gene-expression-analysis

RNA-seq workflow using STAR and DESeq2

Continuous enrichment profiling for transcriptomes. Characterise how functional programmes are distributed across the expression gradient, with built-in significance testing for single samples. Great for non-model organisms, clinical isolates, and unreplicated datasets.

Source code, notebooks, and documentation of Crecerelle, a probabilistic deep learning framework for learning cell embeddings from single-cell gene expression and alternative splicing-induced transcript usage. Please read our accompanying paper for details.

A bioinformatic analysis using the Allen Human Brain Atlas

ExprCompareR is a R package that integrates RNA-seq and protein-expression data across human tissues to enable statistical comparison and visualization of transcript-protein relationships.

scripts and resources for performing miRNA sequencing analysis using tools like mirPRo and miRDeep2. Explore the code to process reads, map them to the genome, quantify known miRNAs, identify novel miRNAs, and browse the results

This project analyzes breast cancer gene expression data from the GEO dataset (GSE183947) using R to study expression patterns and identify (DEGs) potential biomarkers.

A tutorial demonstrating how to analyze gene expression data using elastic net models to predict patient responses to immunotherapy, focusing on regularization, cross-validation, and feature importance.

Gene expression analysis approaches using knowledge graph of Protein-Protein Interaction from STRING database

My Spring 2024 term project for NYU's Applied Genomics graduate course (M.S. Biology). Using data from Brar et al, analysis of average gene expression on certain regions of baker's yeast chromosomes during traditional time course meiosis.

Bioinformatics package created in C# and python, which can analyze biological data such as DNA, RNA and protein sequences, Biological Databases Scrapping, As well as 'GEO Analysis' offers a variety of analysis on RNA-seq. Preprocessing, parsing various biological file formats (FASTA, PDB, SOFT, FASTQ, etc.), this all using C# language and dotnet.

R package for tissue enrichment analysis using read-count or FPKM-value matrix

Automated bioinformatics pipeline for GEO transcriptomic datasets including preprocessing, quality control, differential expression analysis, functional enrichment and automated reporting.

Identifies Multiple Peaks and Qauntifies Transcripts. Quantifies gene expression from TAGseq experiments by identifying transcript isoforms containing distinct 3' UTRs/terminal exons.

Identification of small molecule therapeutics against COVID-19 using phytochemical screening, gene expression prediction, enrichment analysis, and druglikeness evaluation.

RNA-Seq analysis pipeline for investigating transcriptional changes during mammalian cardiac regeneration.

Prior knowledge-guided tree-based models

Sleeping is an essential need for animals. Many studies in the past have demonstrated health affections as consequence of sleep deficiency. In this project, RNA-seq analysis allows to compare gene expression in mice among sleep disruption and normal conditions.

RBP-Data-Processing is a repository containing R code for processing RNA-binding protein (RBP) datasets. The code imports the data table, performs data cleaning and transformation operations, and saves the processed dataset. It provides a convenient and reproducible workflow for analyzing RBP data obtained from external sources.

This project aims to identify gene expression patterns associated with different conditions or diseases, leveraging advanced data processing and model training techniques. The analysis includes preprocessing RNA-Seq data, training multiple classifiers, and evaluating their performance to determine the most effective models for such biological data.