Overview of SLiM and msprime

SLiM: Forward Genetic Simulator

SLiM is a free, open-source tool designed for forward genetic simulations. It allows researchers to model complex evolutionary scenarios, including selection, recombination, and demographic events, making it particularly useful for studying adaptation and selection in populations.

Key Features of SLiM:

• Simulates population evolution forward in time

• Supports custom evolutionary models using an embedded scripting language

• Allows modeling of spatial and ecological dynamics

• Provides high flexibility and extensibility for user-defined scenarios

• Available on GitHub as an open-source project

msprime: Ancestry and Mutation Simulator

msprime is an efficient, open-source tool that simulates ancestry and mutations using a coalescent framework. It is known for its high-speed performance and low memory requirements, making it a popular choice for large-scale genomic simulations.

Key Features of msprime:

• Implements coalescent simulations for ancestry modeling

• Efficiently simulates large population histories

• Supports the addition of mutations to genealogies

• Developed using an open-source community model

• Often faster and more memory-efficient than alternative simulators

Using SLiM and msprime with slendr

Both SLiM and msprime can be integrated with slendr, a framework that facilitates structured population genetic simulations. This integration allows for seamless comparison of simulation outputs.

How They Work Together:

• SLiM and msprime simulations can be analyzed within slendr.

• The ts_read() function in slendr enables loading and comparing tree sequence outputs from both simulators.

• This integration allows researchers to validate simulation results and gain deeper insights into evolutionary processes.

Performance Considerations

While SLiM offers powerful forward simulations with extensive customization, msprime is often preferred for its speed and memory efficiency when simulating ancestry and mutations. The choice between the two depends on the research goals:

• For detailed evolutionary modeling with selection and recombination: Use SLiM.

• For large-scale coalescent simulations with mutations: Use msprime.

• For comparing different simulation models and their outputs: Use slendr to integrate SLiM and msprime results.

Conclusion

SLiM and msprime are valuable tools for genome simulation, each serving distinct but complementary purposes in population genetics research. By leveraging the strengths of both simulators with slendr, researchers can conduct robust and efficient evolutionary simulations, enhancing our understanding of genetic diversity and adaptation.

For more information, check out the official GitHub repositories for SLiM and msprime, and explore the slendr framework for streamlined simulation workflow

Pondicherry University Puducherry has been identified as a nodal agency by the Department of Biotechnology, Govt. of India to coordinate this examination along with nine centres namely, Pune University, Pune; Anna University, Chennai; Calcatta University (WBUT) Kolkata; Institute of Bioinformatics & Applied Biotechnology, Bangalore; North-Eastern Hill University, Shillong, University of Hyderabad, Hyderabad; University of Kerala, Thiruvananthapuram; Jawaharlal Nehru University, New Delhi and Assam Agricultural University, Guwahati.

In the BINC 2013 examination,17 candidates were certified. DBT has agreed to fund Research fellowships for all the BINC qualified Indian nationals to pursue Ph.D. in Indian Institutes/Universities. Note that the candidate must possess a postgraduate degree(or equivalent) & meet the criteria of the institutes/universities in order to avail research fellowship. In addition, cash prize of Rs. 10,000/- will be awarded to the top 10 BINC qualifiers.

More at http://210.212.230.224:9999/BINC/

However, pangenome graphs can be highly complex, making them difficult to visualize and interpret. A recent study published in BMC Bioinformatics introduces PG2 (PanGenoGrapher), an open-source, web-based tool designed to address this challenge. The source code and user guide are openly available on GitHub at https://github.com/iVis-at-Bilkent/pangenographer. A publicly accessible sample deployment is hosted at http://pg2.cs.bilkent.edu.tr. In addition, a demonstration video illustrating the primary use cases of PG2 is available at https://www.youtube.com/watch?v=yCd7-aGY6CQ.

PG2 combines advanced graph-layout algorithms with an interactive visualization platform. It allows researchers to explore genomic paths, identify variations, and examine relationships between different parts of a pangenome graph more easily.

This is important because visualization can play a major role in bioinformatics. When complex genomic information is presented clearly, researchers can more easily identify patterns, understand genetic variation, and generate new biological insights.

The development of PG2 represents a step toward making pangenome analysis more accessible and intuitive. As genomic datasets continue to grow and graph-based representations become more common, tools like PG2 can help researchers navigate this increasing complexity.

Ultimately, PG2 demonstrates how combining genomics, graph algorithms, and interactive visualization can make sophisticated biological data easier to understand and analyze.

Reference: Solun, G. K., Dogrusoz, U., Bingöl, Z., & Alkan, C. (2026). PG2: algorithms and a web-based tool for effective layout and visual analysis of pangenome graphs. BMC Bioinformatics. DOI: 10.1186/s12859-026-06555-4.

Applications with complete bio-data from candidates possessing the required qualifications are invited for the posts of JRF (2) and Project Assistant (1) in

DBT, GOI-funded research project “Next Generation Sequencing (NGS)- based de novo assembly of expressed transcripts and genome information of Orchids in North-East India” in DBT’s Twinning programme for NE as per DBT sanction order and norms.

