| Name : R-MHCtools
| |
| Version : 1.5.3
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.2
| Date : 2024-06-14 11:33:57
|
| Group : Development/Libraries/Other
| Source RPM : R-MHCtools-1.5.3-lp153.2.2.src.rpm
|
| Size : 0.24 MB
| |
| Packager : (none)
| |
| Summary : Analysis of MHC Data in Non-Model Species
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
