Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Efficient association mapping from k-mers-An application in finding sex-specific sequences.

ABSTRACT: Genome wide association studies (GWAS) attempt to map genotypes to phenotypes in organisms. This is typically performed by genotyping individuals using microarray or by aligning whole genome sequencing reads to a reference genome. Both approaches require knowledge of a reference genome which hinders their application to organisms with no or incomplete reference genomes. This caveat can be removed by using alignment-free association mapping methods based on k-mers from sequencing reads. Here we present an improved implementation of an alignment free association mapping method. The new implementation is faster and includes additional features to make it more flexible than the original implementation. We have tested our implementation on an E. Coli ampicillin resistance dataset and observe improvement in execution time over the original implementation while maintaining accuracy in results. We also demonstrate that the method can be applied to find sex specific sequences.

SUBMITTER: Mehrab Z

PROVIDER: S-EPMC7790365 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Efficient association mapping from k-mers-An application in finding sex-specific sequences.

Mehrab Zakaria Z Mobin Jaiaid J Tahmid Ibrahim Asadullah IA Rahman Atif A

PloS one 20210107 1

Genome wide association studies (GWAS) attempt to map genotypes to phenotypes in organisms. This is typically performed by genotyping individuals using microarray or by aligning whole genome sequencing reads to a reference genome. Both approaches require knowledge of a reference genome which hinders their application to organisms with no or incomplete reference genomes. This caveat can be removed by using alignment-free association mapping methods based on k-mers from sequencing reads. Here we p ...[more]

PMID: 33411830

Similar Datasets

An efficient strategy using k-mers to analyse 16S rRNA sequences.

Project description:The use of k-mers has been a successful strategy for improving metagenomics studies, including taxonomic classifications, or de novo assemblies, and can be used to obtain sequences of interest from the available databases. The aim of this manuscript was to propose a simple but efficient strategy to generate k-mers and to use them to obtain and analyse in silico 16S rRNA sequence fragments. A total of 513,309 bacterial sequences contained in the SILVA database were considered for the study, and homemade PHP scripts were used to search for specific nucleotide chains, recover fragments of bacterial sequences, make calculations and organize information. Consensus sequences matching conserved regions were constructed by aligning most of the primers used in the literature. Sequences of k nucleotides (9- to 15-mers) were extracted from the generated primer contigs. Frequency analysis revealed that k-mer size was inversely proportional to the occurrence of k-mers in the different conserved regions, suggesting a stringency relationship; high numbers of duplicate reactions were observed with short k-mers, and a lower proportion of sequences were obtained with large ones, with the best results obtained using 12-mers. Using 12-mers with the proposed method to obtain and study sequences was found to be a reliable approach for the analysis of 16S rRNA sequences and this strategy may probably be extended to other biomarkers. Furthermore, additional applications such as evaluating the degree of conservation and designing primers and other calculations are proposed as examples.

| S-EPMC5537200 | biostudies-other

Association mapping from sequencing reads using k-mers.

Project description:Genome wide association studies (GWAS) rely on microarrays, or more recently mapping of sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome limits the scope of association studies, and also precludes mapping associations outside of the reference. We present an alignment free method for association studies of categorical phenotypes based on counting [Formula: see text]-mers in whole-genome sequencing reads, testing for associations directly between [Formula: see text]-mers and the trait of interest, and local assembly of the statistically significant [Formula: see text]-mers to identify sequence differences. An analysis of the 1000 genomes data show that sequences identified by our method largely agree with results obtained using the standard approach. However, unlike standard GWAS, our method identifies associations with structural variations and sites not present in the reference genome. We also demonstrate that population stratification can be inferred from [Formula: see text]-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach.

| S-EPMC6044908 | biostudies-literature

Efficient counting of k-mers in DNA sequences using a bloom filter.

