BOL: Related items

Genome Simulation with SLiM and msprime

BioStar — Fri, 31 Jan 2025 12:47:43 -0600

Genome simulation is an essential tool in population genetics, enabling researchers to model evolutionary processes and study genetic variation. Two widely used simulation tools in this field are SLiM and msprime. While both serve different purposes, they can be used together with the slendr framework to compare simulation outputs effectively.

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

Aurora: A New Detective for Bacterial Genomes

BioStar — Fri, 21 Aug 2026 11:31:00 -0500

Imagine trying to solve a mystery with thousands of clues—but some of the clues are labelled incorrectly.

That is the challenge researchers face when studying how bacteria adapt to different environments. A bacterial gene may appear to be linked to a particular habitat, when the real reason is simply that closely related bacteria happen to live there.

In their 2025 Genome Biology paper, Bujdoš, Walter, and O’Toole introduce aurora, a machine-learning tool designed to tackle this problem.

Aurora identifies potentially mislabeled or unusual bacterial strains before performing genome-wide association studies (GWAS). By cleaning up the dataset and accounting for bacterial evolutionary relationships, it can help researchers find genetic features that are more genuinely connected to habitat adaptation.

The researchers tested aurora using simulated and real bacterial datasets and found that it could recover important genetic associations even when datasets contained misleading labels.

The bigger lesson is simple: better biological discoveries often begin with better data.

Aurora gives researchers a new way to separate real genetic clues from misleading ones—and could help us better understand how bacteria adapt, survive, and evolve in the environments they call home.

Based on Bujdoš et al., “aurora: a machine learning GWAS tool for analyzing microbial habitat adaptation,” Genome Biology (2025).[Read the original paper](https://link.springer.com/article/10.1186/s13059-025-03524-7?)

Luo Lab | Symbiosis Genomics & Evolution

Wed, 09 Sep 2026 03:30:30 -0500

We study the evolutionary genomics of marine invertebrates to understand their origins and diversity. Our lab combines high-throughput sequencing and single-cell transcriptomics to explore a wide range of non-model systems. We are particularly interested in how evolutionary novelty arises, with a focus on animal development and photosymbiosis.

Research Directions

Stony corals: evolution of novelty and photosymbiosis

Symbiotic acoels: cell type evolution and photosymbiosis

Animal genomes: structural evolution and gene regulation

https://sgel.biodiv.tw/home

Mesquite: A modular system for evolutionary analysis

Rahul Nayak — Tue, 13 Mar 2018 06:54:32 -0500

Mesquite is modular, extendible software for evolutionary biology, designed to help biologists organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics, while others do non-phylogenetic multivariate analysis. Because it is modular, the analyses available depend on the modules installed.

https://github.com/MesquiteProject/MesquiteCore

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

Tiara: deep learning-based classification system for eukaryotic sequences

Rahul Nayak — Mon, 14 Mar 2022 23:02:11 -0500

With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding of eukaryotic diversity.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/38/2/344/6375939

Data Steward / Research & Development Specialist at at the Luxembourg Centre for Systems Biomedicine (LCSB)

Sun, 25 Oct 2020 22:36:38 -0500

Applications should be addressed online to: Prof. Dr. Reinhard Schneider, Head of the Bioinformatics Core Facility

For further information, please contact: Dr. Pinar Alper (pinar.alper@uni.lu)

Applications should be submitted online and include:

A detailed curriculum vitae
Cover letter mentioning the reference number
List of publications/software projects
Description of past experience and future interests
Names and addresses of three referees
Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by email will not be considered.

*gn=gender neutral.

More at https://recruitment.uni.lu/en/details.html?nPostingId=54616&nPostingTargetId=74219&id=QMUFK026203F3VBQB7V7VV4S8&LG=UK&mask=karriereseiten&sType=Social%20Recruiting

VirtualFlow: a versatile, parallel workflow platform for carrying out virtual screening

BioStar — Sat, 13 Jun 2020 13:03:09 -0500

related tasks on Linux-based computer clusters of any type and size which are managed by a batchsystem (such as SLURM).

Currently, there exist two versions of VirtualFlow, which are tailored to different types of tasks:

They use the same core technology regarding the workflow management and parallelization, and they can be used individually or in concert with each other. Additional versions are expected to arrive in the future.

https://github.com/VirtualFlow

https://www.nature.com/articles/s41586-020-2117-z?

Address of the bookmark: https://virtual-flow.org/

Dynamic Programming Alignment

Thu, 22 Aug 2013 09:38:28 -0500

lecture 9, Chem. C100, Spring 2013, UCLA

SATSUMA : Highly sensitive whole-genome synteny alignments.

Jit — Fri, 13 May 2016 05:25:26 -0500

Satsuma is a whole-genome synteny alignment program. It takes two genomes, computes alignments, and then keeps only the parts that are orthologous, i.e. following the conserved order and orientation of features, such as protein coding genes, non-coding genes, or neutral sequences. Satsuma does not require any pre-processing, such as repeat masking, since it will automatically detect ambiguous mappings.

Satsuma has parallelization built-in and is designed to run on multi-core architectures. The run-time for aligning two bird-size genomes (~1.2 Gb) is around two days on 24 CPUs.

You can find the manual here.
Download the latest source code from here.
Stable versions can also be downloaded from the Broad Institute's web site.

An incomplete list of questions and answers (yes, these have really been asked by our users! Please feel free to add your own by e-mailing us) is here.

If you use Satsuma in your research, please cite:
Grabherr, M. G., Russell, P., Meyer, M., Mauceli, E., Alföldi, J., Di Palma, F., & Lindblad-Toh, K. (2010). Genome-wide synteny through highly sensitive sequence alignment: Satsuma. Bioinformatics, 26(9), 1145-51.

Tutorial at http://evomics.org/learning/genomics/satsuma/

Address of the bookmark: http://satsuma.sourceforge.net/

YAHA

Jit — Fri, 20 Jan 2017 05:38:05 -0600

YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints.

Availability: YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from http://faculty.virginia.edu/irahall/YAHA.

Contact:

http://genome.wustl.edu/people/groups/detail/hall-lab/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/