Evidence map›Paper›PMID 29112736›Full record

ArticleNucleic acids research2018

VarCards: an integrated genetic and clinical database for coding variants in the human genome.

Jinchen Li, Leisheng Shi, Kun Zhang, Yi Zhang, Shanshan Hu, Tingting Zhao, Huajing Teng, Xianfeng Li, Yi Jiang, Liying Ji and 1 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
124citing papers in PubMed, 1 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

124 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  18. REEV: review, evaluate and explain variants.Nucleic acids research · 2024
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64 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

11 authors.

Jinchen LiInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Leisheng ShiInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Kun ZhangInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Yi ZhangInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Shanshan HuInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Tingting ZhaoInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Huajing TengBeijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China.
Xianfeng LiLaboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China.
Yi JiangLaboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410078, China.
Liying JiInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.
Zhongsheng SunInstitute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang 325025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing number of genomic tools and databases were developed to facilitate the interpretation of genomic variants, particularly in coding regions. However, these tools are separately available in different online websites or databases, making it challenging for general clinicians, geneticists and biologists to obtain the first-hand information regarding some particular variants and genes of interest. Starting with coding regions and splice sties, we artificially generated all possible single nucleotide variants (n = 110 154 363) and cataloged all reported insertion and deletions (n = 1 223 370). We then annotated these variants with respect to functional consequences from more than 60 genomic data sources to develop a database, named VarCards (http://varcards.biols.ac.cn/), by which users can conveniently search, browse and annotate the variant- and gene-level implications of given variants, including the following information: (i) functional effects; (ii) functional consequences through different in silico algorithms; (iii) allele frequencies in different populations; (iv) disease- and phenotype-related knowledge; (v) general meaningful gene-level information; and (vi) drug-gene interactions. As a case study, we successfully employed VarCards in interpretation of de novo mutations in autism spectrum disorders. In conclusion, VarCards provides an intuitive interface of necessary information for researchers to prioritize candidate variations and genes.

Indexed as

Databases, Nucleic AcidGenetic VariationGenome, HumanAutism Spectrum DisorderGene FrequencyHumansMutationPhenotypeProteinsUser-Computer InterfaceProteins

Identifiers

PMID29112736
PMCPMC5753295

What OpenQuestion holds

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LicenceCC BY-NC
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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.