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https://github.com/alexeyrakov/awesome-bacteria

List of computational resources for analyzing bacterial sequencing data.
https://github.com/alexeyrakov/awesome-bacteria
List: awesome-bacteria
Last synced: 3 months ago
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List of computational resources for analyzing bacterial sequencing data.
Host: GitHub
URL: https://github.com/alexeyrakov/awesome-bacteria
Owner: alexeyrakov
Created: 2017-09-08T21:38:33.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2022-06-17T10:47:40.000Z (over 2 years ago)
Last Synced: 2024-04-21T19:11:51.615Z (7 months ago)
Size: 13.7 KB
Stars: 0
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - awesome-bacteria - List of computational resources for analyzing bacterial sequencing data. (Other Lists / PowerShell Lists)
README

        # awesome-bacteria

List of software packages (and the people developing these methods) for bacterial GWAS, bacterial population genomics etc.


 

Inspired by [awesome-microbes](https://github.com/stevetsa/awesome-microbes/blob/master/README.md)




## Software packages

### GWAS

- [treeWAS](https://github.com/caitiecollins/treeWAS) - [R] - A Phylogenetic Tree-Based Tool for Genome-Wide Association Studies in Microbes.

- [SEER](https://github.com/johnlees/seer) - [C++] - Sequence element (kmer) enrichment analysis.

- [bugwas](https://github.com/sgearle/bugwas) - [R] - Locus and lineage tests for bacterial GWAS.

- [DBGWAS](https://gitlab.com/leoisl/dbgwas) - [C++] - A tool for quick and efficient bacterial GWAS.

- [bacterialGWAS](https://github.com/jessiewu/bacterialGWAS) - [R] - A pipeline for performing genome-wide association tests for bacterial genomes.

- [bacterial_GWAS_tutorial](https://github.com/janepipistrelle/bacterial_GWAS_tutorial) - [R] - Tutorial for bacterial GWAS pipline and bugwas, created for Bodega Bay 2016 NGS workshop.

- [Scoary](https://github.com/AdmiralenOla/Scoary) - [Python] - Microbial pan-genome wide association studies.

- [Roary](https://github.com/sanger-pathogens/Roary) - [Perl] - Rapid large-scale prokaryote pan genome analysis.

### Bacterial population genomics

- [bac-genomics-scripts](https://github.com/aleimba/bac-genomics-scripts) - [Perl] - Collection of scripts for bacterial genomics.

- [PuppetMaster](https://github.com/AdmiralenOla/PuppetMaster) - [Python] - Variable sites extractor.

- [snp-sites](https://github.com/sanger-pathogens/snp-sites) - [C] - Rapid efficient extractor of SNPs from multi-FASTA alignments.

### Other applications

- [poppr](https://github.com/grunwaldlab/poppr) - [R] - An R package for genetic analysis of populations with mixed (clonal/sexual) reproduction.

## Tutorials and workflows

- [scientific_python_cheat_sheet](https://github.com/IPGP/scientific_python_cheat_sheet) - Scientific Python Cheat Sheet.

## Web portals and apps

- [Sofware Carpentry](https://software-carpentry.org/)

- [Data Carpentry](http://www.datacarpentry.org/)

## Educational resources

- [CSHL Programming for Biology 2019](http://programmingforbiology.org) - CSHL Programming for Biology course official site (2019).

- [CSHL Programming for Biology](https://github.com/prog4biol/pfb2019) - CSHL Programming for Biology (2019), all lectures and scripts.

- [CSHL Programming for Biology](https://github.com/srobb1/PFB2012) - CSHL Programming for Biology (2012), all lectures and scripts.

- [CSHL NGS](https://github.com/hyphaltip/CSHL_NGS) - DNA analysis for the CSHL Programming for Biology course (2012-2014).

- [GEN220_2020](https://github.com/biodataprog/GEN220_2020) - GEN220: High Throughput Biological Data Analysis (2020).

- [GEN220 2015](https://github.com/hyphaltip/GEN220_2015) - Computational Analysis of High Throughput Biological Data (2015).

- [programming-intro](https://github.com/biodataprog/2018_programming-intro) - Computational Analysis of High Throughput Biological Data (2018).

- [BCB546X-Spring2022](https://github.com/EEOB-BioData/BCB546-Spring2022) - Computational Skills for Biological Data (Spring 2022).

- [BCB546X-Fall2017](https://github.com/EEOB-BioData/BCB546X-Fall2017) - Computational Skills for Biological Data (Fall 2017).

- [Introduction](https://github.com/BioinformaticsTraining/Introduction) - Talks and Materials for "An Introduction to Bioinformatics".

- [Course-Programme](https://github.com/BacterialCommunitiesAndPopulation/Course-Programme) - Course in Working with bacterial communities and populations (2016).

- [2016-05-16-CAM](https://github.com/Pfern/2016-05-16-CAM) - Data Carpentry (2016).

- [experimental-design](https://github.com/bioinformatics-core-shared-training/experimental-design) - Experimental Design.

- [winter-school2016](https://github.com/bioinformatics-core-shared-training/winter-school2016) - Essential Data Analysis Skills for Biologists (2016).

- [Molecular Phylogenetics](https://sites.google.com/site/eeob563/) - Molecular Phylogenetics Course (2017).

- [functional-genomics-workshop-orange](https://github.com/biolab/functional-genomics-workshop-orange) - Data Mining w/o Programming Workshop (2014).

- [Data Analysis for the Life Sciences](https://github.com/genomicsclass/labs) - Data Analysis for the Life Sciences (2015): Biomedical Data Science, Introduction to Bioconductor: Annotation and analysis, High-performance computing for reproducible genomics with Bioconductor.

- [microbial-informatics-2014](https://github.com/apetkau/microbial-informatics-2014) - Microbial Informatics 2014 Labs.

- [In-depth-NGS-Data-Analysis-Course](https://github.com/hbctraining/In-depth-NGS-Data-Analysis-Course) - In-depth NGS Data Analysis Course (2018).

### Shell

- [shell-novice](https://github.com/swcarpentry/shell-novice) - Software Carpentry introduction to the shell for novices (2017).

- [shell-extras](https://github.com/swcarpentry/shell-extras) - Software Carpentry Extra Unix Shell Material (2017).

- [shell-genomics](https://github.com/datacarpentry/shell-genomics) - Data Carpentry Shell Genomics lessons (2017).

### Python

- [2015-python-intro](https://github.com/ngs-docs/2015-python-intro) - An Introduction to Python (2015).

- [2016-adv-begin-python](https://github.com/ngs-docs/2016-adv-begin-python) - Advanced Beginner Python (2016).

- [python-beginners](https://github.com/OpenTechSchool/python-beginners) - Introduction to programming with Python (2015) (incl. Russian).

- [python-novice-gapminder](https://github.com/swcarpentry/python-novice-gapminder) - Software Carpentry Plotting and Programming in Python (2017).

- [python-novice-inflammation](https://github.com/swcarpentry/python-novice-inflammation/) = Software Carpentry introduction to Python for novices using inflammation data (2017).

- [python-data-intro](https://github.com/OpenTechSchool/python-data-intro) - Introduction to Data Processing with Python (2015).

- [BPBR16](https://github.com/Pfern/BPBR16-Bioinformatics-using-Python-for-Biomedical-Researchers) - Bioinformatics using Python for Biomedical Researchers (2016).

- [2017-python-programming](https://github.com/EEOB-BioData/2017-python-programming) - Programming with Python (2017).

- [evop2018](https://github.com/prog4biol/evop2018) - Programming for Evolutionary Biology Python in 2 Days (2018).

- [Py4Bio](https://github.com/Serulab/Py4Bio) - Public data for the book Python for Bioinformatics.

### R & RStudio

- [r-novice-gapminder](https://github.com/swcarpentry/r-novice-gapminder) - Software Carpentry R for Reproducible Scientific Analysis (2017).

- [r-novice-inflammation](https://github.com/swcarpentry/r-novice-inflammation) - Software Carpentry introduction to R for novices using inflammation data (2017).

- [r-intro](https://github.com/cambiotraining/r-intro) - Cambridge Basic R Course (2017).

- [r-intermediate](https://github.com/bioinformatics-core-shared-training/r-intermediate) - Data Manipulation and Visualisation using R (Intermediate R Course).

- [R-Data-Skills](https://github.com/EEOB-BioData/R-Data-Skills) - R for Reproducible Scientific Analysis (2016).

- [R-genomics](https://github.com/hyphaltip/R-genomics) - Data Carpentry Data Analysis and Visualization in R (2017).

- [webinars](https://github.com/rstudio/webinars) - Code and slides for RStudio webinars.

### Galaxy

- [galaxy-intro](https://github.com/galaxycam/galaxy-intro) - Introduction to Galaxy: data manipulation and visualisation course (2017).

- [training-material](https://github.com/galaxyproject/training-material) - A collection of Galaxy-related training material.

### Bioconductor

- [ngs-in-bioc](https://github.com/bioinformatics-core-shared-training/ngs-in-bioc) - A course on Analysing NGS data using Bioconductor (2015).

- [BiocIntro](https://github.com/Bioconductor/BiocIntro) - Bioconductor introduction (2017).

- [BioC2016Introduction](https://github.com/Bioconductor/BioC2016Introduction) - Bioconductor introduction (2016).

- [LearnBioconductor](https://github.com/Bioconductor/LearnBioconductor) - Learning R / Bioconductor for Sequence Analysis (2015).

- [UseBioconductor](https://github.com/Bioconductor/UseBioconductor) - Use R / Bioconductor for Sequence Analysis (2015).

- [useR2015](https://github.com/Bioconductor/useR2015) - Course material for useR 2015 workshop -- Bioconductor for High Throughput Sequence Analysis (2015).

- [BioC2015Introduction](https://github.com/Bioconductor/BioC2015Introduction) - Introduction to R and Bioconductor (2015).

- [BiocUruguay2015](https://github.com/Bioconductor/BiocUruguay2015) - R Bio: Untangling Genomes (2015).

- [BiocEMBO2015](https://github.com/Bioconductor/BiocEMBO2015) - EMBO Practical Course: Analysis of High-Throughput Sequencing Data (2015).

- [CSAMA2015](https://github.com/Bioconductor/CSAMA2015) - CSAMA 2015: Statistics and Computing in Genome Data Science (2015).

### Git

- [git-novice](https://github.com/swcarpentry/git-novice) - Software Carpentry Version Control with Git (2017).

### Docker

- [docker-4-bioinformatics](https://github.com/bioinformatics-core-shared-training/docker-4-bioinformatics) - Introduction to Docker for Bioinformatics (2017).