Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Short-read fastA files dataset from complexity-reduced genotyping by sequencing data of bacterial isolates from a public hospital in Australia.

ABSTRACT: This data article contains short-read sequences (length 30-69 bp) obtained from complexity-reduced genotyping by sequencing (GBS) of 165 samples bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015. These samples represented 14 bacterial species. Data format is shown as filtered fastA files obtained from an Illumina HiSeq2500 sequencer. The experimental factors of this research used three complexity reduction methods with three combinations of restriction enzymes: PstI with MseI, PstI with HpaII and MseI with HpaII.

SUBMITTER: Talamantes-Becerra B

PROVIDER: S-EPMC6661468 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Short-read fastA files dataset from complexity-reduced genotyping by sequencing data of bacterial isolates from a public hospital in Australia.

Talamantes-Becerra Berenice B Carling Jason J Kennedy Karina K Gahan Michelle E ME Georges Arthur A

Data in brief 20190716

This data article contains short-read sequences (length 30-69 bp) obtained from complexity-reduced genotyping by sequencing (GBS) of 165 samples bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015. These samples represented 14 bacterial species. Data format is shown as filtered fastA files obtained from an Illumina HiSeq2500 sequencer. The experimental factors of this research used three complexity reduction methods with three combinations of rest ...[more]

PMID: 31384653

Similar Datasets

FASTAFS: file system virtualisation of random access compressed FASTA files.

Project description:BackgroundThe FASTA file format, used to store polymeric sequence data, has become a bioinformatics file standard used for decades. The relatively large files require additional files, beyond the scope of the original format, to identify sequences and to provide random access. Multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration.ResultsWe designed a linux based toolkit that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem by using filesystem in userspace. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using bit encodings plus Zstandard (zstd). The toolkit, FASTAFS, can track all its system-wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. The file compression ratios were comparable but not superior to other state of the art archival tools, despite the innovative random access feature implemented in FASTAFS.ConclusionsFASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers format conversion without the need to rewrite code into different programming languages while preserving compatibility.

| S-EPMC8558547 | biostudies-literature

Read files

Project description:10k Salmonella Project

| PRJEB35182 | ENA

Identification of high-efficiency 3'GG gRNA motifs in indexed FASTA files with ngg2.

Project description:CRISPR/Cas9 is emerging as one of the most-used methods of genome modification in organisms ranging from bacteria to human cells. However, the efficiency of editing varies tremendously site-to-site. A recent report identified a novel motif, called the 3'GG motif, which substantially increases the efficiency of editing at all sites tested in C. elegans. Furthermore, they highlighted that previously published gRNAs with high editing efficiency also had this motif. I designed a python command-line tool, ngg2, to identify 3'GG gRNA sites from indexed FASTA files. As a proof-of-concept, I screened for these motifs in six model genomes: Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus, and Homo sapiens. I also scanned the genomes of pig (Sus scrofa) and African elephant (Loxodonta africana) to demonstrate the utility in non-model organisms. I identified more than 60 million single match 3'GG motifs in these genomes. Greater than 61% of all protein coding genes in the reference genomes had at least one unique 3'GG gRNA site overlapping an exon. In particular, more than 96% of mouse and 93% of human protein coding genes have at least one unique, overlapping 3'GG gRNA. These identified sites can be used as a starting point in gRNA selection, and the ngg2 tool provides an important ability to identify 3'GG editing sites in any species with an available genome sequence.

| S-EPMC4750479 | biostudies-literature

BigSeqKit: a parallel Big Data toolkit to process FASTA and FASTQ files at scale.

Project description:BackgroundHigh-throughput sequencing technologies have led to an unprecedented explosion in the amounts of sequencing data available, which are typically stored using FASTA and FASTQ files. We can find in the literature several tools to process and manipulate those type of files with the aim of transforming sequence data into biological knowledge. However, none of them are well fitted for processing efficiently very large files, likely in the order of terabytes in the following years, since they are based on sequential processing. Only some routines of the well-known seqkit tool are partly parallelized. In any case, its scalability is limited to use few threads on a single computing node.ResultsOur approach, BigSeqKit, takes advantage of a high-performance computing-Big Data framework to parallelize and optimize the commands included in seqkit with the aim of speeding up the manipulation of FASTA/FASTQ files. In this way, in most cases, it is from tens to hundreds of times faster than several state-of-the-art tools. At the same time, our toolkit is easy to use and install on any kind of hardware platform (local server or cluster), and its routines can be used as a bioinformatics library or from the command line.ConclusionsBigSeqKit is a very complete and ultra-fast toolkit to process and manipulate large FASTA and FASTQ files. It is publicly available at https://github.com/citiususc/BigSeqKit.

| S-EPMC10388699 | biostudies-literature

Reduced complexity

Project description:Identification of RCO targets using ChIP-seq

| PRJEB29759 | ENA

Robust FCS Parsing: Exploring 211,359 Public Files.

