ArticleBioinformatics (Oxford, England)2012
SNP calling using genotype model selection on high-throughput sequencing data.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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
13 citing papers in PubMed.
- A high-throughput SNP discovery strategy for RNA-seq data.BMC genomics · 2019Article
- Review
- Single-cell SNP analyses and interpretations based on RNA-Seq data for colon cancer research.Scientific reports · 2016Article
- SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.BMC systems biology · 2016Article
- Monovar: single-nucleotide variant detection in single cells.Nature methods · 2016Article
- MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.Bioinformatics (Oxford, England) · 2016Article
- An investigation of causes of false positive single nucleotide polymorphisms using simulated reads from a small eukaryote genome.BMC bioinformatics · 2015Article
- A hidden Markov approach for ascertaining cSNP genotypes from RNA sequence data in the presence of allelic imbalance by exploiting linkage disequilibrium.BMC bioinformatics · 2015Article
- SNPest: a probabilistic graphical model for estimating genotypes.BMC research notes · 2014Article
- From genes to health - challenges and opportunities.Frontiers in pediatrics · 2014Review
- A primer for disease gene prioritization using next-generation sequencing data.Genomics & informatics · 2013Review
- Coval: improving alignment quality and variant calling accuracy for next-generation sequencing data.PloS one · 2013Article
- MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data.F1000Research · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
motivationA review of the available single nucleotide polymorphism (SNP) calling procedures for Illumina high-throughput sequencing (HTS) platform data reveals that most rely mainly on base-calling and mapping qualities as sources of error when calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.
resultsA novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts for the errors that occur during the preparation of the genomic sample. Simulations and real data analyses indicate that GeMS has the best performance balance of sensitivity and positive predictive value among the tested SNP callers. AVAILABILITY: The GeMS package can be downloaded from https://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software or http://computationalbioenergy.org/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
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.