Perl script for creating a publication-ready vectorial genetic/linkage map in Scalable Vector Graphics (SVG) format. The resulting file can either be submitted for publication and edited with any vectorial drawing software like Inkscape and Abobe Illustrator(R).

The input file must be a text file with at least the marker name (ID), linkage group (LG) and the position (POS) separeted by tabulations. Additionally a logarithm of odds (LOD score) can be provided. Any extra parameter will be ignored.

map.tsv

ID<tab>LG<tab>POS<tab>LOD
13519  12     0       0.250840894
2718   12     1.0     0.250840893
11040  12     1.6     0.252843341
...

https://github.com/pseudogene/genetic-mapper

Address of the bookmark: https://github.com/pseudogene/genetic-mapper

You might learn a lot about that house—the rooms, the doors, the furniture—but you would miss the bigger story: the streets, the neighborhoods, and all the ways the city changes from one place to another.

Something similar happens when scientists study bacterial genomes one at a time.

Over the past decade, researchers have collected thousands of bacterial genomes. These genomes contain an enormous amount of information about how bacteria survive, adapt, and evolve. But comparing thousands of individual genomes can quickly become a computational maze.

That is where PANORAMA enters the story.

Developed by Jérôme Arnoux and colleagues, PANORAMA is a computational tool designed to explore bacterial pangenomes—the complete collection of genetic possibilities found across a species or group of related organisms. Instead of looking at every genome as an isolated object, the approach represents their shared and variable genetic features as a graph.

Think of this graph as a giant subway map.

Some stations appear on almost every route. These represent genes that are highly conserved. Other stations appear only on certain routes, representing genes that some bacteria possess while others do not. By looking at the entire network, scientists can begin to see not just *what genes exist*, but how they are organized and how biological systems are distributed across populations.

The researchers put PANORAMA to the test using 941 genomes of Pseudomonas aeruginosa, a bacterium important in human health. They used the tool to investigate biological systems, including bacterial defense mechanisms against viruses known as bacteriophages. They then expanded the analysis to more than 6,000 genomes from four Enterobacteriaceae species.

The result is more than a faster way to process data.

PANORAMA allows researchers to ask a bigger question: What can an entire microbial species do, genetically speaking?

By comparing pangenomes, the researchers could identify systems shared between species as well as distinctive features. They could also find recurring genomic locations where genetic material is inserted—clues that may reveal common evolutionary processes.

And this is perhaps the most exciting part of the story.

Every bacterial genome is like a page in a huge evolutionary book. Until recently, reading thousands of those pages together was difficult. PANORAMA provides a way to turn those pages into a map, allowing scientists to see patterns that might disappear when each genome is studied separately.

The study, published in PLOS Computational Biology in July 2026, presents PANORAMA as a foundation for large-scale comparative pangenomics. The software and accompanying analysis resources are openly available, giving other researchers the opportunity to explore microbial diversity themselves.

So the story is not really about one bacterium or one genome.

It is about changing the way we look at life.

Instead of asking, “What is inside this genome?”, scientists can increasingly ask, “What is the full genetic landscape of this species—and how did it become this way?”

And sometimes, when you stop looking at one house and finally see the whole city, the most interesting discoveries are hiding in the streets between them.

Read more at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013856

Stable version with pip:

pip3 install --user chromosight

Stable version with conda:

conda install -c bioconda -c conda-forge chromosight

or, if you want to get the latest development version:

pip3 install --user -e git+https://github.com/koszullab/chromosight.git@master#egg=chromosight

Address of the bookmark: https://github.com/koszullab/Chromosight

Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

This tool is a standalone version of Ragout module [http://fenderglass.github./Ragout]

Address of the bookmark: https://github.com/fenderglass/maf2synteny

Address of the bookmark: http://genome.cs.nthu.edu.tw/Multi-CAR/

https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454

Address of the bookmark: http://ani.mypathogen.cn/

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

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

Krona

https://sourceforge.net/p/krona/home/krona/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/