Evidence map›Paper›PMID 27044653›Full record

ArticleBMC bioinformatics2016

SoftPanel: a website for grouping diseases and related disorders for generation of customized panels.

Likun Wang, Cong Zhang, Johnathan Watkins, Yan Jin, Michael McNutt, Yuxin Yin

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

6 authors.

Likun WangInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.
Cong ZhangInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.
Johnathan WatkinsInstitute for Mathematical and Molecular Biomedicine, King's College London, Guy's Campus, London, SE1 1UL, UK.
Yan JinInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.
Michael McNuttInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China.
Yuxin YinInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China. yinyuxin@hsc.pku.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTargeted next-generation sequencing is playing an increasingly important role in biological research and clinical diagnosis by allowing researchers to sequence high priority genes at much higher depths and at a fraction of the cost of whole genome or exome sequencing. However, in designing the panel of genes to be sequenced, investigators need to consider the tradeoff between the better sensitivity of a broad panel and the higher specificity of a potentially more relevant panel. Although tools to prioritize candidate disease genes have been developed, the great majority of these require prior knowledge and a set of seed genes as input, which is only possible for diseases with a known genetic etiology.

resultsTo meet the demands of both researchers and clinicians, we have developed a user-friendly website called SoftPanel. This website is intended to serve users by allowing them to input a single disorder or a disorder group and generate a panel of genes predicted to underlie the disorder of interest. Various methods of retrieval including a keyword search, browsing of an arborized list of International Classification of Diseases, 10th revision (ICD-10) codes or using disorder phenotypic similarities can be combined to define a group of disorders and the genes known to be associated with them. Moreover, SoftPanel enables users to expand or refine a gene list by utilizing several biological data resources. In addition to providing users with the facility to create a "hard" panel that contains an exact gene list for targeted sequencing, SoftPanel also enables generation of a "soft" panel of genes, which may be used to further filter a significantly altered set of genes identified through whole genome or whole exome sequencing. The service and data provided by SoftPanel can be accessed at http://www.isb.pku.edu.cn/SoftPanel/ . A tutorial page is included for trying out sample data and interpreting results.

conclusionSoftPanel provides a convenient and powerful tool for creating a targeted panel of potential disease genes while supporting different forms of input. SoftPanel may be utilized in both genomics research and personalized medicine.

Indexed as

User-Computer InterfaceArea Under CurveDatabases, FactualDiseaseExomeHumansInternetPhenotypePrecision MedicineROC CurveSupport Vector MachineGene prioritizationHuman disorder phenotype similarityTargeted panelWeb service

Identifiers

PMID27044653
PMCPMC4820874

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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.