Project description:Counting k-mers (substrings of length k in DNA sequence data) is an essential component of many methods in bioinformatics, including for genome and transcriptome assembly, for metagenomic sequencing, and for error correction of sequence reads. Although simple in principle, counting k-mers in large modern sequence data sets can easily overwhelm the memory capacity of standard computers. In current data sets, a large fraction-often more than 50%-of the storage capacity may be spent on storing k-mers that contain sequencing errors and which are typically observed only a single time in the data. These singleton k-mers are uninformative for many algorithms without some kind of error correction.We present a new method that identifies all the k-mers that occur more than once in a DNA sequence data set. Our method does this using a Bloom filter, a probabilistic data structure that stores all the observed k-mers implicitly in memory with greatly reduced memory requirements. We then make a second sweep through the data to provide exact counts of all nonunique k-mers. For example data sets, we report up to 50% savings in memory usage compared to current software, with modest costs in computational speed. This approach may reduce memory requirements for any algorithm that starts by counting k-mers in sequence data with errors.A reference implementation for this methodology, BFCounter, is written in C++ and is GPL licensed. It is available for free download at http://pritch.bsd.uchicago.edu/bfcounter.html.

| S-EPMC3166945 | biostudies-literature

Reference-free Association Mapping from Sequencing Reads Using k-mers.

Project description:Association mapping is the process of linking phenotypes with genotypes. In genome wide association studies (GWAS), individuals are first genotyped using microarrays or by aligning sequenced reads to reference genomes. However, both these approaches rely on reference genomes which limits their application to organisms with no or incomplete reference genomes. To address this, reference free association mapping methods have been developed. Here we present the protocol of an alignment free method for association studies which is based on counting k-mers in sequenced reads, testing for associations between k-mers and the phenotype of interest, and local assembly of the k-mers of statistical significance. The method can map associations of categorical phenotypes to sequence and structural variations without requiring prior sequencing of reference genomes.

| S-EPMC7842384 | biostudies-literature

Application of sex-specific single-nucleotide polymorphism filters in genome-wide association data.

Project description:We explored five sex-specific quality control filters in North American Rheumatoid Arthritis Consortium's Illumina 550 k datasets. Three X chromosome and three autosomal single-nucleotide polymorphisms flagged by sex quality control filters were missed by filters of call rate at 95% and Hardy-Weinberg equilibrium at 10-6. We applied a subset of these sex-specific quality control filters to eight chromosomes in the Framingham Heart Study samples genotyped by Affymetrix 500 k SNP arrays, and identified another two single-nucleotide polymorphisms that failed to be picked up by the above global filters.

| S-EPMC2795957 | biostudies-literature

KmerGO: A Tool to Identify Group-Specific Sequences With <i>k</i>-mers.

Project description:Capturing group-specific sequences between two groups of genomic/metagenomic sequences is critical for the follow-up identifications of singular nucleotide variants (SNVs), gene families, microbial species or other elements associated with each group. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered a "group-specific" sequence in our study. We developed a user-friendly tool, KmerGO, to identify group-specific sequences between two groups of genomic/metagenomic long sequences or high-throughput sequencing datasets. Compared with other tools, KmerGO captures group-specific k-mers (k up to 40 bps) with much lower requirements for computing resources in much shorter running time. For a 1.05 TB dataset (.fasta), it takes KmerGO about 21.5 h (including k-mer counting) to return assembled group-specific sequences on a regular stand-alone workstation with no more than 1 GB memory. Furthermore, KmerGO can also be applied to capture trait-associated sequences for continuous-trait. Through multi-process parallel computing, KmerGO is implemented with both graphic user interface and command line on Linux and Windows free from any pre-installed supporting environments, packages, and complex configurations. The output group-specific k-mers or sequences from KmerGO could be the inputs of other tools for the downstream discovery of biomarkers, such as genetic variants, species, or genes. KmerGO is available at https://github.com/ChnMasterOG/KmerGO.

| S-EPMC7477287 | biostudies-literature

Gene finding in metatranscriptomic sequences.

