Evidence map›Paper›PMID 22208806›Full record

ArticleBMC bioinformatics2011

Identifying elemental genomic track types and representing them uniformly.

Sveinung Gundersen, Matúš Kalaš, Osman Abul, Arnoldo Frigessi, Eivind Hovig, Geir Kjetil Sandve

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Tasks, Techniques, and Tools for Genomic Data Visualization.Computer graphics forum : journal of the European Association for Computer Graphics · 2019
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  6. Review
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sveinung GundersenDepartment of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway.
Matúš Kalaš
Osman Abul
Arnoldo Frigessi
Eivind Hovig
Geir Kjetil Sandve

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological context, and the increasingly dispersed mode of data generation, imply a need for precise, interoperable and flexible representations of genomic features through formats that are easy to parse. A host of alternative formats are currently available and in use, complicating analysis and tool development. The issue of whether and how the multitude of formats reflects varying underlying characteristics of data has to our knowledge not previously been systematically treated.

resultsWe here identify intrinsic distinctions between genomic features, and argue that the distinctions imply that a certain variation in the representation of features as genomic tracks is warranted. Four core informational properties of tracks are discussed: gaps, lengths, values and interconnections. From this we delineate fifteen generic track types. Based on the track type distinctions, we characterize major existing representational formats and find that the track types are not adequately supported by any single format. We also find, in contrast to the XML formats, that none of the existing tabular formats are conveniently extendable to support all track types. We thus propose two unified formats for track data, an improved XML format, BioXSD 1.1, and a new tabular format, GTrack 1.0.

conclusionsThe defined track types are shown to capture relevant distinctions between genomic annotation tracks, resulting in varying representational needs and analysis possibilities. The proposed formats, GTrack 1.0 and BioXSD 1.1, cater to the identified track distinctions and emphasize preciseness, flexibility and parsing convenience.

Indexed as

Sequence Analysis, DNAGenomeGenome, HumanGenomicsHumansRegulatory Sequences, Nucleic AcidSoftware

Identifiers

PMID22208806
PMCPMC3315820

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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.