Evidence map›Paper›PMID 24498045›Full record

ArticlePloS one2014

Genome-wide identification of somatic aberrations from paired normal-tumor samples.

Ao Li, Yuanning Liu, Qihong Zhao, Huanqing Feng, Lyndsay Harris, Minghui Wang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

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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

6 authors at 3 institutions in 2 countries.

Ao LiCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, China ; School of Information Science and Technology, University of Science and Technology of China, Hefei, China.
Yuanning LiuSchool of Information Science and Technology, University of Science and Technology of China, Hefei, China.
Qihong ZhaoSchool of Public Health, Anhui Medical University, Hefei, China.
Huanqing FengSchool of Information Science and Technology, University of Science and Technology of China, Hefei, China.
Lyndsay HarrisSeidman Cancer Center, School of Medicine, Case Western Reserve University, Cleveland, United States of America.
Minghui WangCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, China ; School of Information Science and Technology, University of Science and Technology of China, Hefei, China.
University of Science and Technology of China · CNAnhui Medical University · CNCase Western Reserve University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.

Indexed as

Cell LineCell Line, TumorDNA Copy Number VariationsGenome, HumanGenome-Wide Association StudyGenotypeHumansPolymorphism, Single Nucleotide

Identifiers

PMID24498045
PMCPMC3907544
OpenAlexW2009997078

What OpenQuestion holds

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LicenceCC BY
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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.