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Jos B. Poell

dblp:247/2155 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-1776-3779ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cancer genomics
0.412019
ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019
Bioinformatics and computational biology › cancer genomics › copy number analysis
copy number estimation
0.412019
ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019
Bioinformatics and computational biology › cancer genomics
tumor purity estimation
0.112019
ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019
YearPublicationVenuePosition
2019 ACE: absolute copy number estimation from low-coverage whole-genome sequencing data
abstract
SUMMARY: Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. AVAILABILITY AND IMPLEMENTATION: ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jos B. Poell, Matias Mendeville, Daoud Sie, Arjen Brink, Ruud H. Brakenhoff, Bauke Ylstra
Bioinform.1