Evidence map›Paper›PMID 30653336›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2019

miCloud: A Plug-n-Play, Extensible, On-Premises Bioinformatics Cloud for Seamless Execution of Complex Next-Generation Sequencing Data Analysis Pipelines.

Baekdoo Kim, Thahmina Ali, Changsu Dong, Carlos Lijeron, Raja Mazumder, Claudia Wultsch, Konstantinos Krampis

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Baekdoo Kim1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
Thahmina Ali1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
Changsu Dong1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
Carlos Lijeron1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
Raja Mazumder2 Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, District of Columbia.
Claudia Wultsch1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
Konstantinos Krampis1 Weill Cornell Medicine, Belfer Research Building, New York, New York.

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Clinical and Translational Science CenterUL1TR000457 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$46.0M
Basic and Translational Research Center for Reducing Health DisparitiesG12MD007599 · NIMHD · HUNTER COLLEGE · PI ANGULO, JESUS A · 2012 to 2017
$14.6M
NCATS NIH HHS UL1 TR000457NCATS NIH HHS UL1 TR002384NIMHD NIH HHS G12 MD007599
6 · The paper itself

Abstract

The availability of low-cost small-factor sequencers, such as the Illumina MiSeq, MiniSeq, or iSeq, have paved the way for democratizing genomics sequencing, providing researchers in minority universities with access to the technology that was previously only affordable by institutions with large core facilities. However, these instruments are not bundled with software for performing bioinformatics data analysis, and the data analysis can be the main bottleneck for independent laboratories or even small clinical facilities that consider adopting genomic sequencing for medical applications. To address this issue, we have developed miCloud, a bioinformatics platform that enables genomic data analysis through a fully featured data analysis cloud, which seamlessly integrates with genome sequencers over the local network. The miCloud can be easily deployed without any prior bioinformatics expertise on any computing environment, from a laboratory computer workstation to a university computer cluster. Our platform not only provides access to a set of preconfigured RNA-Seq and CHIP-Seq bioinformatics pipelines, but also enables users to develop or install new preconfigured tools from the large selection available on open-source online Docker container repositories. The miCloud built-in analysis pipelines are also integrated with the Visual Omics Explorer framework (Kim et al., 2016), which provides rich interactive visualizations and publication-ready graphics from the next-generation sequencing data. Ultimately, the miCloud demonstrates a bioinformatics approach that can be adopted in the field for standardizing genomic data analysis, similarly to the way molecular biology sample preparation kits have standardized laboratory operations.

Indexed as

Cloud ComputingSoftwareAnimalsGenomicsHumansRNA-Seqbioinformaticscloud computingDockerGalaxynext-generation sequencing

Identifiers

PMID30653336
PMCPMC6441280

What OpenQuestion holds

Textmetadata
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.