MHCtools - Analysis of MHC Data in Non-Model Species
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
<https://benjjneb.github.io/dada2/> ), 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.