RNA-seq workflow using STAR and DESeq2

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

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

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 and classification using XGBoost and Differential Expression Analysis (DES)

Scaled matrix completion and cell deconvolution with NanoString data, Yichen Zhang, 2019

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.

Code and analysis scripts for analyzing newly transcribed RNA in large-scale compound screen experiments

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.

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.

This toolkit provides Python code for preprocessing, quality control, clustering, and visualization of single-cell RNA sequencing data using Scanpy. Ideal for deep insights into cell populations and gene expression.

This project involves an exploratory data analysis (EDA) of gene expression in breast tumor and normal tissue as part of the ICCS361 course at Mahidol University International College.