Evidence map›Paper›PMID 27507884›Full record

ArticleNucleic acids research2016

Enhanced copy number variants detection from whole-exome sequencing data using EXCAVATOR2.

Romina D'Aurizio, Tommaso Pippucci, Lorenzo Tattini, Betti Giusti, Marco Pellegrini, Alberto Magi

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

64 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Integrative genomic analysis identifies clinically relevant prognostic markers for colorectal cancer patients with liver metastasis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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  16. Case report: Duplication of theFrontiers in endocrinology · 2024
    Article
  17. Article
  18. Discovery of a Novel Shared Variant AmongInternational journal of genomics · 2024
    Article
  19. Review
  20. Article

4 more citing papers are in PubMed but not listed here.

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.

Romina D'AurizioLaboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics and Institute of Clinical Physiology, National Research Council, Pisa, Italy romina.daurizio@gmail.com.
Tommaso PippucciMedical Genetics Unit, Sant'Orsola Malpighi Polyclinic, Bologna, Italy.
Lorenzo TattiniDepartment of Computer Science, University of Pisa, Pisa, Italy.
Betti GiustiDepartment of Experimental and Clinical Medicine, University of Florence, Florence.
Marco PellegriniLaboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics and Institute of Clinical Physiology, National Research Council, Pisa, Italy.
Alberto MagiDepartment of Experimental and Clinical Medicine, University of Florence, Florence albertomagi@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Copy Number Variants (CNVs) are structural rearrangements contributing to phenotypic variation that have been proved to be associated with many disease states. Over the last years, the identification of CNVs from whole-exome sequencing (WES) data has become a common practice for research and clinical purpose and, consequently, the demand for more and more efficient and accurate methods has increased. In this paper, we demonstrate that more than 30% of WES data map outside the targeted regions and that these reads, usually discarded, can be exploited to enhance the identification of CNVs from WES experiments. Here, we present EXCAVATOR2, the first read count based tool that exploits all the reads produced by WES experiments to detect CNVs with a genome-wide resolution. To evaluate the performance of our novel tool we use it for analysing two WES data sets, a population data set sequenced by the 1000 Genomes Project and a tumor data set made of bladder cancer samples. The results obtained from these analyses demonstrate that EXCAVATOR2 outperforms other four state-of-the-art methods and that our combined approach enlarge the spectrum of detectable CNVs from WES data with an unprecedented resolution. EXCAVATOR2 is freely available at http://sourceforge.net/projects/excavator2tool/.

Indexed as

DNA Copy Number VariationsExomeHigh-Throughput Nucleotide SequencingAlgorithmsComputational BiologyGenomicsHumansReproducibility of ResultsSoftwareWeb Browser

Identifiers

PMID27507884
PMCPMC5175347

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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.