Evidence map›Paper›PMID 24667251›Full record

ArticleDatabase : the journal of biological databases and curation2014

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).

Tsung-Jung Wu, Amirhossein Shamsaddini, Yang Pan, Krista Smith, Daniel J Crichton, Vahan Simonyan, Raja Mazumder

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 citing papers in PubMed.

  1. Toll like receptor 4 (TLR4) in neuroinflammation: From acute hemorrhagic stroke to chronic neurodegeneration.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
    Review
  2. Predictive Analysis and Validation of Critical Missense SNPs of theInternational journal of molecular sciences · 2025
    Review
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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

7 authors.

Tsung-Jung WuDepartment of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Data Systems and Technology Jet Propulsion Laboratory 4800 Oak Grove Drive Pasadena, CA 91109 Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, MD 20852, USA and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
Amirhossein Shamsaddini
Yang Pan
Krista Smith
Daniel J Crichton
Vahan Simonyan
Raja Mazumder

Funding

Alliance of Glycobiologists for Detection of CancerU01CA168926 · NCI · GEORGETOWN UNIVERSITY · PI GOLDMAN, RADOSLAV · 2012 to 2016
$2.0M
NCI NIH HHS U01 CA168926
6 · The paper itself

Abstract

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Indexed as

Databases, GeneticGenetic VariationHigh-Throughput Nucleotide SequencingPublicationsSoftwareUser-Computer InterfaceHumansNeoplasmsPolymorphism, Single NucleotideProteomePubMedProteome

Identifiers

PMID24667251
PMCPMC3965850

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.