Abstract
Background: Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis. Results: By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.Conclusion: SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.
Original language | English (US) |
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Article number | 84 |
Journal | BMC Genomics |
Volume | 15 |
Issue number | 1 |
DOIs | |
State | Published - Jan 29 2014 |
Bibliographical note
Funding Information:We would like to thank Jiwoong Kim, Minsoo Kim, and Kiejung Park for their help in running experiments and analyzing results. We are grateful to the Minnesota Supercomputing Institute for providing computing, software, and data storage support for this project. This work was funded in part by a NIH training grant (T32 EB008389) and the University of Minnesota Interdisciplinary Doctoral Fellowship, both awarded to SL.
Keywords
- Assembly
- Genomics
- Heterogeneity
- Next-generation sequencing
- Personal genome
- Prostate cancer
- Sequence analysis
- Structural variation