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Table · dataset · 2026

Thesis: Supplementary Datasets and Resources

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Description

<p dir="ltr">This repository contains the supplementary datasets generated during a doctoral research project investigating schistosome adhesion and lipid acquisition networks through comparative genomics, orthology analysis, domain architecture profiling, phylogenetics, and network-based target prioritisation. The collection includes curated protein catalogues, orthogroup assignments, phylogenetic classifications, conserved motif annotations, membrane topology predictions, functional module classifications, evidence-based confidence assessments, and prioritisation outputs for candidate proteins across <i>Schistosoma mansoni</i>, <i>S. haematobium</i>, and <i>S. japonicum</i>.</p><p dir="ltr">The final adhesome dataset has been integrated into an interactive <a href="thesis-h5trcx3waczuh7mvz5l7af.streamlit.app/" target="_blank" rel="noreferrer">Streamlit-based atlas</a> that provides standardised protein identifiers, species distributions, predicted orthology relationships, domain architectures, conserved motifs, membrane topology, functional modules, phylogenetic classifications, network positions, evidence levels, and prioritisation scores.</p><p dir="ltr">All custom scripts used for data retrieval, processing, annotation, orthology inference, phylogenetic reconstruction, network analysis, prioritisation, and atlas generation are maintained under version control and are publicly available at <a href="github.com/EvansKCCR/Thesis" target="_blank" rel="noopener noreferrer">GitHub Repository</a>.</p><p dir="ltr">Primary input datasets were obtained from <a href="parasite.wormbase.org/" target="_blank" rel="noopener noreferrer">WormBase ParaSite</a> and <a href="ncbi.nlm.nih.gov/datasets/taxonomy/9606/" target="_blank" rel="noreferrer">NCBI-NLM-NIH</a>.

These resources provided the reference proteomes and genomic annotations used throughout the study.</p><p dir="ltr">The datasets are provided to support transparency, reproducibility, and reuse of the computational workflows and research outputs described in the associated thesis.</p>

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