Evidence map›Paper›PMID 30214605›Full record

ArticleOncology letters2018

Detection of copy number variants and loss of heterozygosity from impure tumor samples using whole exome sequencing data.

Xiaocheng Liu, Ao Li, Jianing Xi, Huanqing Feng, Minghui Wang

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 37% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Low-pass genome sequencing-based detection of absence of heterozygosity: validation in clinical cytogenetics.Genetics in medicine : official journal of the American College of Medical Genetics · 2021
    Article
  2. Article
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

5 authors at 1 institution in 1 country.

Xiaocheng LiuDepartment of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China.
Ao LiDepartment of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China.
Jianing XiDepartment of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China.
Huanqing FengDepartment of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China.
Minghui WangDepartment of Electronic Science and Technology, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China.
University of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using whole-exome sequencing (WES) for the detection of chromosomal aberrations from tumor samples has become increasingly popular, as it is cost-effective and time efficient. However, factors which present in WES tumor samples, including diversity in exon size, batch effect and tumor impurity, can complicate the identification of somatic mutation in each region of the exon. To address these issues, the authors of the present study have developed a novel method, PECNV, for the detection of genomic copy number variants and loss of heterozygosity in WES datasets. PECNV combines normalized logarithm ratio of read counts (Log Ratio) and B allele frequency (BAF), and then employs expectation maximization (EM) algorithm to estimate parameters involved in the models. A comprehensive assessment of PECNV of PECNV was performed by analyzing simulated datasets contaminated with different normal cell proportion and eight real primary triple-negative breast cancer samples. PECNV demonstrated superior results compared with ExomeCNV and EXCAVATOR for the detection of genomic aberrations in WES data.

Indexed as

copy number variantexome sequencingexpectation maximization algorithmloss of heterozygosity

Identifiers

PMID30214605
PMCPMC6126229
OpenAlexW2884308757

What OpenQuestion holds

Textmetadata
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.