This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This project involves the computational analysis of transcriptomic sequencing data to study molecular mechanisms involved in fibrosis. Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets will be analyzed to investigate gene expression patterns and cellular heterogeneity associated with fibrotic processes. The analyses will use established bioinformatics pipelines to process and interpret large-scale sequencing datasets. HPC resources are required to efficiently handle the computational demands of these data-intensive analyses.
The HPC resources will be used to perform bulk RNA-seq, mRNA-seq, and single-cell RNA-seq (scRNA-seq) data analysis. The analysis pipeline will include standard bioinformatics tools for next-generation sequencing data processing, including FastQC and MultiQC for quality control, Cutadapt or Trimmomatic for adapter trimming, and STAR or HISAT2 for read alignment to reference genomes. Transcript quantification will be performed using featureCounts, Salmon, or Kallisto, followed by downstream statistical analyses in R using Bioconductor packages such as DESeq2, edgeR, and limma. For single-cell RNA-seq analysis, tools such as Cell Ranger, Seurat, and Scanpy will be used for preprocessing, normalization, dimensionality reduction, clustering, and cell-type annotation.