Project description:BackgroundMetatranscriptomic sequencing is a highly sensitive bioassay of functional activity in a microbial community, providing complementary information to the metagenomic sequencing of the community. The acquisition of the metatranscriptomic sequences will enable us to refine the annotations of the metagenomes, and to study the gene activities and their regulation in complex microbial communities and their dynamics.ResultsIn this paper, we present TransGeneScan, a software tool for finding genes in assembled transcripts from metatranscriptomic sequences. By incorporating several features of metatranscriptomic sequencing, including strand-specificity, short intergenic regions, and putative antisense transcripts into a Hidden Markov Model, TranGeneScan can predict a sense transcript containing one or multiple genes (in an operon) or an antisense transcript.ConclusionWe tested TransGeneScan on a mock metatranscriptomic data set containing three known bacterial genomes. The results showed that TranGeneScan performs better than metagenomic gene finders (MetaGeneMark and FragGeneScan) on predicting protein coding genes in assembled transcripts, and achieves comparable or even higher accuracy than gene finders for microbial genomes (Glimmer and GeneMark). These results imply, with the assistance of metatranscriptomic sequencing, we can obtain a broad and precise picture about the genes (and their functions) in a microbial community.AvailabilityTransGeneScan is available as open-source software on SourceForge at https://sourceforge.net/projects/transgenescan/.

| S-EPMC4168707 | biostudies-literature

Efficient association mapping of quantitative trait loci with selective genotyping.

Project description:Selective genotyping (i.e., genotyping only those individuals with extreme phenotypes) can greatly improve the power to detect and map quantitative trait loci in genetic association studies. Because selection depends on the phenotype, the resulting data cannot be properly analyzed by standard statistical methods. We provide appropriate likelihoods for assessing the effects of genotypes and haplotypes on quantitative traits under selective-genotyping designs. We demonstrate that the likelihood-based methods are highly effective in identifying causal variants and are substantially more powerful than existing methods.

| S-EPMC1821103 | biostudies-other

Isolation of Y chromosome-specific sequences from Silene latifolia and mapping of male sex-determining genes using representational difference analysis.

Project description:The genomic subtraction method representational difference analysis (RDA) was used to identify male-specific restriction fragments in the dioecious plant Silene latifolia. Male-specific restriction fragments are linked to the male sex chromosome (the Y chromosome). Four RDA-derived male-specific restriction fragments were used to identify polymorphisms in a collection of X-ray-generated mutant plants with either hermaphroditic or asexual flowers. Some of the mutants have cytologically detectable deletions in the Y chromosome that were correlated with loss of male-specific restriction fragments. One RDA-derived probe detected a restriction fragment present in all mutants, indicating that each has retained Y chromosomal DNA. The other three probes detected genomic fragments that were linked in a region deleted in some hermaphroditic and some asexual mutants. Based on the mutant phenotypes and the correlation of cytologically visible deletions with loss of male-specific restriction fragments, these markers were assigned to positions on the Y chromosome close to the carpel suppression locus. This RDA mapping also revealed a Y-linked locus, not previously described, which is responsible for early stamen development.

| S-EPMC1207736 | biostudies-other

MERS coronavirus nsp1 participates in an efficient propagation through a specific interaction with viral RNA.

Project description:MERS-CoV is the only lethal human CoV still endemic in the Arabian Peninsula and neither vaccine nor therapeutics against MERS-CoV infection is available. The nsp1 of CoV is thought to be a major virulence factor because it suppresses protein synthesis through the degradation of host mRNA. In contrast, viral RNA circumvents the nsp1-mediated translational shutoff for an efficient propagation. In this study, we identified amino acid residue in MERS-CoV nsp1 that differ from those of SARS-CoV nsp1, and that appear to be crucial for circumventing the translational shutoff. In addition, reverse genetics analysis suggested the presence of a cis-acting element at the 5'-terminus of the nsp1-coding region, which contributes to the specific recognition of viral RNA that is required for an efficient viral replication. Our results suggest the CoVs share a common mechanism for circumventing the nsp1-mediated translational shutoff.

| S-EPMC7118922 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data