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Mikhail G Divashuk

Publications and source records attributed to Mikhail G Divashuk.

2 recordsLinked to original sources
PubMed2026

Soybean α-Amylase Gene Family: Structure and Expression in Response to Abiotic Stresses.

α-Amylases are involved in starch breakdown, thereby influencing plant development. Information on the α-amylase genes in soybean is limited. Here, we identified five soybean α-amylase genes from subfamilies AtAMY1 (GmaAMY5), AtAMY2 (GmaAMY4), AtAMY3 (GmaAMY1, GmaAMY3), and AMY6 (GmaAMY2). In silico analysis indicated that all five genes were actively expressed in leaves, flowers, and pods but weakly in roots. GmaAMY1-GmaAMY5 mRNAs were predicted to be targets of miRNAs associated with stress response, organ development, and nitrogen fixation. Putative GmaAMY1-GmaAMY5 proteins contained α-amylase-specific catalytic domain, signatures, and active sites. Short-term abiotic stresses (100 mM NaCl, 2.5-20% PEG, and 4 °C cold) applied to the cv. Doka affected both GmaAMY1-GmaAMY5 expression and the content of starch and soluble sugars in leaves. GmaAMY1 gene expression increased in response to NaCl and PEG, GmaAMY2 in response to PEG, and GmaAMY5 in response to NaCl. Salt stress suppressed the expression of the GmaAMY2-GmaAMY4 genes. The mRNA levels of all five genes increased after 2 h of cold exposure. Under salinity stress, there was inverse correlation of starch content with GmaAMY4 expression (r = -0.5135, p = 0.0293) and overall GmaAMY1-GmaAMY5 expression (r = -0.6318, p = 0.0049), suggesting a possible role of GmaAMY genes in protecting soybean from salinity by maintaining the starch/soluble sugars balance. Our results may aid in the breeding of stress-tolerant soybean varieties.

Glycine max↗
PubMed2026

REAPER: a project-centric workflow layer for comparative repeatome analysis.

INTRODUCTION: Repeatome characterization from short-read sequencing data is widely performed using RepeatExplorer2/TAREAN. However, long-lived multisample projects and explicit comparative designs are often executed as ad hoc command sequences that are hard to version, rerun, and monitor on shared compute environments - a gap that motivates a project-centric workflow layer for repeatome analysis. METHODS: We present REAPER (Repeatome Extended Analysis Pipeline-Execution and Reporting), a project-centric workflow layer that couples a modular Snakemake pipeline with a Python project manager to enforce a stable on-disk layout and configuration-driven execution for single-sample and comparative repeatome analyses. REAPER does not implement a new repeat-discovery algorithm; it is an orchestration layer, and biological accuracy for clustering and satellite calling depends on the underlying RepeatExplorer2/TAREAN and satMiner methods it coordinates. REAPER standardizes: Read QC Deterministic subsampling and preparation RepeatExplorer2/TAREAN execution via seqclust, with satMiner-inspired iterative assembly Post-TAREAN BLAST-based annotation against curated repeat collections (optionally including taxon-scoped NCBI-derived resources with freshness checks) Optional graph-based comparative reports The pipeline makes comparative read allocation, prefix policy, and analysis-ready tables explicit; caching supports incremental reruns and structured logs support monitoring. Performance was assessed using a Triticeae short-read dataset (five samples), with rule-level logging of runtime and memory across pipeline stages. RESULTS: Rule-level performance logs show that graph-based clustering dominates runtime and memory, while QC and preparation steps are lightweight by comparison. Graph-report annotations for the Triticeae project additionally link high-ranking clusters to established repeat markers - including pTa794- and pSc119-class entries in curated databases. DISCUSSION: These findings illustrate biologically interpretable outputs (recovery of known Triticeae repeat markers) alongside quantitative performance metrics (identification of graph-based clustering as the dominant computational cost). By making comparative read allocation, prefix policy, and analysis-ready tables explicit - and by supporting caching and structured logging - REAPER supports reproducible comparative repeatome analysis in evolving multisample projects. As an orchestration layer rather than a discovery algorithm, REAPER's contribution lies in reproducibility, monitorability, and comparative-analysis infrastructure, with biological accuracy remaining contingent on the underlying RepeatExplorer2/TAREAN and satMiner methods.

TAREAN↗