ArticleBioData mining2016
A biologically informed method for detecting rare variant associations.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.Frontiers in genetics · 2020Article
- Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico.BMC bioinformatics · 2019Article
- Exome-Wide Rare Variant Analysis From the DiscovEHR Study Identifies Novel Candidate Predisposition Genes for Endometrial Cancer.Frontiers in oncology · 2019Article
- Rare variants in drug target genes contributing to complex diseases, phenome-wide.Scientific reports · 2018Article
- Novel features and enhancements in BioBin, a tool for the biologically inspired binning and association analysis of rare variants.Bioinformatics (Oxford, England) · 2018Article
- Novel CYP2A6 diplotypes identified through next-generation sequencing are associated with in-vitro and in-vivo nicotine metabolism.Pharmacogenetics and genomics · 2018Article
- Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease.BMC medical informatics and decision making · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundBioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting. PURPOSE OF THE STUDY: In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool.
resultsSimulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data. CONCLUSIONS AND POTENTIAL IMPLICATIONS: The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download.
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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.