Project description:When it comes to data storage, the field of flow cytometry is fairly standardized, thanks to the flow cytometry standard (FCS) file format. The structure of FCS files is described in the FCS specification. Software that strictly complies with the FCS specification is guaranteed to be interoperable (in terms of exchange via FCS files). Nowadays, software interoperability is crucial for eco system, as FCS files are frequently shared, and workflows rely on more than one piece of software (e.g., acquisition and analysis software). Ideally, software developers strictly follow the FCS specification. Unfortunately, this is not always the case, which resulted in various nonconformant FCS files being generated over time. Therefore, robust FCS parsers must be developed, which can handle a wide variety of nonconformant FCS files, from different resources. Development of robust FCS parsers would greatly benefit from a fully fledged set of testing files. In this study, readability of 211,359 public FCS files was evaluated. Each FCS file was checked for conformance with the FCS specification. For each data set, within each FCS file, validated parse results were obtained for the TEXT segment. Highly space efficient testing files were generated. FlowCore was benchmarked in depth, by using the validated parse results, the generated testing files, and the original FCS files. Robustness of FlowCore (as measured by testing against 211,359 files) was improved by re-implementing the TEXT segment parser. Altogether, this study provides a comprehensive resource for FCS parser development, an in-depth benchmark of FlowCore, and a concrete proposal for improving FlowCore. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.

| S-EPMC7754493 | biostudies-literature

VCF2CAPS-A high-throughput CAPS marker design from VCF files and its test-use on a genotyping-by-sequencing (GBS) dataset.

Project description:Next-generation sequencing (NGS) is a powerful tool for massive detection of DNA sequence variants such as single nucleotide polymorphisms (SNPs), multi-nucleotide polymorphisms (MNPs) and insertions/deletions (indels). For routine screening of numerous samples, these variants are often converted into cleaved amplified polymorphic sequence (CAPS) markers which are based on the presence versus absence of restriction sites within PCR products. Current computational tools for SNP to CAPS conversion are limited and usually infeasible to use for large datasets as those generated with NGS. Moreover, there is no available tool for massive conversion of MNPs and indels into CAPS markers. Here, we present VCF2CAPS-a new software for identification of restriction endonucleases that recognize SNP/MNP/indel-containing sequences from NGS experiments. Additionally, the program contains filtration utilities not available in other SNP to CAPS converters-selection of markers with a single polymorphic cut site within a user-specified sequence length, and selection of markers that differentiate up to three user-defined groups of individuals from the analyzed population. Performance of VCF2CAPS was tested on a thoroughly analyzed dataset from a genotyping-by-sequencing (GBS) experiment. A selection of CAPS markers picked by the program was subjected to experimental verification. CAPS markers, also referred to as PCR-RFLPs, belong to basic tools exploited in plant, animal and human genetics. Our new software-VCF2CAPS-fills the gap in the current inventory of genetic software by high-throughput CAPS marker design from next-generation sequencing (NGS) data. The program should be of interest to geneticists involved in molecular diagnostics. In this paper we show a successful exemplary application of VCF2CAPS and we believe that its usefulness is guaranteed by the growing availability of NGS services.

| S-EPMC8186816 | biostudies-literature

biobambam: tools for read pair collation based algorithms on BAM files

Project description:Background Sequence alignment data is often ordered by coordinate (id of the reference sequence plus position on the sequence where the fragment was mapped) when stored in BAM files, as this simplifies the extraction of variants between the mapped data and the reference or of variants within the mapped data. In this order paired reads are usually separated in the file, which complicates some other applications like duplicate marking or conversion to the FastQ format which require to access the full information of the pairs. Results In this paper we introduce biobambam, a set of tools based on the efficient collation of alignments in BAM files by read name. The employed collation algorithm avoids time and space consuming sorting of alignments by read name where this is possible without using more than a specified amount of main memory. Using this algorithm tasks like duplicate marking in BAM files and conversion of BAM files to the FastQ format can be performed very efficiently with limited resources. We also make the collation algorithm available in the form of an API for other projects. This API is part of the libmaus package. Conclusions In comparison with previous approaches to problems involving the collation of alignments by read name like the BAM to FastQ or duplication marking utilities our approach can often perform an equivalent task more efficiently in terms of the required main memory and run-time. Our BAM to FastQ conversion is faster than all widely known alternatives including Picard and bamUtil. Our duplicate marking is about as fast as the closest competitor bamUtil for small data sets and faster than all known alternatives on large and complex data sets.

| S-EPMC4075596 | biostudies-literature

VLA lyssavirus genotyping dataset

Project description:Data from the VLA lyssavirus genotyping microarray. The array platform for this data is GEO accession GPL8066, and consists of 624 oligos representing two viral families. The data set itself consists of 14 arrays, 7 hybridised with RNA from mice brains infected with 7 genotypes of lyssaviruses, 1 hybridised with RNA from normal mouse brain, and 6 hybridised with RNA from coded samples consisting of infected mouse brains or control mouse brains. Keywords: Lyssavirus genotyping microarray

2009-06-09 | GSE14705 | GEO

mzML files of metabolomics data from Metabolights dataset MTBLS162

Project description: Not available

| S-BSST400 | biostudies-other