Dataset Information

A Kmer-based paired-end read de novo assembler and genotyper for canine MHC class I genotyping.

ABSTRACT: The major histocompatibility complex class I (MHC-I) genes are highly polymorphic. MHC-I genotyping is required for determining the peptide epitopes available to an individual's T-cell repertoire. Current genotyping software tools do not work for the dog, due to very limited known canine alleles. To address this, we developed a Kmer-based paired-end read (KPR) de novo assembler and genotyper, which assemble paired-end RNA-seq reads from MHC-I regions into contigs, and then genotype each contig and estimate its expression level. KPR tools outperform other popular software examined in typing new alleles. We used KPR tools to successfully genotype152 dogs from a published dataset. The study discovers 33 putative new alleles, finds dominant alleles in 4 dog breeds, and builds allele diversity and expression landscapes among the 152 dogs. Our software meets a significant need in biomedical research.

SUBMITTER: Feng Y

PROVIDER: S-EPMC9926114 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Kmer-based paired-end read <i>de novo</i> assembler and genotyper for canine MHC class I genotyping.

Feng Yuan Y Hess Paul R PR Tompkins Stephen M SM Hildebrand William H WH Zhao Shaying S

iScience 20230116 2

The major histocompatibility complex class I (MHC-I) genes are highly polymorphic. MHC-I genotyping is required for determining the peptide epitopes available to an individual's T-cell repertoire. Current genotyping software tools do not work for the dog, due to very limited known canine alleles. To address this, we developed a Kmer-based paired-end read (KPR) <i>de novo</i> assembler and genotyper, which assemble paired-end RNA-seq reads from MHC-I regions into contigs, and then genotype each c ...[more]

PMID: 36798440

Similar Datasets

Project description:Since the read lengths of high throughput sequencing (HTS) technologies are short, de novo assembly which plays significant roles in many applications remains a great challenge. Most of the state-of-the-art approaches base on de Bruijn graph strategy and overlap-layout strategy. However, these approaches which depend on k-mers or read overlaps do not fully utilize information of paired-end and single-end reads when resolving branches. Since they treat all single-end reads with overlapped length larger than a fix threshold equally, they fail to use the more confident long overlapped reads for assembling and mix up with the relative short overlapped reads. Moreover, these approaches have not been special designed for handling tandem repeats (repeats occur adjacently in the genome) and they usually break down the contigs near the tandem repeats. We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from Omax to Omin to resolve the gaps and branches. By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. When the correct extension cannot be determined, PERGA will try to extend the contig by all feasible extensions and determine the correct extension by using look-ahead approach. Many difficult-resolved branches are due to tandem repeats which are close in the genome. PERGA detects such different copies of the repeats to resolve the branches to make the extension much longer and more accurate. We evaluated PERGA on both Illumina real and simulated datasets ranging from small bacterial genomes to large human chromosome, and it constructed longer and more accurate contigs and scaffolds than other state-of-the-art assemblers. PERGA can be freely downloaded at https://github.com/hitbio/PERGA.

Project description:BackgroundThe challenges when developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize a de Bruijn graph, with which complexity grows linearly with data size while suffering from errors and repeats. Although one can correct the errors by inspecting the topological structure of the graph, this is not an easy task when there are too many branches. Two research directions are to improve either the graph reliability or the path search precision, and in this study, we focused on the former.ResultsWe present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. In the proposed approach, we built contigs by direct read alignment within a distance margin and performed a junction search to construct splicing graphs. While doing so, a contig of length l was represented by a 4 × l matrix (called a consensus matrix), in which each element was the base count of the aligned reads so far. A representative sequence was obtained by taking the majority in each column of the consensus matrix to be used for further read alignment. Once the splicing graphs had been obtained, we used IsoLasso to find paths with a noticeable read depth. The experiments using real and simulated reads show that the method provided considerable improvement in sensitivity and moderately better performance when comparing sensitivity and precision. This was achieved by the error-aware graph construction using the consensus matrix, with which the reads having errors were made usable for the graph construction (otherwise, they might have been eventually discarded). This improved the quality of the coverage depth information used in the subsequent path search step and finally the reliability of the graph.ConclusionsDe novo assembly is mainly used to explore undiscovered isoforms and must be able to represent as many reads as possible in an efficient way. In this sense, TraRECo provides us with a potential alternative for improving graph reliability even though the computational burden is much higher than the single k-mer in the de Bruijn graph approach.

Dataset Information

A Kmer-based paired-end read de novo assembler and genotyper for canine MHC class I genotyping.

Publications

A Kmer-based paired-end read <i>de novo</i> assembler and genotyper for canine MHC class I genotyping.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets