Title |
SHEAR: sample heterogeneity estimation and assembly by reference
|
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Published in |
BMC Genomics, January 2014
|
DOI | 10.1186/1471-2164-15-84 |
Pubmed ID | |
Authors |
Sean R Landman, Tae Hyun Hwang, Kevin AT Silverstein, Yingming Li, Scott M Dehm, Michael Steinbach, Vipin Kumar |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 25% |
United Kingdom | 2 | 13% |
France | 2 | 13% |
India | 1 | 6% |
Germany | 1 | 6% |
Sweden | 1 | 6% |
China | 1 | 6% |
Unknown | 4 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 56% |
Scientists | 6 | 38% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 7% |
United Kingdom | 1 | 2% |
Netherlands | 1 | 2% |
Sweden | 1 | 2% |
Unknown | 48 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 36% |
Student > Ph. D. Student | 13 | 24% |
Other | 4 | 7% |
Student > Master | 4 | 7% |
Student > Bachelor | 4 | 7% |
Other | 5 | 9% |
Unknown | 5 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 27 | 49% |
Computer Science | 7 | 13% |
Biochemistry, Genetics and Molecular Biology | 5 | 9% |
Engineering | 2 | 4% |
Medicine and Dentistry | 2 | 4% |
Other | 3 | 5% |
Unknown | 9 | 16% |