Evidence map›Paper›PMID 33213365›Full record

ArticleBMC genomics2020

NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes' factors.

Bronte Wen, Hyun Jun Jung, Lihe Chen, Fahad Saeed, Mark A Knepper

Abstract read
In one paragraph

Article in BMC genomics, 2020. 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. Review
  2. Article
  3. Review
  4. Bayesian identification of candidate transcription factors for the regulation ofAmerican journal of physiology. Renal physiology · 2021
    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.

Bronte WenEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Hyun Jun JungEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Lihe ChenEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Fahad SaeedSchool of Computing and Information Sciences, Florida International University, Miami, Florida, USA.
Mark A KnepperEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA. knepperm@nhlbi.nih.gov.ORCID http://orcid.org/0000-0002-2276-8091

Funding

Solute And Water Transport In Renal EpitheliaZIAHL001285 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI KNEPPER, MARK · 2009 to 2025
$34.9M
Computational Tools for ProteomicsZIAHL006129 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI KNEPPER, MARK · 2011 to 2025
$8.1M
Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics DataR01GM134384 · NIGMS · FLORIDA INTERNATIONAL UNIVERSITY · PI SAEED, FAHAD · 2020 to 2022
$1.3M
Parallel Algorithms for Big Data from Mass Spectrometry based ProteomicsR15GM120820 · NIGMS · WESTERN MICHIGAN UNIVERSITY · PI GUPTA, AJAY · 2017 to 2017
$419k
Intramural NIH HHS ZIA HL001285Intramural NIH HHS ZIA HL006129National Science Foundation NSF CAREER OAC 1925960NHLBI NIH HHS ZIA-HL001285NHLBI NIH HHS ZIA-HL006129NIGMS NIH HHS R15 GM120820NIGMS NIH HHS R15GM120820
6 · The paper itself

Abstract

backgroundNext-generation sequencing (NGS) is widely used for genome-wide identification and quantification of DNA elements involved in the regulation of gene transcription. Studies that generate multiple high-throughput NGS datasets require data integration methods for two general tasks: 1) generation of genome-wide data tracks representing an aggregate of multiple replicates of the same experiment; and 2) combination of tracks from different experimental types that provide complementary information regarding the location of genomic features such as enhancers.

resultsNGS-Integrator is a Java-based command line application, facilitating efficient integration of multiple genome-wide NGS datasets. NGS-Integrator first transforms all input data tracks using the complement of the minimum Bayes' factor so that all values are expressed in the range [0,1] representing the probability of a true signal given the background noise. Then, NGS-Integrator calculates the joint probability for every genomic position to create an integrated track. We provide examples using real NGS data generated in our laboratory and from the mouse ENCODE database.

conclusionsOur results show that NGS-Integrator is both time- and memory-efficient. Our examples show that NGS-Integrator can integrate information to facilitate downstream analyses that identify functional regulatory domains along the genome.

Indexed as

High-Throughput Nucleotide SequencingSoftwareAnimalsBayes TheoremGenomeGenomicsMiceEfficient data integrationGenome-wide NGSMinimum Bayes factorNGS data analysis

Identifiers

PMID33213365
PMCPMC7678096

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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.