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message: 'To cite package "MHCtools" in publications use:'
type: software
license: MIT
title: 'MHCtools: Analysis of MHC Data in Non-Model Species'
version: 1.5.3
abstract: Fifteen tools for bioinformatics processing and analysis of major histocompatibility
complex (MHC) data. The functions are tailored for amplicon data sets that have
been filtered using the dada2 method (for more information on dada2, visit
), but even other types of data sets can be analyzed. The ReplMatch() function matches
replicates in data sets in order to evaluate genotyping success. The GetReplTable()
and GetReplStats() functions perform such an evaluation. The CreateFas() function
creates a fasta file with all the sequences in the data set. The CreateSamplesFas()
function creates individual fasta files for each sample in the data set. The DistCalc()
function calculates Grantham, Sandberg, or p-distances from pairwise comparisons
of all sequences in a data set, and mean distances of all pairwise comparisons within
each sample in a data set. The function additionally outputs five tables with physico-chemical
z-descriptor values (based on Sandberg et al. 1998) for each amino acid position
in all sequences in the data set. These tables may be useful for further downstream
analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper
for the kmeans() function of the 'stats' package, which allows for bootstrapping.
Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values
do not produce clear inflection points ("elbows"). BootKmeans() performs multiple
runs of kmeans() and estimates optimal k-values based on a user-defined threshold
of BIC reduction. The method is an automated and bootstrapped version of visually
inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool
for evaluating whether different k-means() clustering models identify similar clusters,
and summarize bootstrap model stats as means for different estimated values of k.
It is designed to take files produced by the BootKmeans() function as input, but
other data can be analysed if the descriptions of the required data formats are
observed carefully. The PapaDiv() function compares parent pairs in the data set
and calculate their joint MHC diversity, taking into account sequence variants that
occur in both parents. The HpltFind() function infers putative haplotypes from families
in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy
of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical)
haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview
of which sequences are present in which haplotypes. The HpltMatch() function compares
haplotypes to help identify overlapping and potentially identical types. The NestTablesXL()
function translates the output from HpltFind() to an Excel workbook, that provides
a convenient overview for evaluation and curating of the inferred putative haplotypes.
authors:
- family-names: Roved
given-names: Jacob
email: jacob.roved@biol.lu.se
repository: https://CRAN.R-project.org/package=MHCtools
date-released: '2023-07-08'
contact:
- family-names: Roved
given-names: Jacob
email: jacob.roved@biol.lu.se