Title |
Repliscan: a tool for classifying replication timing regions
|
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Published in |
BMC Bioinformatics, August 2017
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DOI | 10.1186/s12859-017-1774-x |
Pubmed ID | |
Authors |
Gregory J. Zynda, Jawon Song, Lorenzo Concia, Emily E. Wear, Linda Hanley-Bowdoin, William F. Thompson, Matthew W. Vaughn |
Abstract |
Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 25% |
United States | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 75% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 27% |
Student > Master | 6 | 20% |
Researcher | 6 | 20% |
Student > Doctoral Student | 3 | 10% |
Student > Bachelor | 3 | 10% |
Other | 2 | 7% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 10 | 33% |
Agricultural and Biological Sciences | 7 | 23% |
Engineering | 4 | 13% |
Computer Science | 4 | 13% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
Other | 3 | 10% |
Unknown | 1 | 3% |