Abstract
In this paper, we devote to find structural variants including deletions, insertions, and inversions which occur in Hedou12 genome in constrast to Williams82 genome. To find as many as possible potential structural variants, we try to develop new principles to detect discordant and split read map sets supporting structural variants. Aiming to enhance the precision of structural variant detection, we propose two new sequencing characteristic based models, which use the sequencing parameters of Hedou12 paired-end reads, as well as the parameters for Hedou12 paired-end reads to be aligned onto Williams82, to evaluate the probability a potential structural variant can occur in. To remove those false members from the potential structural variants, we propose a integer linear program to describe formally on which potential structural variants it should accept to achieve as high as possible a probability summation, whose solution can help predict more credible structural variants. The feasibility and precision of our algorithm are verified by comparing with DELLY version 0.5.8 and LUMPY version 0.2.2.3. The software is available for download at https://pan.baidu.com/s/1rasmtti.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jiang, Y., Wang, Y., Brudno, M.: PRISM: pair-read informed split-read mapping for base-pair level detection of insertion, deletion and structural variants. Bioinformatics 28(20), 2576–2583 (2012)
Korbel, J., Abyzov, A., Mu, X., Carriero, N., Cayting, P., Zhang, Z., Snyder, M., Gerstein, M.: PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biol. 10(2), R23 (2009)
Abyzov, A., Urban, A., Snyder, M., Gerstein, M.: CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21(6), 974–984 (2011)
Chen, K., Wallis, J., McLellan, M., Larson, D., Kalicki, J., Poh, C., McGrath, S., Wendl, M., Zhang, Q., Locke, D., Shi, X., Fulton, R., Ley, T., Wilson, R., Ding, L., Mardis, E.: BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat. Methods 6(9), 677–681 (2009)
Fan, X., Abbott, T., Larson, D., Chen, K.: BreakDancer: identification of genomic structural variation from paired-end read mapping. Curr. Protoc. Bioinf. 45, 15.6.1–15.6.11 (2014)
Karakoc, E., Alkan, C., O’Roak, B., Dennis, M., Vives, L., Mark, K., Rieder, M., Nickerson, D., Eichler, E.: Detection of structural variants and indels within exome data. Nat. Methods 9(2), 176–178 (2012)
Medvedev, P., Fiume, M., Dzamba, M., Smith, T., Brudno, M.: Detecting copy number variation with mated short reads. Genome Res. 20(11), 1613–1622 (2010)
Rausch, T., Zichner, T., Schlattl, A., Stütz, A., Benes, V., Korbel, J.: DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28(18), i333–i339 (2012)
Layer, R., Chiang, C., Quinlan, A., Hall, I.: LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15(6), R84 (2014)
Song, X., Wei, H., Cheng, W., Yang, S., Zhao, Y., Zhang, H., Feng, X.: Development of INDEL markers for genetic mapping based on whole genome resequencing in soybean. G3 (Bethesda, Md) 5(12), 2793–2799 (2015)
Gao, J., Yang, S., Cheng, W., Fu, Y., Leng, J., Yuan, X., Jiang, N., Ma, J., Feng, X.: GmILPA1, encoding an APC8-like protein, controls leaf petiole angle in soybean. J. Am. Soc. Plant Biologists 174(2), 1167–11176 (2017)
Li, H., Durbin, R.: Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)
Hach, F., Hormozdiari, F., Alkan, C., Hormozdiari, F., Birol, I., Eichler, E., Sahinalp, S.: mrsFAST: a cache-oblivious algorithm for short-read mapping. Nat. Methods 7(8), 576–577 (2010)
Ewing, B., Green, P.: Base-calling of automated sequencer traces using Phred. II. Error probabilities. Genome Res. 8(3), 186–194 (1998)
Acknowledgment
This paper is supported by National natural science foundation of China, No. 61472222, 61732009, 61672325, 61761136017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Jia, H., Wei, H., Zhu, D., Wang, R., Feng, H., Feng, X. (2018). PASA: Identifying More Credible Structural Variants of Hedou12. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-95930-6_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95929-0
Online ISBN: 978-3-319-95930-6
eBook Packages: Computer ScienceComputer Science (R0)