BioMacLab supports research questions across genomics, transcriptomics, microbial-community analysis, peptide research, and machine-learning-assisted bioinformatics through clearly scoped computational workflows.
The current BioMacLab research page lists more than twenty peer-reviewed papers across genomics and related computational biology topics.
Eight verified standalone BioMacLab service areas are published in the current service catalog.
The current BioMacLab About page lists the founder, co-founder, and four additional researchers.
The current BioMacLab homepage lists six interns from biotechnology, fisheries, pharmacy, and microbiology backgrounds.
Current BioMacLab Analysis Areas
Explore the current BioMacLab computational analysis areas. Each project is scoped around the available data, research question, appropriate methodology, and clearly defined expected outputs.
Comparative analysis of genome sequences to examine shared and variable genomic features, evolutionary relationships, and pangenome-level patterns relevant to a defined research question.
Bioinformatics analysis of sequencing data from microbial communities to characterize composition, diversity, and biologically relevant community patterns within the scope of the supplied dataset.
Computational analysis of RNA sequencing datasets for expression-focused research questions, including quality-aware processing and downstream interpretation appropriate to the study design.
Research-oriented computational analysis of cancer genomics datasets to examine genomic variation, candidate biomarkers, and biologically relevant patterns without representing the output as clinical diagnosis or treatment advice.
Computational prioritization and modelling workflows that combine established in-silico approaches with machine-learning methods where they are appropriate to a clearly defined drug-discovery research question.
Computational peptide analysis and prioritization for research applications, including sequence-informed and machine-learning-assisted approaches where the available evidence and project scope support them.
Machine-learning-assisted analysis for research questions involving potential protein-protein interaction patterns, with interpretation kept within the limits of the available data and predictive methodology.
Bioinformatics analysis of next-generation sequencing datasets using project-appropriate quality control, processing, and interpretation steps defined by the experimental design and research objective.
A transparent research workflow that moves from the question and available data to an agreed computational plan, analysis, quality review, and clearly documented outputs.
Discuss the research question, study design, available data, and expected outcomes before a computational workflow is proposed.
Confirm the data structure, metadata, project boundaries, required quality checks, and the outputs that should be produced.
Run the agreed bioinformatics workflow and review technical quality, statistical or predictive behaviour, and output consistency where applicable.
Deliver the agreed tables, figures, methods notes, and research-oriented interpretation with limitations stated clearly.
Benchmarking pangenome dynamics and horizontal gene transfer in Mycobacterium marinum evolution
Multiscale comparative pathogenomic analysis of Vibrio anguillarum linking serotype diversity, genomic plasticity and pathogenicity
Pathogenomic Insights into Piscirickettsia salmonis with a Focus on Virulence Factors, Single-Nucleotide Polymorphism Identification, and Resistance Dynamics
Share the research question, data type, available metadata, and expected outputs. BioMacLab can then assess whether the requested analysis fits the current computational scope before any sensitive files are exchanged.
