ArticleBMC bioinformatics2011
Identifying elemental genomic track types and representing them uniformly.
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.
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.
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.
Who cites it
12 citing papers in PubMed.
- HiCognition: a visual exploration and hypothesis testing tool for 3D genomics.Genome biology · 2023Article
- Recommendations for the FAIRification of genomic track metadata.F1000Research · 2021Article
- Tasks, Techniques, and Tools for Genomic Data Visualization.Computer graphics forum : journal of the European Association for Computer Graphics · 2019Article
- Computational 3D genome modeling using Chrom3D.Nature protocols · 2018Article
- GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome.GigaScience · 2017Article
- In the loop: promoter-enhancer interactions and bioinformatics.Briefings in bioinformatics · 2016Review
- ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.PloS one · 2015Article
- Transcriptionally active regions are the preferred targets for chromosomal HPV integration in cervical carcinogenesis.PloS one · 2015Article
- HiBrowse: multi-purpose statistical analysis of genome-wide chromatin 3D organization.Bioinformatics (Oxford, England) · 2014Article
- Integrating multiple oestrogen receptor alpha ChIP studies: overlap with disease susceptibility regions, DNase I hypersensitivity peaks and gene expression.BMC medical genomics · 2013Article
- Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease.BMC medicine · 2013Article
- The Genomic HyperBrowser: an analysis web server for genome-scale data.Nucleic acids research · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.