Blaz Stres

Bio: Blaz Stres is an academic researcher from University of Ljubljana. The author has contributed to research in topics: Medicine & mothur. The author has an hindex of 9, co-authored 17 publications receiving 15283 citations. Previous affiliations of Blaz Stres include University of Innsbruck & Jožef Stefan Institute.

Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

Rhizosphere bacteria and fungi associated with plant growth in soils of three replanted apple orchards

Abstract: High-throughput 454 pyrosequencing was applied to investigate differences in bacterial and fungal communities between replant and closely situated control non-replant (fallow) soils. The V1-V3 region of the bacterial 16S rRNA gene and the ITS1 region of fungi from the different soils were sequenced using 454 pyrosequencing (Titanium chemistry), and data were analysed using the MOTHUR pipeline. The bacterial phyla Proteobacteria, Actinobacteria and Acidobacteria dominated in both fallow and replant apple orchard soils, and community composition at both phylum and genus level did not significantly differ according to NP-MANOVA. The fungal phyla Ascomycota, Zygomycota and Basidiomycota were dominant, and communities also did not differ in composition at either phylum or genus level. High positive Pearson correlations with plant growth in a plant growth assay performed with apple rootstocks plantlets were detected for the bacterial genera Gp16 and Solirubrobacter (r: >0.82) and fungal genera Scutellinia, Penicillium, Lecythophora and Paecilomyces (r: >0.65). Strong negative correlations with plant growth were detected for the bacterial genera Chitinophaga and Hyphomicrobium (r: <−0.78) and the fungal genera Acremonium, Fusarium and Cylindrocarpon (r: <−0.81). Study findings are in part consistent with those of previous research, but also highlight associations between apple plants and certain microbial genera. The functional role of these genera in affecting soil health and fertility should be further investigated.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

Abstract: The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Microbial activity and community structure in two drained fen soils in the Ljubljana Marsh

Abstract: Fen peatlands are specific wetland ecosystems containing high soil organic carbon (SOC). There is a general lack of knowledge about the microbial communities that abound in these systems. We examined the microbial activity and community structure in two fen soils differing in SOC content sampled from the Ljubljana Marsh under different seasonal conditions. Substrate-induced respiration and dehydrogenase activity were used as indicators of total microbial activity. Both methods indicated higher microbial activities in the fen soil with the higher SOC content on all dates of sampling. To determine whether the differences in microbial activity were associated with differences in the microbial community structures, terminal restriction fragment length polymorphism (T-RFLP) of bacterial 16S rRNA genes was performed. Comparison of the T-RFLP profiles revealed very similar community structures in both fens and in the two seasonal extremes investigated. This suggested a stable community structure in the two fens, which is not affected by the SOC content or seasonal variation. In addition, a bacterial 16S ribosomal RNA gene based clone library was prepared from the fen soil with the higher SOC content. Out of 114 clones analysed, approximately 53% belonged to the Proteobacteria, 23% to the Acidobacteria, 21% to a variety of other taxa, and less than 3% were affiliated with the Firmicutes.

Recently Deglaciated High-Altitude Soils of the Himalaya: Diverse Environments, Heterogenous Bacterial Communities and Long-Range Dust Inputs from the Upper Troposphere

Abstract: Background The Himalaya with its altitude and geographical position forms a barrier to atmospheric transport, which produces much aqueous-particle monsoon precipitation and makes it the largest continuous ice-covered area outside polar regions. There is a paucity of data on high-altitude microbial communities, their native environments and responses to environmental-spatial variables relative to seasonal and deglaciation events.

QIIME allows analysis of high-throughput community sequencing data.

Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

Abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

DADA2: High-resolution sample inference from Illumina amplicon data

Abstract: We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

Abstract: Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

Metagenomic biomarker discovery and explanation

Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.