguez; Dave Kudrna; Prasanna R Bhat; Shiaoman Chao; Pascal Condamine; Shane Heinen; Josh Resnik; Rod Wing; Heather N Witt; Matthew Alpert; Marco Beccuti; Serdar Bozdag; Francesca Cordero; Hamid Mirebrahim; Rachid Ounit; Yonghui Wu; Frank You; Jie Zheng; Hana Simková; Jaroslav Dolezel; Jane Grimwood; Jeremy Schmutz; Denisa Duma; Lothar Altschmied; Tom Blake; Phil Bregitzer; Laurel Cooper; Muharrem Dilbirligi; Anders Falk; Leila Feiz; Andreas Graner; Perry Gustafson; Patrick M Hayes; Peggy Lemaux; Jafar Mammadov; Timothy J Close Journal: Plant J Date: 2015-09-21 Impact factor: 6.417