Evidence map›Paper›PMID 24265680›Full record

ArticlePloS one2013

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data.

Mengjie Chen, Murat Gunel, Hongyu Zhao

Abstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mengjie ChenProgram of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
Murat Gunel
Hongyu Zhao

Funding

STATISTICAL METHODS TO MAP GENES FOR COMPLEX TRAITSR01GM059507 · NIGMS · YALE UNIVERSITY · PI ZHAO, HONGYU · 1999 to 2013
$3.6M
NIGMS NIH HHS R01 GM059507NIGMS NIH HHS R01 GM59507
6 · The paper itself

Abstract

Whole genome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. However, analysis of somatic copy-number changes from sequencing data is still challenging because of insufficient sequencing coverage, unknown tumor sample purity and subclonal heterogeneity. Here we describe a computational framework, named SomatiCA, which explicitly accounts for tumor purity and subclonality in the analysis of somatic copy-number profiles. Taking read depths (RD) and lesser allele frequencies (LAF) as input, SomatiCA will output 1) admixture rate for each tumor sample, 2) somatic allelic copy-number for each genomic segment, 3) fraction of tumor cells with subclonal change in each somatic copy number aberration (SCNA), and 4) a list of substantial genomic aberration events including gain, loss and LOH. SomatiCA is available as a Bioconductor R package at http://www.bioconductor.org/packages/2.13/bioc/html/SomatiCA.html.

Indexed as

Bayes TheoremDNA Copy Number VariationsFalse Positive ReactionsGene FrequencyGenomicsGlioblastomaHumansModels, StatisticalNeoplasmsPolymorphism, Single NucleotideSequence Analysis, DNA

Identifiers

PMID24265680
PMCPMC3827077

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.