Automated Isoform Discovery Detector (AIDD)
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Updated
Mar 3, 2019 - R
Automated Isoform Discovery Detector (AIDD)
Gene co-expression network analysis
RNA-seq Differential Gene Expression (DGE) analysis comparing multiple dataset samples involves the comparison of gene expression levels across multiple samples with the possibliity to account for defined conditions, treatments, or groups.
Creating a simple mirrorplot can be good visualization for showing up/down regulated genes in an RNA-seq. This details how to create a mirrorplot using ggplot2.
An R package to parse and manage text output of the sequencing quality control utility (fastQC)
Snakemake workflow for Bulk RNASeq at both genome level and transcripts level.
Walks through installation and usage of FASTQC, MultiQC, Trimmomatic, and Salmon for transcriptomic data preprocessing. Includes Grid Engine shell scripts that can be looped over many files in a directory.
Clustering is a common exercise to determine how closely samples are related to each other. This shows how samples can be clustered using a PCoA and PCA and visualizing using ggplot. Particularly, how to cluster RNA-seq samples.
This is a script I used to make an R Shiny application that allows for browsing the time-course gene expression signal, and associated statistics, for an experiment where MCF-7 cells were exposed to oxidative stress-inducing compounds. This dataset was published in *Free Radical Biology and Medicine*, article found here https://doi.org/10.1016/j…
Differential expression analysis in R for RNA sequence reads of fission yeast.
Bioc2022 workshop. SEESAW: Statistical Estimation Of Allelic Expression Using Salmon And Swish
To perform RNA-Seq data analysis and calculate length-scaled transcripts per million (TPM) values using the Salmon tool and the GenomicFeatures package in R.
IMOM - A pipeline for interacting with, processing, and analyzing multi-OM datasets in phylogenetic and evolutionary context
Advanced bioinformatics analysis of RNA sequencing data and genomic databases using R. Explore allelic imbalances, SNP variants, and phylogenetic trees to uncover genetic insights and visualize complex data interactions.
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