RNA-seq workflow using STAR and DESeq2
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Updated
Sep 10, 2025 - Python
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
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.
RNA-Seq analysis pipeline for investigating transcriptional changes during mammalian cardiac regeneration.
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.
Identification of small molecule therapeutics against COVID-19 using phytochemical screening, gene expression prediction, enrichment analysis, and druglikeness evaluation.
Identifies Multiple Peaks and Qauntifies Transcripts. Quantifies gene expression from TAGseq experiments by identifying transcript isoforms containing distinct 3' UTRs/terminal exons.
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.
Prior knowledge-guided tree-based models
Code and analysis scripts for analyzing newly transcribed RNA in large-scale compound screen experiments
In this module, you will take a deeper look at RNA-sequencing using single-cell approaches and miRNA sequencing, and investigate their impacts on gene regulation
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.
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.
Docker based GUI for in-depth but accessible scRNA annotation and analysis
Gene expression analysis and classification using XGBoost and Differential Expression Analysis (DES)
A classification model to classify samples based on their gene expression profiles
This repository hosts resources and analysis related to the genome-wide identification and characterization of N6-methyladenosine (m6A) regulatory genes in soybean.
Scaled matrix completion and cell deconvolution with NanoString data, Yichen Zhang, 2019
Single-cell RNA-seq analysis of NSCLC tumor cells using Seurat to identify cellular heterogeneity and cluster-specific marker genes. Includes preprocessing, dimensionality reduction, clustering, and visualization.
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