DOI: 10.5061/dryad.4fn4g
title: Data from: Stepwise Threshold Clustering: a new method for genotyping MHC loci using next-generation sequencing technology

description: Genes of the vertebrate major histocompatibility complex (MHC) are of
 great interest to biologists because of their important role in immunity
 and disease, and their extremely high levels of genetic diversity. Next
 generation sequencing (NGS) technologies are quickly becoming the method
 of choice for high-throughput genotyping of multi-locus templates like MHC
 in non-model organisms. Previous approaches to genotyping MHC genes using
 NGS technologies suffer from two problems: 1) a “gray zone” where low
 frequency alleles and high frequency artifacts can be difficult to
 disentangle and 2) a similar sequence problem, where very similar alleles
 can be difficult to distinguish as two distinct alleles. Here were present
 a new method for genotyping MHC loci – Stepwise Threshold Clustering (STC)
 – that addresses these problems by taking full advantage of the increase
 in sequence data provided by NGS technologies. Unlike previous approaches
 for genotyping MHC with NGS data that attempt to classify individual
 sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering
 algorithm to cluster similar sequences at increasing levels of sequence
 similarity. By applying frequency and similarity based criteria to
 clusters rather than individual sequences, STC is able to successfully
 identify clusters of sequences that correspond to individual or similar
 alleles present in the genomes of individual samples. Furthermore, STC
 does not require duplicate runs of all samples, increasing the number of
 samples that can be genotyped in a given project. We show how the STC
 method works using a single sample library. We then apply STC to 295
 threespine stickleback (Gasterosteus aculeatus) samples from four
 populations and show that neighboring populations differ significantly in
 MHC allele pools. We show that STC is a reliable, accurate, efficient, and
 flexible method for genotyping MHC that will be of use to biologists
 interested in a variety of downstream applications.
description: shell script to exectue
 commands for STC genotypingrunall.shR script for performing STC genotypingstc.Rperl script for demultiplexing raw read datareadFNA.plChimeras functionschimeras_functions.RR script to create 'barcodes' file for de-multiplexingNOTE: This file is part of the original version of this data package. It is superfluous to the new version.make_barcodes.RR script to create "sequences" file for de-multiplexingmake_sequences.Rfunctions for stc.Rstc_functions.RFASTA format file of raw sequence reads used to generate dataset described in the paperThis file contains the raw-read data from the 454 sequencing run used to produce the results in the paper. It can be used as input for readFNA.pl. Please see README.txt for more information.reads.fnaempty fasta format file for de-multiplexingNeeded for de-multiplexing using readFNA.pl.master.fastaFASTA file of all unique amplicon sequences found in reads.fnaContains all the unique amplicon sequences (no primers) along with numeric identifiers. This file is created from the output of readFNA.pl and make_sequences.R. It can be used as input for stc.R. Please see the README.txt file for more information.sequences.fastatable of samples and barcode IDs used in for genotypingContains rows designating the sample ID numbers and forward and reverse barcode ID numbers used for each sample genotyped in the paper. See barcode_sequences.csv for barcode nucleotide sequences. This file can be used as input for stc.R. Please see README.txt for more information and meta data.sample_barcodes.csvtable of nucleotide sequences used as barcodesContains the sequences of all barcodes used to genotype samples described in the paper. Numeric ID numbers correspond to the ID numbers in samples_barcodes.csv. Can be used as input to the 'make_barcodes.R" script to create a list of barcode sequences for de-multiplexing. Please see README.txt for more information.barcode_sequences.csvtable of processed reads from 454 sequencing runContains the output of running the readFNA.pl script on the included reads.fna input file. Output includes barcode IDs, seuqence IDs and indicates to which sample each read was assigned. Please see the README.txt file for meta-data and more information.raw_reads.csvpost-genotyping data set used for analysesContains the output of stc.R after processing the samples described in the paper. Data includes the number of alleles for each sample and the presence/absence of each unique sequence within each sample. Please see the README.txt file for more information and meta-datafinal_data.csvREADME file for STC genotyping