Tools and tips for bioinformatics analyses

Resources

Programming languages

The Essentials: Key Programming Languages for Bioinformatics and Data Sciencecore.

Bash

The command-line powerhouse. Bash is your go-to for automating repetitive tasks, managing files, and running pipelines on servers or clusters. In bioinformatics, it’s essential for processing large datasets, running tools like BWA or SAMtools, and chaining commands into efficient workflows.

Python

The Swiss Army knife of bioinformatics. Python’s simplicity and vast ecosystem make it ideal for data analysis, scripting, and machine learning. Use it to parse genomic data, build pipelines, or create interactive visualizations, Python does it all.

R

The statistician’s best friend. R shines in exploratory data analysis, statistical modeling, and publication-ready visualizations (ggplot2, DESeq2). In bioinformatics, it’s the tool of choice for differential expression analysis, clustering, and interpreting high-throughput sequencing data.

Integrated Development Environments

IDE are essential tools for developers, offering a range of features that facilitate coding, debugging, and project management.

Visual Studio Code

Visual Studio Code (VS Code) is a highly popular, open-source code editor developed by Microsoft. It supports a wide range of programming languages and comes with features like IntelliSense code completion, debugging tools, Git integration, and a vast marketplace for extensions.

PyCharm

PyCharm is another IDE from JetBrains, specifically designed for Python development. It provides features like code analysis, graphical debugging, and an integrated terminal. PyCharm also supports web development with Django, Flask, and other frameworks.

Rstudio

RStudio is a powerful and widely-used Integrated Development Environment (IDE) designed to enhance productivity in coding, data analysis, and visualization, primarily for R but also supporting Python. It is the go-to choice for over 90% of R programmers, making it the de facto standard in the field.

Positron

Positron is the next-generation IDE for developing R code. Developed by Posit, it is set to be the successor of RStudio, bringing new features and a smoother user experience.

Plots and figures

packages and websites that will allow you to easily create stunning figures.

R

ggplot2

A leading data visualization package that implements the Grammar of Graphics, making it easy to create complex plots from data in a data frame.

plotly

An interactive graphing library that makes interactive, publication-quality graphs online.

Python

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations.

Seaborn

A statistical data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

Web sites

From data to Viz

Find the most appropriate graph for your data with great examples and code in R and Python.

Datawrapper

Paste your data and create stunning figures directly with eases and speed.

Others

GenoVi

GenoVi generates circular genome representations for complete, draft, and multiple bacterial and archaeal genomes.

Pipelines tools

The best workflows to create analysis pipelines.

Snakemake

Workflow management system that uses a Python-based syntax to create reproducible and scalable data analysis pipelines, making it particularly accessible for users familiar with Python.

Nextflow

Workflow management system that utilizes a domain-specific language based on Groovy, enabling seamless integration of various programming languages and providing robust support for parallel and distributed computing environments.

Web applications

A list of packages and websites to create web applications for your pipelines and scripts.

Shiny

A web application framework for R or Python that can be used to build interactive web applications.

Dash

Dash is the original low-code framework for rapidly building data apps in Python.

Flask

Web application framework designed to make getting started quick and easy, with the ability to scale up to complex applications.

Material

Databases, courses and more.

Glittr

Web application framework designed to make getting started quick and easy, with the ability to scale up to complex applications.

Data & ML
Database

A collection of 200 real-world data science and machine learning case studies across companies.

What statistical test to do ?

A guide to choose the right statistical test to do with your data. Part of an amazing blog about statistics.

Fundamentals of Data Visualization

A complete guide about what to do and not to do when creating figures. And more !

Machine learning, deep learning and LLMs

Links to tutorials or projects for the analysis of genomics data using ML

LLM for genomics data

A very cool project for training and using LLM with sequences data.

Random Forests in R

A complete guide for performing Random Forest in R.

Data manipulation

Diverse tools and packages that will help you handle or analyse your data.

bioconvert

Convert any type of data into any other type of data.

Easyfig

Create multiple genomes comparison figures with easy-to-use GUI.

Biopython

The most complete Python package for manipulating sequencing data (fastq, fasta, Genbank, ...).

Easyfasta

Alternative to Biopython with the advantage of being highly memory-efficient.

SeqKit

A cross-platform and ultrafast toolkit for FASTA/Q file manipulation

Others

Diverse tools or websites that will help you in your work!

regex101

Create, test and fine tuned your regular expressions in an interactive environment.

Bioinformatics_toolkit

A GitHub repository packed with tutorials, analysis examples, and other valuable resources.

sanbox.bio

Practice your Bash commands in a safe sandbox. Experiment freely—no risk of breaking anything!