Evidence map›Paper›PMID 30906832›Full record

ArticleGenes & diseases2019

AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data.

Taobo Hu, Si Chen, Ata Ullah, Hong Xue

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes & diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Identification of Copy Number Alterations from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022
    Article
  4. 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

4 authors.

Taobo HuDivision of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Si ChenDivision of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Ata UllahDivision of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Hong XueDivision of Life Science, Applied Genomics Centre and Centre for Statistical Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The usage of next-generation sequencing (NGS) to detect copy number variation (CNV) is widely accepted in cancer research. Based on an AluScanCNV software developed by us previously, an AluScanCNV2 software has been developed in the present study as an R package that performs CNV detection from NGS data obtained through AluScan, whole-genome sequencing or other targeted NGS platforms. Its applications would include the expedited usage of somatic CNVs for cancer subtyping, and usage of recurrent germline CNVs to perform machine learning-assisted prediction of a test subject's susceptibility to cancer.

Indexed as

AluScanBioinformaticsCancer predispositionCancer subtypingMachine learning

Identifiers

PMID30906832
PMCPMC6411622

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.