Showing 6 of total 6 results (show query)
dcgerard
updog:Flexible Genotyping for Polyploids
Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.
Maintained by David Gerard. Last updated 1 years ago.
28 stars 8.45 score 83 scripts 2 dependents
swihart
rmutil:Utilities for Nonlinear Regression and Repeated Measurements Models
A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at <https://www.commanster.eu/rcode.html>).
Maintained by Bruce Swihart. Last updated 2 years ago.
1 stars 8.35 score 358 scripts 70 dependents
bioc
deepSNV:Detection of subclonal SNVs in deep sequencing data.
This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.
Maintained by Moritz Gerstung. Last updated 5 months ago.
geneticvariabilitysnpsequencinggeneticsdataimportcurlbzip2xz-utilszlibcpp
6.53 score 38 scripts 1 dependents
chgigot
epiphy:Analysis of Plant Disease Epidemics
A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) <doi:10.1094/9780890545058> for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package.
Maintained by Christophe Gigot. Last updated 1 years ago.
14 stars 6.02 score 37 scripts
