Tutorial https://github.com/andyrimmer/Platypus/blob/master/misc/README.txt

Address of the bookmark: http://www.well.ox.ac.uk/platypus

Address of the bookmark: https://github.com/yjx1217/LRSDAY

In addition to the knowledge of the human genome, all these genomes, widely sampled across mammals, can be used to research how particular organisms respond to different conditions. Some otters, for example, have a thick, water-resistant shell, and some rodents, but not all, have adapted to hibernation. These animal traits will help us to understand human traits, such as metabolic diseases.

With climate change and more animal ecosystems being threatened by human activity, the protection of endangered species is becoming increasingly important. Scientists have historically researched several people in various populations of a species to understand the genetic variation that occurs in that species. This is important for understanding how particular species can be protected. In this study, animals on the Red List of Endangered Species of the International Union for Conservation of Nature had fewer differences in their genomes, which is consistent with their endangered status.

Ref @ A comparative genomics multitool for scientific discovery and conservation https://www.nature.com/articles/s41586-020-2876-6

 Data at http://zoonomiaproject.org/

Address of the bookmark: https://github.com/sanger-pathogens/ariba/wiki/Task%3A-getref

So, What Is Data Science?
At its core, data science is the interdisciplinary field that extracts knowledge and insights from data using programming, statistics, and domain expertise. In bioinformatics, data science enables us to turn gigabytes of sequence data into biological meaning.

Imagine trying to understand gene regulation in cancer by analyzing thousands of RNA-seq samples, or predicting antibiotic resistance from bacterial genomes—these challenges are not solvable through wet lab experiments alone. They require data-driven thinking.

Data Science Meets Bioinformatics
Bioinformatics is inherently a data science domain. From genomics to systems biology, every field in modern biology relies on data science techniques to:

Clean and process massive datasets

Discover patterns in high-dimensional data

Build predictive models (e.g., for disease classification)

Visualize complex biological networks and trends

Integrate diverse data types (e.g., transcriptomic + epigenomic data)

The Bioinformatics Toolkit
Here’s what data science typically looks like in bioinformatics:

Task Data Science Role
Sequence alignment Efficient algorithms, indexing, parallel processing
Gene expression analysis Statistical modeling (e.g., DESeq2, limma)
Variant calling Data filtering, probabilistic models
Clustering of cells in single-cell data Unsupervised learning
Protein structure prediction Deep learning models (e.g., AlphaFold)
Metagenomics Data integration, classification, dimensionality reduction

Common tools include Python, R, Bioconductor, scikit-learn, Pandas, Seurat, and TensorFlow—often working together in reproducible workflows.

It's Not Just About Coding
A common misconception is that bioinformatics is just programming or scripting. But being a data scientist in bioinformatics also means:

Understanding experimental design

Asking biologically meaningful questions

Choosing the right statistical or machine learning models

Communicating findings effectively (e.g., plots, dashboards, papers)

In other words, data science in bioinformatics is where biology, statistics, and computer science converge.

Why It Matters
The real power of data science in bioinformatics is its ability to scale discovery.

Instead of studying one gene, we can study thousands.

Instead of analyzing one species, we can explore entire ecosystems.

Instead of waiting months for lab results, we can generate hypotheses in days.

From personalized medicine and cancer diagnostics to agricultural genomics and pandemic surveillance, data science is at the heart of the bioinformatics revolution.

Final Thoughts
If you’re a biologist who’s curious about code, or a data enthusiast fascinated by life sciences, bioinformatics is your playground—and data science is your toolkit.

In bioinformatics, data science isn’t just useful. It’s essential.

Welcome to phylobabble.org, a discussion forum for phylogenetic theory and applications. The primary goal of this forum is to discuss best practice and new developments in phylogenetics. Although we do have a Troubleshooting category for getting feedback on analyses, this is not a help site for running phylogenetics programs.

A great place to chat about phylogenetics for researchers and the broader community of students and science-interested citizens. 

Address of the bookmark: http://phylobabble.org/

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

 https://academic.oup.com/bioinformatics/article/34/15/2666/4934939

Address of the bookmark: https://github.com/wdecoster/nanoQC