Control-FREEC: A tool for assessing copy number and allelic content using next-generation sequencing data
Bioinformatics, ISSN: 1367-4803, Vol: 28, Issue: 3, Page: 423-425
2012
- 710Citations
- 709Captures
- 4Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations710
- Citation Indexes706
- 706
- CrossRef635
- Patent Family Citations4
- Patent Families4
- Captures709
- Readers709
- 624
- 80
- Mentions4
- News Mentions4
- News4
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Cellular adaptation to cancer therapy along a resistance continuum
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Article Description
More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. © The Author(s) 2011. Published by Oxford University Press. All rights reserved.
Bibliographic Details
Oxford University Press (OUP)
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