Dataset Information

MetaID: a novel method for identification and quantification of metagenomic samples.

ABSTRACT: Advances in next-generation sequencing (NGS) technology has provided us with an opportunity to analyze and evaluate the rich microbial communities present in all natural environments. The shorter reads obtained from the shortgun technology has paved the way for determining the taxonomic profile of a community by simply aligning the reads against the available reference genomes. While several computational methods are available for taxonomic profiling at the genus- and species-level, none of these methods are effective at the strain-level identification due to the increasing difficulty in detecting variation at that level. Here, we present MetaID, an alignment-free n-gram based approach that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample, given a metagenomic sequencing dataset.MetaID is an n-gram based method that calculates the profile of unique and common n-grams from the dataset of 2,031 prokaryotic genomes and assigns weights to each n-gram using a scoring function. This scoring function assigns higher weightage to the n-grams that appear in fewer genomes and vice versa; thus, allows for effective use of both unique and common n-grams for species identification. Our 10-fold cross-validation results on a simulated dataset show a remarkable accuracy of 99.7% at the strain-level identification of the organisms in gut microbiome. We also demonstrated that our model shows impressive performance even by using only 25% or 50% of the genome sequences for modeling. In addition to identification of the species, our method can also estimate the relative abundance of each species in the simulated metagenomic samples. The generic approach employed in this method can be applied for accurate identification of a wide variety of microbial species (viruses, prokaryotes and eukaryotes) present in any environmental sample.The proposed scoring function and approach is able to accurately identify and estimate the entire taxa in any metagenomic community. The weights assigned to the common n-grams by our scoring function are precisely calibrated to match the reads up to the strain level. Our multipronged validation tests demonstrate that MetaID is sufficiently robust to accurately identify and estimate the abundance of each taxon in any natural environment even when using incomplete or partially sequenced genomes.

SUBMITTER: Srinivasan SM

PROVIDER: S-EPMC4042266 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MetaID: a novel method for identification and quantification of metagenomic samples.

Srinivasan Satish M SM Guda Chittibabu C

BMC genomics 20131209

<h4>Background</h4>Advances in next-generation sequencing (NGS) technology has provided us with an opportunity to analyze and evaluate the rich microbial communities present in all natural environments. The shorter reads obtained from the shortgun technology has paved the way for determining the taxonomic profile of a community by simply aligning the reads against the available reference genomes. While several computational methods are available for taxonomic profiling at the genus- and species- ...[more]

PMID: 24564518

Similar Datasets

Project description:BackgroundThe number of members in the genus Gammapapillomavirus of Family Papillomaviridae has recently been expanding most rapidly. The aim of this study was to characterize a novel human gammapapillomavirus type identified in a vaginal swab from a 25-year-old pregnant woman suffering from vaginitis.MethodsViral metagenomics method was used to detect the viral sequences in 88 vaginal swab samples collected from 88 pregnant women with vaginitis. A novel papillomavirus, named HPV-ZJ01 (GenBank no. KX082661), was detected in one sample and its complete genome sequence was amplified by PCR and sequenced by Sanger walking. Phylogenetic analyses based on the complete genome and the L1 protein of HPV-ZJ01 and other representative human papillomaviruses were done, respectively. Further PCR screening was performed in vaginal swabs (n = 135), cervical smears (n = 40) and cervical cancer tissues (n = 40) using nested-PCR primers designed based on HPV-ZJ01 sequence to investigate the prevalence of HPV-ZJ01.ResultsThe genome of HPV-ZJ01 is 7,358 bp in length with a GC content of 37.8 %. HPV-ZJ01 was predicted to contain six open reading frames (E6, E7, E1, E2, L2, and L1) and a non-coding long control region (LCR). The genome shared the highest overall similarity to HPV-166, with 70.6 % nucleotide sequence identity while its L1 gene shared the highest nucleotide similarity to HPV-162, with 71.1 % sequence identity. Phylogenetic analysis suggested that HPV-ZJ01 belongs to a novel HPV type in the Gamma-PV genus, species Gamma-19, already containing HPV161, HPV162 and HPV166. PCR screening results indicated that none of the other samples were positive for HPV-ZJ01 except the original HPV-ZJ01 positive vaginal swab specimen.ConclusionThe genome sequence of a novel type of species Gamma-19 HPV was characterized. The screening PCR results suggested that HPV-ZJ01 is not associated with any of the cervical cancer samples tested. In order to confirm the prevalence and disease association, if any, for HPV-ZJ01, a further study with different sample types and a larger sample size is needed.

Project description:Xylella fastidiosa (Xf) is a globally distributed plant-pathogenic bacterium. The primary control strategy for Xf diseases is eradicating infected plants; therefore, timely and accurate detection is necessary to prevent crop losses and further pathogen dispersal. Conventional Xf diagnostics primarily relies on quantitative PCR (qPCR) assays. However, these methods do not consider new or emerging variants due to pathogen genetic recombination and sensitivity limitations. We developed and tested a metagenomics pipeline using in-house short-read sequencing as a complementary approach for affordable, fast, and highly accurate Xf detection. We used metagenomics to identify Xf to the strain level in single- and mixed-infected plant samples at concentrations as low as 1 pg of bacterial DNA per gram of tissue. We also tested naturally infected samples from various plant species originating from Europe and the United States. We identified Xf subspecies in samples previously considered inconclusive with real-time PCR (quantification cycle [Cq], >35). Overall, we showed the versatility of the pipeline by using different plant hosts and DNA extraction methods. Our pipeline provides taxonomic and functional information for Xf diagnostics without extensive knowledge of the disease. This pipeline demonstrates that metagenomics can be used for early detection of Xf and incorporated as a tool to inform disease management strategies. IMPORTANCE Destructive Xylella fastidiosa (Xf) outbreaks in Europe highlight this pathogen's capacity to expand its host range and geographical distribution. The current disease diagnostic approaches are limited by a multiple-step process, biases to known sequences, and detection limits. We developed a low-cost, user-friendly metagenomic sequencing tool for Xf detection. In less than 3 days, we were able to identify Xf subspecies and strains in field-collected samples. Overall, our pipeline is a diagnostics tool that could be easily extended to other plant-pathogen interactions and implemented for emerging plant threat surveillance.

Project description:Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R2 ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities.

Dataset Information

MetaID: a novel method for identification and quantification of metagenomic samples.

Publications

MetaID: a novel method for identification and quantification of metagenomic samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets