Evidence map›Paper›PMID 27048349›Full record

ArticleDatabase : the journal of biological databases and curation2016

Genic insights from integrated human proteomics in GeneCards.

Simon Fishilevich, Shahar Zimmerman, Asher Kohn, Tsippi Iny Stein, Tsviya Olender, Eugene Kolker, Marilyn Safran, Doron Lancet

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
128citing papers in PubMed, 3 pooled it
–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

128 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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68 more citing papers are in PubMed but not listed here.

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.

Simon FishilevichDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel simon.fishilevich@weizmann.ac.il.
Shahar ZimmermanDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Asher KohnLifeMap Sciences Ltd., Tel Aviv 69710, Israel.
Tsippi Iny SteinDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Tsviya OlenderDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Eugene KolkerCDO Analytics, Seattle Children's Hospital, Seattle, WA 98101 USA Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, WA 98101 USA Data-Enabled Life Sciences Alliance (DELSA), Seattle, Washington, 98101, USA Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington School of Medicine, Seattle, WA 98109, USA Department of Chemistry and Chemical Biology, Northeastern University College of Science, Boston, MA 02115 USA.
Marilyn SafranDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
Doron LancetDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 7610001, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/.

Indexed as

Databases, ProteinData MiningGenesCluster AnalysisHumansPrincipal Component AnalysisProteomeProteomicsRNAProteomeRNA

Identifiers

PMID27048349
PMCPMC4820835

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.