ArticlePloS one2012
A model-based clustering method for genomic structural variant prediction and genotyping using paired-end sequencing data.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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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.
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Who cites it
8 citing papers in PubMed.
- Progress in Methods for Copy Number Variation Profiling.International journal of molecular sciences · 2022Review
- Current status of structural variation studies in plants.Plant biotechnology journal · 2021Review
- Comprehensive evaluation and characterisation of short read general-purpose structural variant calling software.Nature communications · 2019Article
- Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model.PloS one · 2016Article
- SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines.BMC genomics · 2015Article
- Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and perspectives.Oncotarget · 2015Review
- Identification of copy number variants in whole-genome data using Reference Coverage Profiles.Frontiers in genetics · 2015Article
- Bellerophon: a hybrid method for detecting interchromosomal rearrangements at base pair resolution using next-generation sequencing data.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from http://cbc.case.edu/svminer/.
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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.