Final Exam for Coursera's Python for Genomic Data Science course
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Updated
Oct 12, 2022 - Jupyter Notebook
Final Exam for Coursera's Python for Genomic Data Science course
Genomic Analysis of Canis lupus with the use of Genomic Maps and Philogenetic Trees
R package for annotating and parsing transposable elements-associated data
This repository contains the file codes used to complete the final project for the course Post Genomic Analysis.
NLR-Assembler is a command line tool for improving RenSeq Assemblies using linked-read sequencing by 10x Genomics.
Kissinger Research Group Shared Code
Genomics Carpentries Workshop at Rutgers University, April 25-26, 2024
Filter DE genes based on log2Folchange, FDR value or both
Genetic Algorithm implementation to study haplotypes of genomic features
Reverse Complement PCR (RC-PCR_ Classification pipeline.
This repository serves as a valuable resource for individuals engaged in data exploration, statistical analysis, and research within the domains of plant breeding, genetics, statistics, and genomics. The purpose of this repository is to share a collection of R codes that can be utilized by others for their own data analysis projects
This repository houses the Genomic Sequence Comparison Code (GSCC), a collection of Python scripts designed for genomic sequence analysis. Whether you're comparing suspected sequences with known reference sequences or delving into bioinformatics, GSCC provides versatile tools for pairwise alignment. Feel free to explore!
FinaleToolkit is a package and standalone program to extract fragmentation features of cell-free DNA from paired-end sequencing data.
Data for Smith et al. 2021 in Ecology and Evolution
This is a TxDb package created for Coffea arabica (coffee).
Supervised classification of various species DNA sequences using FFT and Machine Learning.
This repository is the current repository for our Jones et al. 2024 manuscript titled Long-read RNA sequencing identifies region- and sex-specific C57BL/6J mouse brain mRNA isoform expression and usage.
Machine Learning in Omics: Integration of Metagenomics and Metabolomics.
Using NVIDIA Deepmind's Nucleotide Transformer to generate embeddings for yeast genomes.
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.
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