Evidence map›Paper›PMID 29379135›Full record

ReviewNature reviews. Genetics2018

Cloud computing for genomic data analysis and collaboration.

Ben Langmead, Abhinav Nellore

Erratum issuedAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature reviews. Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 101 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 1 pooled it
–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

101 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  18. Rare disease genomics and precision medicine.Genomics & informatics · 2024
    Review
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  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Ben LangmeadDepartment of Computer Science, Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
Abhinav NelloreDepartment of Biomedical Engineering, Department of Surgery, Computational Biology Program, Oregon Health and Science University, Portland, OR, USA.

Funding

Hardening and Scaling Core Genomics SoftwareR01GM118568 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI LANGMEAD, BENJAMIN THOMAS · 2016 to 2020
$2.0M
NIGMS NIH HHS R01 GM118568
6 · The paper itself

Abstract

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

Indexed as

Cloud ComputingGenomicsHigh-Throughput Nucleotide SequencingInternetComputational BiologyHumans

Identifiers

PMID29379135
PMCPMC6452449

What OpenQuestion holds

Textmetadata
LicenceTDM
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.