DOI: 10.26190/unsworks/31843
title: Supplementary Data for PhD Thesis: "Unravelling the Complexity of RNA Splicing and Gene Expression Regulation in Health and Diseases"

description: This dataset consists of Excel spreadsheets containing tabular research data supporting three studies investigating gene expression, biological attributes, and cross-species gene relationships. Chapter 2 folder contains 7 files with exon usage analysis in neuromuscular disease genes. These files include exon-level and gene-level quantitative data from RNA-seq samples across developmental stages (fetal/neonatal, pediatric, adult) and muscle types (skeletal, cardiac). The primary variable is PSI (Percent Spliced In) values ranging from 0-1 indicating exon inclusion/exclusion rates. Secondary variables include delta PSI (differences between conditions), standard deviations, and variant densities (pathogenic/likely-pathogenic variants from ClinVar, population variants from gnomAD). Data are organized by gene identifiers, exon coordinates, and variant statistics, grouped by age group and muscle subtype. Chapter 4 folder contains 5 files with GeneRAIN model predictions and performance metrics. These include performance metrics (Adjusted Rand Index, Normalized Mutual Information, Fowlkes-Mallows Index) assessing model learning across 21 biological databases, classification results from 5,050 machine learning classifiers with AUC scores (Area Under Curve, 0-1 scale), and 62.5 million biological attribute predictions for genes including disease associations, transcription factor targets, Gene Ontology terms, and pathways. The data structure consists of gene identifiers with prediction probabilities and confidence metrics. Chapter 5 folder contains 5 files with human-mouse gene comparison analysis. These include cross-species homolog analysis for gene pairs with RNA embedding similarity scores and DNA sequence alignment metrics. Variables include cosine similarity values (0-1), DNA alignment scores (normalized), GC content percentages, and DNA sequence conservation statistics. The files also contain phenotype and disease association data integrating RNA versus DNA similarity measures, and gene set enrichment analysis results with functional annotation clusters. All files are organized in tabular format with columns containing gene identifiers (Ensembl ID, gene symbol), numerical metrics, statistical values, and categorical annotations. Consistent formatting enables integrated analysis across chapters.
description: Chapter 2 data collection involved exon usage quantification from muscle RNA-seq samples obtained from the GTEx project (adult skeletal and cardiac muscle), Cardoso-Moreira et al. developmental transcriptome study (fetal and pediatric samples), and in-house Australian cohorts. RNA reads were aligned to the GRCh38 reference genome using STAR, and exon usage was quantified as PSI using the DEXSeq package. Quality control was performed using RNA-SeQC with samples filtered by mapping rate, exonic rate, and detected gene numbers. Batch effect correction was applied using intronic rate as a covariate in the limma R package (removeBatchEffect function), with a design matrix that included tissue type, age group, and cohort as factors. Chapter 4 data collection involved the GeneRAIN deep learning model trained on 410,850 high-quality human bulk RNA-seq samples from the ARCHS4 database, representing diverse tissues, ages, and conditions. Data preprocessing included library size normalization to 10 million reads, log-transformation, and application of the Binning-By-Gene normalization method with 2,000 bins per gene based on expression rank. The model architecture consisted of a 6-layer Transformer with 200-dimensional gene embeddings, trained via self-supervised learning. Training was conducted for 40 epochs on a masked gene prediction task, and classifiers were trained using 5-fold cross-validation with undersampling for class balance. Model performance was validated against external databases including 21 databases for clustering evaluation, with comparisons made against Geneformer and Gene2vec models. Chapter 5 data collection involved cross-species gene alignment using 410,850 human and 366,750 mouse bulk RNA-seq samples from ARCHS4, along with human-mouse one-to-one orthologs from the MGI database. GeneRAIN models were trained with separate species-specific embeddings using a mixed training approach, followed by supervised alignment using ortholog pairs. DNA analysis was performed using pairwise transcript alignment via Biopython, with sequences obtained from GENCODE (GRCh38 for human, GRCm39 for mouse). Phenotype integration incorporated data from the MGI database, LncRNADisease database, and Alliance of Genome Resources for cross-species phenotype associations.