Evidence map›Paper›PMID 29432475›Full record

ArticlePloS one2018

High-Throughput Tabular Data Processor - Platform independent graphical tool for processing large data sets.

Piotr Madanecki, Magdalena Bałut, Patrick G Buckley, J Renata Ochocka, Rafał Bartoszewski, David K Crossman, Ludwine M Messiaen, Arkadiusz Piotrowski

Abstract read
In one paragraph

Article in PloS one, 2018. 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

8 authors.

Piotr MadaneckiDepartment of Biology and Pharmaceutical Botany, Medical University of Gdansk, Gdansk, Poland.ORCID 0000-0003-1111-3695
Magdalena BałutDepartment of Biology and Pharmaceutical Botany, Medical University of Gdansk, Gdansk, Poland.
Patrick G BuckleyMolecular Pathology Laboratory, Beaumont Hospital, Dublin, Ireland.
J Renata OchockaDepartment of Biology and Pharmaceutical Botany, Medical University of Gdansk, Gdansk, Poland.
Rafał BartoszewskiDepartment of Biology and Pharmaceutical Botany, Medical University of Gdansk, Gdansk, Poland.
David K CrossmanHeflin Center for Genomic Science, Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Ludwine M MessiaenMedical Genomics Laboratory, Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Arkadiusz PiotrowskiDepartment of Biology and Pharmaceutical Botany, Medical University of Gdansk, Gdansk, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput technologies generate considerable amount of data which often requires bioinformatic expertise to analyze. Here we present High-Throughput Tabular Data Processor (HTDP), a platform independent Java program. HTDP works on any character-delimited column data (e.g. BED, GFF, GTF, PSL, WIG, VCF) from multiple text files and supports merging, filtering and converting of data that is produced in the course of high-throughput experiments. HTDP can also utilize itemized sets of conditions from external files for complex or repetitive filtering/merging tasks. The program is intended to aid global, real-time processing of large data sets using a graphical user interface (GUI). Therefore, no prior expertise in programming, regular expression, or command line usage is required of the user. Additionally, no a priori assumptions are imposed on the internal file composition. We demonstrate the flexibility and potential of HTDP in real-life research tasks including microarray and massively parallel sequencing, i.e. identification of disease predisposing variants in the next generation sequencing data as well as comprehensive concurrent analysis of microarray and sequencing results. We also show the utility of HTDP in technical tasks including data merge, reduction and filtering with external criteria files. HTDP was developed to address functionality that is missing or rudimentary in other GUI software for processing character-delimited column data from high-throughput technologies. Flexibility, in terms of input file handling, provides long term potential functionality in high-throughput analysis pipelines, as the program is not limited by the currently existing applications and data formats. HTDP is available as the Open Source software (https://github.com/pmadanecki/htdp).

Indexed as

Computer GraphicsDatabases, GeneticData Interpretation, StatisticalDNA Copy Number VariationsDNA, NeoplasmGenetic Predisposition to DiseaseHigh-Throughput Nucleotide SequencingHumansNeurilemmomaSequence Analysis, DNASoftwareUser-Computer InterfaceDNA, Neoplasm

Identifiers

PMID29432475
PMCPMC5809091

What OpenQuestion holds

Textmetadata
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.