Dataset Information

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.

ABSTRACT: Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the challenges of such an endeavor, we have developed a novel web-based application named ANASTASIA (automated nucleotide aminoacid sequences translational plAtform for systemic interpretation and analysis). ANASTASIA provides a rich environment of bioinformatic tools, either publicly available or novel, proprietary algorithms, integrated within numerous automated algorithmic workflows, and which enables versatile data processing tasks for (meta)genomic sequence datasets. ANASTASIA was initially developed in the framework of the European FP7 project HotZyme, whose aim was to perform exhaustive analysis of metagenomes derived from thermal springs around the globe and to discover new enzymes of industrial interest. ANASTASIA has evolved to become a stable and extensible environment for diversified, metagenomic, functional analyses for a range of applications overarching industrial biotechnology to biomedicine, within the frames of the ELIXIR-GR project. As a showcase, we report the successful in silico mining of a novel thermostable esterase termed "EstDZ4" from a metagenomic sample collected from a hot spring located in Krisuvik, Iceland.

SUBMITTER: Koutsandreas T

PROVIDER: S-EPMC6543708 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.

Koutsandreas Theodoros T Ladoukakis Efthymios E Pilalis Eleftherios E Zarafeta Dimitra D Kolisis Fragiskos N FN Skretas Georgios G Chatziioannou Aristotelis A AA

Frontiers in genetics 20190524

Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the ...[more]

PMID: 31178894

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Project description:BackgroundMetagenomic next-generation sequencing (mNGS) technology has been widely used to diagnose various infections. Based on the most common pathogen profiles, targeted mNGS (tNGS) using multiplex PCR has been developed to detect pathogens with predesigned primers in the panel, significantly improving sensitivity and reducing economic burden on patients. However, there are few studies on summarizing pathogen profiles of pulmonary infections in immunocompetent and immunocompromised patients in Jilin Province of China on large scale.MethodsFrom January 2021 to December 2023, bronchoalveolar lavage fluid (BALF) or sputum samples from 546 immunocompetent and immunocompromised patients with suspected community-acquired pneumonia were collected. Pathogen profiles in those patients on whom mNGS was performed were summarized. Additionally, we also evaluated the performance of tNGS in diagnosing pulmonary infections.ResultsCombined with results of mNGS and culture, we found that the most common bacterial pathogens were Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii in both immunocompromised and immunocompetent patients with high detection rates of Staphylococcus aureus and Enterococcus faecium, respectively. For fungal pathogens, Pneumocystis jirovecii was commonly detected in patients, while fungal infections in immunocompetent patients were mainly caused by Candida albicans. Most of viral infections in patients were caused by Human betaherpesvirus 5 and Human gammaherpesvirus 4. It is worth noting that, compared with immunocompetent patients (34.9%, 76/218), more mixed infections were found in immunocompromised patients (37.8%, 14/37). Additionally, taking final comprehensive clinical diagnoses as reference standard, total coincidence rate of BALF tNGS (81.4%, 48/59) was much higher than that of BALF mNGS (40.0%, 112/280).ConclusionsOur findings supplemented and classified the pathogen profiles of pulmonary infections in immunocompetent and immunocompromised patients in Jilin Province of China. Most importantly, our findings can accelerate the development and design of tNGS specifically used for regional pulmonary infections.

Project description:Importance:Identifying infectious causes of subacute or chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed. Objective:To present a case series of patients with diagnostically challenging subacute or chronic meningitis using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious. Design, Setting, and Participants:In this case series, mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples were used to develop and implement a weighted scoring metric based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results. Total RNA was extracted for mNGS from the CSF of 7 participants with subacute or chronic meningitis who were recruited between September 2013 and March 2017 as part of a multicenter study of mNGS pathogen discovery among patients with suspected neuroinflammatory conditions. The neurologic infections identified by mNGS in these 7 participants represented a diverse array of pathogens. The patients were referred from the University of California, San Francisco Medical Center (n = 2), Zuckerberg San Francisco General Hospital and Trauma Center (n = 2), Cleveland Clinic (n = 1), University of Washington (n = 1), and Kaiser Permanente (n = 1). A weighted z score was used to filter out environmental contaminants and facilitate efficient data triage and analysis. Main Outcomes and Measures:Pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results. Results:The 7 participants ranged in age from 10 to 55 years, and 3 (43%) were female. A parasitic worm (Taenia solium, in 2 participants), a virus (HIV-1), and 4 fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis) were identified among the 7 participants by using mNGS. Evaluating mNGS data with a weighted z score-based scoring algorithm reduced the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed, effectively separating bona fide pathogen sequences from spurious environmental sequences so that, in each case, the causative pathogen was found within the top 2 scoring microbes identified using the algorithm. Conclusions and Relevance:Diverse microbial pathogens were identified by mNGS in the CSF of patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for 1 year, the first reported case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of C dubliniensis meningitis. Prioritizing metagenomic data with a scoring algorithm greatly clarified data interpretation and highlighted the problem of attributing biological significance to organisms present in control samples used for metagenomic sequencing studies.

Project description:BackgroundExtrachromosomal circular DNAs (eccDNAs) are ring-like DNA structures physically separated from the chromosomes with 100 bp to several megabasepairs in size. Apart from carrying tandemly repeated DNA, eccDNAs may also harbor extra copies of genes or recently activated transposable elements. As eccDNAs occur in all eukaryotes investigated so far and likely play roles in stress, cancer, and aging, they have been prime targets in recent research-with their investigation limited by the scarcity of computational tools.ResultsHere, we present the ECCsplorer, a bioinformatics pipeline to detect eccDNAs in any kind of organism or tissue using next-generation sequencing techniques. Following Illumina-sequencing of amplified circular DNA (circSeq), the ECCsplorer enables an easy and automated discovery of eccDNA candidates. The data analysis encompasses two major procedures: first, read mapping to the reference genome allows the detection of informative read distributions including high coverage, discordant mapping, and split reads. Second, reference-free comparison of read clusters from amplified eccDNA against control sample data reveals specifically enriched DNA circles. Both software parts can be run separately or jointly, depending on the individual aim or data availability. To illustrate the wide applicability of our approach, we analyzed semi-artificial and published circSeq data from the model organisms Homo sapiens and Arabidopsis thaliana, and generated circSeq reads from the non-model crop plant Beta vulgaris. We clearly identified eccDNA candidates from all datasets, with and without reference genomes. The ECCsplorer pipeline specifically detected mitochondrial mini-circles and retrotransposon activation, showcasing the ECCsplorer's sensitivity and specificity.ConclusionThe ECCsplorer (available online at https://github.com/crimBubble/ECCsplorer ) is a bioinformatics pipeline to detect eccDNAs in any kind of organism or tissue using next-generation sequencing data. The derived eccDNA targets are valuable for a wide range of downstream investigations-from analysis of cancer-related eccDNAs over organelle genomics to identification of active transposable elements.

Dataset Information

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.

Publications

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.

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