Evidence map›Paper›PMID 25408880›Full record

ArticleCell regeneration (London, England)2014

glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

Andrew Paul Hutchins, Ralf Jauch, Mateusz Dyla, Diego Miranda-Saavedra

Abstract read
In one paragraph

Article in Cell regeneration (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

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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.

Andrew Paul HutchinsKey Laboratory of Regenerative Biology, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530 China.
Ralf JauchGenome Regulation Laboratory, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530 China ; Laboratory for Structural Biochemistry, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672 Singapore.
Mateusz DylaLaboratory for Structural Biochemistry, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672 Singapore ; Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
Diego Miranda-SaavedraFibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne, NE2 4HH UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic datasets and the tools to analyze them have proliferated at an astonishing rate. However, such tools are often poorly integrated with each other: each program typically produces its own custom output in a variety of non-standard file formats. Here we present glbase, a framework that uses a flexible set of descriptors that can quickly parse non-binary data files. glbase includes many functions to intersect two lists of data, including operations on genomic interval data and support for the efficient random access to huge genomic data files. Many glbase functions can produce graphical outputs, including scatter plots, heatmaps, boxplots and other common analytical displays of high-throughput data such as RNA-seq, ChIP-seq and microarray expression data. glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing. In summary, glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data (especially next-generation sequencing and genome-wide data), and which has been instrumental in the analysis of complex data sets. glbase is freely available at http://bitbucket.org/oaxiom/glbase/.

Indexed as

BioinformaticsChIP-seqGenomicsMicroarrayMotifsRNA-seqTranscription factor

Identifiers

PMID25408880
PMCPMC4230833

What OpenQuestion holds

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LicenceCC BY
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.