• A simple intro to statistical distributions
• hypothesis testing
• linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

• Understanding basic intuition behind machine learning approaches.
• Using unsupervised learning to cluster and visualise data points
• Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

• Understanding and using supervised learning methods for predictive purposes
• How to measure prediction performance
• Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

• Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
• Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

• New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
• Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
• Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

https://github.com/remicnrd/ml_cheatsheet

Visual AI / ML

https://setosa.io/ev/

Simple and efficient tools for predictive data analysis

https://scikit-learn.org/stable/

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

To run:

perl centro.pl

A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".

The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures  In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.

More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf

Population genetics: A concise guide. John Gillespie.The Johns Hopkins University Press (1997)•

Population genetics. J. S. Gale. Wiley (1980)•

Evolutionary genetics. John Maynard-Smith. Oxford University Press (1998)•

The growth of biological thought. Ernst Mayr. Harvard University Press (1985)•

Guns, germs and steel. Jared Diamond. W. W. Norton (2007)•

Evolutionary theory: Mathematical and conceptual foundations. Sean Rice. Oxford University Press (2004)

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0