Search

Microbiome research is advancing rapidly, and every new study should definitively be based on updated methods, trends and milestones in this field to avoid the wrong interpretation of results. Most human microbiota surveys rely on data captured from snapshots—single data points from subjects—and have permitted uncovering the recognized interindividual variability and major covariates of such microbial communities. Currently, changes in individualized microbiota profiles are under the spotlight to serve as robust predictors of clinical outcomes (e.g. weight loss via dietary interventions) and disease anticipation. Therefore, novel methods are needed to provide robust evaluation of longitudinal series of microbiota data with the aim of assessing intrapersonally short-term to long-term microbiota changes likely linked to health and disease states. Consequently, we developed micro biota ST ability AS sessment via I terative clu S tering (μSTASIS)—a multifunction R package to evaluate individual-centered microbiota stability. μSTASIS targets the recognized interindividual variability inherent to microbiota data to stress the tight relationships observed among and characteristic of longitudinal samples derived from a single individual via iteratively growing-partitioned clustering. The algorithms and functions implemented in this framework deal properly with the sparse and compositional nature of microbiota data. Moreover, the resulting metric is intuitive and independent of beta diversity distance methods and correlation coefficients, thus estimating stability for each microbiota sample rather than giving nonconsensus magnitudes that are difficult to interpret within and between datasets. Our method is freely available under GPL-3 licensing. We demonstrate its utility by assessing gut microbiota stability from three independent studies published previously with multiple longitudinal series of multivariate data and respective metadata. [ABSTRACT FROM AUTHOR]

The miniaturized and portable DNA sequencer MinION™ has demonstrated great potential in different analyses such as genome-wide sequencing, pathogen outbreak detection and surveillance, human genome variability, and microbial diversity. In this study, we tested the ability of the MinION™ platform to perform long amplicon sequencing in order to design new approaches to study microbial diversity using a multi-locus approach. After compiling a robust database by parsing and extracting the rrn bacterial region from more than 67 000 complete or draft bacterial genomes, we demonstrated that the data obtained during sequencing of the long amplicon in the MinION™ device using R9 and R9.4 chemistries were sufficient to study 2 mock microbial communities in a multiplex manner and to almost completely reconstruct the microbial diversity contained in the HM782D and D6305 mock communities. Although nanopore-based sequencing produces reads with lower per-base accuracy compared with other platforms, we presented a novel approach consisting of multi-locus and long amplicon sequencing using the MinION™ MkIb DNA sequencer and R9 and R9.4 chemistries that help to overcome the main disadvantage of this portable sequencing platform. Furthermore, the nanopore sequencing library, constructed with the last releases of pore chemistry (R9.4) and sequencing kit (SQK-LSK108), permitted the retrieval of the higher level of 1D read accuracy sufficient to characterize the microbial species present in each mock community analysed. Improvements in nanopore chemistry, such as minimizing base-calling errors and new library protocols able to produce rapid 1D libraries, will provide more reliable information in the near future. Such data will be useful for more comprehensive and faster specific detection of microbial species and strains in complex ecosystems. [ABSTRACT FROM AUTHOR]

Background: The miniaturised and portable DNA sequencer MinION™ has been released to the scientific community within the framework of an early access programme to evaluate its application for a wide variety of genetic approaches. This technology has demonstrated great potential, especially in genome-wide analyses. In this study, we tested the ability of the MinION™ system to perform amplicon sequencing in order to design new approaches to study microbial diversity using nearly full-length 16S rDNA sequences. Results: Using R7.3 chemistry, we generated more than 3.8 million events (nt) during a single sequencing run. These data were sufficient to reconstruct more than 90 % of the 16S rRNA gene sequences for 20 different species present in a mock reference community. After read mapping and 16S rRNA gene assembly, consensus sequences and 2d reads were recovered to assign taxonomic classification down to the species level. Additionally, we were able to measure the relative abundance of all the species present in a mock community and detected a biased species distribution originating from the PCR reaction using 'universal' primers. Conclusions: Although nanopore-based sequencing produces reads with lower per-base accuracy compared with other platforms, the MinION™ DNA sequencer is valuable for both high taxonomic resolution and microbial diversity analysis. Improvements in nanopore chemistry, such as minimising base-calling errors and the nucleotide bias reported here for 16S amplicon sequencing, will further deliver more reliable information that is useful for the specific detection of microbial species and strains in complex ecosystems. [ABSTRACT FROM AUTHOR]

In recent years, numerous biocomputational tools have been designed to extract functional and evolutionary information from multiple sequence alignments (MSAs) of proteins and genes. Most biologists working actively on the characterization of proteins from a single or family perspective use the MSA analysis to retrieve valuable information about amino acid conservation and the functional role of residues in query protein(s). In MSAs, adjustment of alignment parameters is a key point to improve the quality of MSA output. However, this issue is frequently underestimated and/or misunderstood by scientists and there is no in-depth knowledge available in this field. This brief review focuses on biocomputational approaches complementary to MSA to help distinguish functional residues in protein families. These additional analyses involve issues ranging from phylogenetic to statistical, which address the detection of amino acids pivotal for protein function at any level. In recent years, a large number of tools has been designed for this very purpose. Using some of these relevant, useful tools, we have designed a practical pipeline to perform in silico studies with a view to improving the characterization of family proteins and their functional residues. This review-guide aims to present biologists a set of specially designed tools to study proteins. These tools are user-friendly as they use web servers or easy-to-handle applications. Such criteria are essential for this review as most of the biologists (experimentalists) working in this field are unfamiliar with these biocomputational analysis approaches. [ABSTRACT FROM PUBLISHER]