A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.

Background: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.

A multi-study analysis enables identification of potential microbial features associated with skin aging signs.

During adulthood, the skin microbiota can be relatively stable if environmental conditions are also stable, yet physiological changes of the skin with age may affect the skin microbiome and its function. The microbiome is an important factor to consider in aging since it constitutes most of the genes that are expressed on the human body. However, severity of specific aging signs (one of the parameters used to measure "apparent" age) and skin surface quality (e.

HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis.

Introduction: The fungi ITS sequence length dissimilarity, non-specific amplicons, including chimaera formed during Polymerase Chain Reaction (PCR), added to sequencing errors, create bias during similarity clustering and abundance estimation in the downstream analysis. To overcome these challenges, we present a novel approach, Hierarchical Clustering with Kraken (HCK), to classify ITS1 amplicons and Abundance-Base Alternative Approach (ABAA) pipeline to detect and filter non-specific amplicons in fungi metabarcoding sequencing datasets.

Materials And Methods: We compared the performances of both pipelines against QIIME, KRAKEN, and DADA2 using publicly available fungi ITS mock community datasets and using BLASTn as a reference.

An assessment of bacterial small RNA target prediction programs.

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are.

RNA at 92 °C: the non-coding transcriptome of the hyperthermophilic archaeon Pyrococcus abyssi.

The non-coding transcriptome of the hyperthermophilic archaeon Pyrococcus abyssi is investigated using the RNA-seq technology. A dedicated computational pipeline analyzes RNA-seq reads and prior genome annotation to identify small RNAs, untranslated regions of mRNAs, and cis-encoded antisense transcripts. Unlike other archaea, such as Sulfolobus and Halobacteriales, P.

Transcriptomic profiling of the oyster pathogen Vibrio splendidus opens a window on the evolutionary dynamics of the small RNA repertoire in the Vibrio genus.

Work in recent years has led to the recognition of the importance of small regulatory RNAs (sRNAs) in bacterial regulation networks. New high-throughput sequencing technologies are paving the way to the exploration of an expanding sRNA world in nonmodel bacteria. In the Vibrio genus, compared to the enterobacteriaceae, still a limited number of sRNAs have been characterized, mostly in Vibrio cholerae, where they have been shown to be important for virulence, as well as in Vibrio harveyi.

NAPP: the Nucleic Acid Phylogenetic Profile Database.

Nucleic acid phylogenetic profiling (NAPP) classifies coding and non-coding sequences in a genome according to their pattern of conservation across other genomes. This procedure efficiently distinguishes clusters of functional non-coding elements in bacteria, particularly small RNAs and cis-regulatory RNAs, from other conserved sequences. In contrast to other non-coding RNA detection pipelines, NAPP does not require the presence of conserved RNA secondary structure and therefore is likely to identify previously undetected RNA genes or elements.