(i) JRF(2 nos.):

Qualifications: M.Tech/M.Sc in Life Sciences/ Botany/ Zoology/Biochemistry/ Biotechnology/ Bioinformatics; Desirable: Aptitude for Bioinformatics and Computer Programming/ Next generation sequencing data analysis

(ii) Project Assistant (1 no.):

Qualifications: Graduation in Science, Desirable: Experience of working in a Life Science/Plant Biotechnology lab. and familiarity with computers and field work viz. collection of samples.

The applications through email bicnehu@gmail.com or post must reach the undersigned
within 15 days from the date of publication of this advertisement. The advertised posts are purely temporary for the duration of the project and subject to availability of the funds from DBT. The appointment does not confer any entitlement or right over the posts for absorption in the University service.

Advertisement: www.nehu.ac.in/Advertisements/BICAdvtPV_200115.pdf

Scientists face a big challenge with the human genome. They can read its letters, but understanding their roles is much harder. Only about 2 percent of the genome codes for proteins. The rest acts like a huge control panel, deciding when and where genes turn on. With about 9 billion possible single-letter changes, testing them all in a lab just isn’t possible.

So, Google DeepMind asked a new question: what if we could predict what those changes might do?

This question led to the AlphaGenome Atlas (https://deepmind.google.com/science/alphagenome/atlas?), a detailed map of nearly every possible single-letter change in the human genome. Instead of checking each change one by one, researchers can use the Atlas to spot the ones most likely to matter. The AlphaGenome Variant Impact (AVI) score works like a trail marker, pointing scientists toward the changes worth a closer look.

This is where the Atlas gets especially useful. Much of the genome lies outside the protein-coding regions, where DNA acts as a switch or controller for genes. AlphaGenome lets researchers explore these areas and see how small changes could affect gene activity.

An atlas isn't the destination; it's a guide.

The AlphaGenome Atlas doesn’t replace experiments or solve every mystery. Instead, it helps scientists decide where to begin. From billions of possibilities, it turns the vast genetic landscape into something researchers can start to explore.

There are nine billion possibilities, a vast map, and maybe among them therWith nine billion possibilities and a huge map to explore, there may be clues hidden here to some of medicine’s toughest mysteries.

More at https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/alphagenome-atlas.pdf

Organiser: Christine Carrington (University of the West Indies - UWI, St. Augustine, Trinidad and Tobago)
Co-organisers: Anne-Mieke Vandamme, Philippe Lemey (Katholieke Universiteit Leuven, Belgium), Marco Salemi, Mattia Prosperi (University of Florida, Gainesville, USA) and Karen E. Nelson (J. Craig Venter Institute, Rockville, USA)

Requests for information directly to:
Christine Carrington
Department of Preclinical Sciences
Faculty of Medical Sciences
University of the West Indies (UWI)
St. Augustine
Trinidad and Tobago
Telephone: +1-868-6452640 ext. 5009, +1-868-6848803
Fax: +1-868-6621873
E-mail: veme2015@sta.uwi.edu

Deadline for receipt of applications by local organiser: 15 March 2015
CALL FOR APPLICATIONS NOW OPEN
http://www.icgeb.org/course-application-trinidad-and-tobago-2015.html

http://rega.kuleuven.be/cev/veme-workshop/2015

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

• Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
• NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
• RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
• ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
• A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
• SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
• WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
• DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
• GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
• Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
• RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
• Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
• Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
• Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
• Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
• RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

Posts Research Associate

Date & Time of Interview 18th Feb 2015 at 10.00 a.m.

Project Name “Centre for Agricultural Bioinformatics (CABin)”

Duration 12th Plan (2012-2017) / co-terminus with the project 12th Plan (2012-2017) / co-terminus with the project

Essential Qualification Ph.d / M.Sc./ M.Phil (Bioinformatics) With 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience in the relevant subject.

Technician

B.Tech. or Master degree in computer science/Computer Engineering /MCA or equivalent. Desirable qualification: Experience in Java/ C++/ PHP/ PERL/ Python etc. based application development using Linux, Apache and MySQL/Oracle.

Emoluments Rs.24000/- p.m. + 30% HRA for Ph.D holders / Rs.23000/- p.m. + 30% HRA for Master Degree holders A consolidated pay Rs.25000/- per month.

Age Limit Maximum 40 years for men and 45 years for women as on date of interview.

Age limits are relaxable for SC / ST / OBC candidates as per rules.

Maximum 40 years for men and 45 years for women as on date of interview.

Age limits are relaxable for SC / ST / OBC candidates as per rules.

Eligible Candidates may send their Curriculum Vitae along with the contact numbers to ashok@ciba.res.in on or before 13-02-2015.

Candidates selected after initial screening will be called for interview.

Advertisement: www.ciba.res.in/attachments/jobs/CABin-3006.pdf

Address of the bookmark: http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html