Dataset Information

Next generation sequencing data of a defined microbial mock community.

ABSTRACT: Generating sequence data of a defined community composed of organisms with complete reference genomes is indispensable for the benchmarking of new genome sequence analysis methods, including assembly and binning tools. Moreover the validation of new sequencing library protocols and platforms to assess critical components such as sequencing errors and biases relies on such datasets. We here report the next generation metagenomic sequence data of a defined mock community (Mock Bacteria ARchaea Community; MBARC-26), composed of 23 bacterial and 3 archaeal strains with finished genomes. These strains span 10 phyla and 14 classes, a range of GC contents, genome sizes, repeat content and encompass a diverse abundance profile. Short read Illumina and long-read PacBio SMRT sequences of this mock community are described. These data represent a valuable resource for the scientific community, enabling extensive benchmarking and comparative evaluation of bioinformatics tools without the need to simulate data. As such, these data can aid in improving our current sequence data analysis toolkit and spur interest in the development of new tools.

SUBMITTER: Singer E

PROVIDER: S-EPMC5037974 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Next generation sequencing data of a defined microbial mock community.

Singer Esther E Andreopoulos Bill B Bowers Robert M RM Lee Janey J Deshpande Shweta S Chiniquy Jennifer J Ciobanu Doina D Klenk Hans-Peter HP Zane Matthew M Daum Christopher C Clum Alicia A Cheng Jan-Fang JF Copeland Alex A Woyke Tanja T

Scientific data 20160927

Generating sequence data of a defined community composed of organisms with complete reference genomes is indispensable for the benchmarking of new genome sequence analysis methods, including assembly and binning tools. Moreover the validation of new sequencing library protocols and platforms to assess critical components such as sequencing errors and biases relies on such datasets. We here report the next generation metagenomic sequence data of a defined mock community (Mock Bacteria ARchaea Com ...[more]

PMID: 27673566

Similar Datasets

Project description:BACKGROUND: Renewable energy production is currently a major issue worldwide. Biogas is a promising renewable energy carrier as the technology of its production combines the elimination of organic waste with the formation of a versatile energy carrier, methane. In consequence of the complexity of the microbial communities and metabolic pathways involved the biotechnology of the microbiological process leading to biogas production is poorly understood. Metagenomic approaches are suitable means of addressing related questions. In the present work a novel high-throughput technique was tested for its benefits in resolving the functional and taxonomical complexity of such microbial consortia. RESULTS: It was demonstrated that the extremely parallel SOLiD™ short-read DNA sequencing platform is capable of providing sufficient useful information to decipher the systematic and functional contexts within a biogas-producing community. Although this technology has not been employed to address such problems previously, the data obtained compare well with those from similar high-throughput approaches such as 454-pyrosequencing GS FLX or Titanium. The predominant microbes contributing to the decomposition of organic matter include members of the Eubacteria, class Clostridia, order Clostridiales, family Clostridiaceae. Bacteria belonging in other systematic groups contribute to the diversity of the microbial consortium. Archaea comprise a remarkably small minority in this community, given their crucial role in biogas production. Among the Archaea, the predominant order is the Methanomicrobiales and the most abundant species is Methanoculleus marisnigri. The Methanomicrobiales are hydrogenotrophic methanogens. Besides corroborating earlier findings on the significance of the contribution of the Clostridia to organic substrate decomposition, the results demonstrate the importance of the metabolism of hydrogen within the biogas producing microbial community. CONCLUSIONS: Both microbiological diversity and the regulatory role of the hydrogen metabolism appear to be the driving forces optimizing biogas-producing microbial communities. The findings may allow a rational design of these communities to promote greater efficacy in large-scale practical systems. The composition of an optimal biogas-producing consortium can be determined through the use of this approach, and this systematic methodology allows the design of the optimal microbial community structure for any biogas plant. In this way, metagenomic studies can contribute to significant progress in the efficacy and economic improvement of biogas production.

Project description:Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample's library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

Project description:BackgroundThe human leukocyte antigen (HLA) region, the 3.8-Mb segment of the human genome at 6p21, has been associated with more than 100 different diseases, mostly autoimmune diseases. Due to the complex nature of HLA genes, there are difficulties in elucidating complete HLA gene sequences especially HLA gene haplotype structures by the conventional sequencing method. We propose a novel, accurate, and cost-effective method for generating phase-defined complete sequencing of HLA genes by using indexed multiplex next generation sequencing.ResultsA total of 33 HLA homozygous samples, 11 HLA heterozygous samples, and 3 parents-child families were subjected to phase-defined HLA gene sequencing. We applied long-range PCR to amplify six HLA genes (HLA-A, -C, -B, DRB1, -DQB1, and -DPB1) followed by transposase-based library construction and multiplex sequencing with the MiSeq sequencer. Paired-end reads (2 × 250 bp) derived from the sequencer were aligned to the six HLA gene segments of UCSC hg19 allowing at most 80 bases mismatch. For HLA homozygous samples, the six amplicons of an individual were pooled and simultaneously sequenced and mapped as an individual-tagging method. The paired-end reads were aligned to corresponding genes of UCSC hg19 and unambiguous, continuous sequences were obtained. For HLA heterozygous samples, each amplicon was separately sequenced and mapped as a gene-tagging method. After alignments, we detected informative paired-end reads harboring SNVs on both forward and reverse reads that are used to separate two chromosomes and to generate two phase-defined sequences in an individual. Consequently, we were able to determine the phase-defined HLA gene sequences from promoter to 3'-UTR and assign up to 8-digit HLA allele numbers, regardless of whether the alleles are rare or novel. Parent-child trio-based sequencing validated our sequencing and phasing methods.ConclusionsOur protocol generated phased-defined sequences of the entire HLA genes, resulting in high resolution HLA typing and new allele detection.

Dataset Information

Next generation sequencing data of a defined microbial mock community.

Publications

Next generation sequencing data of a defined microbial mock community.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets