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Ebou, A.

Publications and source records attributed to Ebou, A..

3 recordsLinked to original sources

Microbial succession in West African secondary forests: rapid internal stabilisation without convergence toward old-growth reference states

Secondary forest succession following agricultural abandonment is a dominant land-use transition across the tropics, yet whether soil microbial communities recover toward old-growth forest reference states remains poorly understood, particularly in West Africa. Here, we investigated the successional dynamics of bacterial and arbuscular mycorrhizal (AM) fungal communities along post-agricultural chronosequences spanning 1 to 43 years across six classified forests in Cote d'Ivoire, using Bayesian hierarchical models applied to amplicon sequencing data. Both guilds attained moderate to high alpha diversity within the first decade of succession; AM fungal diversity showed moderate evidence of age-related increase thereafter while bacterial diversity showed no directional trend. Pairwise turnover analyses revealed progressive internal convergence in AM fungal communities with plots farther apart in successional time becoming more compositionally similar, while bacterial communities showed only a weak and uncertain tendency in the same direction. Beta-dispersion modelling further indicated progressive within-forest homogenisation of AM fungal communities across abundance-weighted metrics, while bacterial assemblages showed no such stabilisation. Despite this internal convergence, compositional distances to old-growth reference plots remained persistently high for both guilds throughout the chronosequence, with no statistical evidence of recovery toward old-growth states across any dissimilarity metric or guild within the 40-year window. Indicator species analysis identified no robust stage-specific taxa after correction for multiple testing. These results indicate that microbial succession in post-agricultural West African forests is characterised by rapid early reorganisation followed by stabilisation into site-specific assemblages that remain persistently distinct from old-growth reference communities. This outcome challenges the direct application of classical vegetation successional theory to soil microbiomes and indicates that no detectable recovery toward old-growth microbial community states was observed within the 40-year chronosequence.

ecology↗

HyperEx: A Tool to Extract Hypervariable Regions from 16S rRNA Sequencing Data

The 16S ribosomal RNA gene is one of the most studied genes in biology. This 16S ribosomal RNA importance is due to its wide application in phylogenetics and taxonomic elucidation of bacteria and archaea. Indeed, 16S ribosomal RNA is present in almost all bacteria and archaea and has, among many other useful characteristics, a low mutation rate. The 16S ribosomal RNA is composed of nine hypervariable regions which are commonly targeted by high throughput sequencing technologies in identification or community studies like metabarcoding studies. Unfortunately, the hypervariable regions do not have the same taxonomic resolution among all bacteria taxa. This requires a preliminary in silico analysis to determine the best hypervariable regions to target in a particular study. Nevertheless, to the best of our knowledge, no automated primer-based open-source tool exists to extract hypervariable regions from complete or near-complete 16S rRNA sequencing data. Here we present HyperEx which efficiently extracts the hypervariable region of interest based on embedded primers or user-given primers. HyperEx implements the Myers algorithm for the exact pairwise sequence alignment. HyperEx is freely available under the MIT license as an operating system independent Rust command-line tool at https://github.com/Ebedthan/hyperex and https://crates.io.

bioinformatics↗

bioSyntax: Syntax Highlighting For Computational Biology

Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information that is obfuscated by the complexity of the data structures. bioSyntax (http://bioSyntax.org) is a freely available suite of syntax highlighting packages for vim, gedit, Sublime, and less, which aids computational scientists to parse and work with their data more efficiently.

bioinformatics↗