Address of the bookmark: https://github.com/schatzlab/appliedgenomics

• 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.

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

NO: 340/ESTB/ADMN/NIBMG/2015-16
Doctoral Research In Biomedical Genomics, Including Statistical Genomics conduct National Institute of Biomedical Genomics (NIBMG)
Information For Students Interested To Pursue Doctoral Research In Biomedical Genomics, Including Statistical Genomics, At The National Institute Of Biomedical Genomics (Nibmg), Kalyan
Eligibility conditions for specific areas of research are :
Statistical Genomics : An applicant who wishes to pursue research in Statistical Genomics should hold a Master's degree (First class or equivalent) in a relevant discipline (Statistics, Mathematics, Bioinformatics, or a related discipline)
Biomedical Genomics : An applicant who wishes to pursue research in any area of biomedical genomics, other than statistical genomics, should hold a Master's degree (First class or equivalent) in a relevant discipline (Biochemistry, Biotechnology, Molecular Biology, Genetics, Zoology, Physiology, or a related discipline)
Fellowship : An applicant should have passed the NET conducted by CSIR/UGC/ICMR/DBT within the past ONE year AND should have been awarded a valid Junior Research Fellowship from CSIR, UGC, ICMR, DBT (Category-I only), DST (INSPIRE), NBHM. Preference will be given to candidates with demonstrable research training in the form of summer training or short-term courses in established research laboratories in preparation for a research career in biomedical sciences
How to apply
Online application will be accepted until 5 PM of December 31, 2015. A formal interview of the short-listed candidates will be held on January 12, 2016

More at http://www.nibmg.ac.in/?q=Career%20Opportunities

Address of the bookmark: https://www.stats.ox.ac.uk/__data/assets/pdf_file/0018/5373/LintonFinalReport.pdf

Address of the bookmark: http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html

The postdocs will join an international group and network of researchers at the University of Vienna studying a diverse range of species and questions in molecular evolution, development, morphology and genomics. Our group is also part of the large evolVienna network of more than 50 evolutionary biology labs in Vienna, across several universities and research institutes. Our Faculty will be relocating to a new campus at the Vienna Biocenter in summer 2021 (https://biologiezentrum.univie.ac.at/en/). Vienna is a vibrant historic European capital with a high QOL. Information about postdoctoral salaries in Austria can be found on this webpage: https://www.fwf.ac.at/en/research-funding/personnel-costs/

Earliest start date will be after July 2021. Initial term of employment is for two years with the possibility of extension. Remote working, at least initially, is a possibility.

Requirements:
- PhD degree or equivalent by the start date
- Publishing record in peer-reviewed journals or evidence thereof
- At least 2 letters of support

Applications including a letter of motivation should be submitted via the Job Center to the University of Vienna (https://personalwesen.univie.ac.at/en/jobs-recruiting/job-center/,
job reference number 11615).

Application deadline: January 15th 2021.
Application link: https://univis.univie.ac.at/ebewerbung

http://www.nibmg.ac.in/academic/SyMeC-ICGC/SyMeC%20&%20ICGC_May%202018.pdf

http://www.nibmg.ac.in/academic/plp/15_05_2018/AdvertisementMay2018.pdf

Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf