Genomes, and in particular differences between them, are a crucial source of information to understand evolution and biology in general. They provide a record of the evolutionary past of populations, their relatedness patterns, their demography, and their adaptations.

More at https://www.uantwerpen.be/en/staff/hannes-svardal/svardal-lab/

Address of the bookmark: https://github.com/esolares/HapSolo

More at https://www.biologiaevolutiva.org/gonzalez_lab/

The NCBI Datasets datasets command line tools are datasets and dataformat .

Use datasets to download biological sequence data across all domains of life from NCBI.

Use dataformat to convert metadata from JSON Lines format to other formats.

Conda download:

https://anaconda.org/conda-forge/ncbi-datasets-cli

Buld Download

 https://www.ncbi.nlm.nih.gov/datasets/builder/?tax_id=29979

Address of the bookmark: https://www.ncbi.nlm.nih.gov/datasets/docs/v1/quickstarts/command-line-tools/

Address of the bookmark: http://www.weizmann.ac.il/Biological_Regulation/IgorUlitsky/PLAR

Address of the bookmark: https://www.ncbi.nlm.nih.gov/nuccore/ON563414

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

• Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

Key Findings of the Study:

1. Alu Insertion and Tail Loss:
   The researchers discovered an Alu-mediated genetic change in a common ancestor of modern apes and humans. This change disrupted the normal function of a gene involved in tail development, leading to the suppression of tail formation.

2. Gene Disruption Mechanism:
   The Alu insertion was found within a regulatory region of the TBXT gene (also known as T or Brachyury), which is crucial for tail development in vertebrates. This insertion likely altered the gene's expression patterns, leading to tail reduction over evolutionary time.

3. Functional Evidence from Model Organisms:
   To test their hypothesis, the researchers introduced similar genetic modifications in mice. The modified mice exhibited shortened or absent tails, supporting the idea that the identified mutation played a role in tail loss in hominoids.

4. Evolutionary Implications:
   The findings suggest that small, random genomic changes—such as transposable element insertions—can have profound effects on body morphology. This study provides evidence that mobile DNA elements (like Alu) can drive major evolutionary transitions.

5. Relevance to Human Evolution:
   Understanding the genetic basis of tail loss helps in reconstructing the evolutionary history of hominins (the lineage that includes humans and our extinct relatives). It also sheds light on how genetic variations contribute to anatomical diversity among primates.

Significance of the Study:

This research highlights the role of transposable elements in shaping evolutionary traits and provides a concrete genetic explanation for a defining characteristic of humans and great apes. It also demonstrates how mutations in regulatory regions of developmental genes can lead to significant anatomical changes.

The authors tested the pipeline across 5 datasets from 4 species, spanning an almost 80-fold difference in sample size (from 136 to over 10,700 individuals) and found that the predictive accuracy was within 0.02-0.05 of the standard GBLUP and Bayesian ridge regression approaches, again without the user having to manually select a model. More importantly the pipeline was able to recover known biological signals (FLC, FT and DOG1 in Arabidopsis, Waxy and GW5 in rice and Glu-D1 in wheat) and produced well calibrated association statistics. The most interesting observation though is that prediction accuracy and biological association are not necessarily the same thing. Two traits with identical predictive performance could have association significance that varied by over 60 orders of magnitude. So knowing how well we can predict a trait doesn't necessarily tell us anything about the distribution of the genetic architecture of that trait across the genome. That's where GenoBridge is actually of value. It's not just another machine learning pipeline. It's an example of an adaptive analytical workflow that doesn't just throw algorithms at the data but asks whether the data actually justify the next analytical step. As the amount of genomic data we generate continues to grow and the number of computational methods available to us increases, the bottleneck may not be in finding the right algorithm at all. The bottleneck may be in deciding which algorithm we can actually trust.

GenoBridge offers a potential solution: let the data tell you which model is appropriate, validate the prediction rigorously and only then move from prediction to biological discovery. That may be where computational genomics is heading. Rather than a future in which there is one single 'best' model, we may be heading towards a future of intelligent systems that know when a model is appropriate, when a result is reliable and when the biology is actually telling us something that we should be listening to.

More at https://link.springer.com/article/10.1186/s13007-026-01596-5

Analysis of regulatory sequences (RSAT project)
Classification and analysis of mobile genetic elements (ACLAME project).
Analysis of molecular interaction networks (NeAT project)
Inference of metabolic pathways from genomic and post-genomic data
(metabolic pathfinding, see also metabolic pathfinding in NeAT)
Critical assesment of protein interactions (CAPRI)

Lab Page http://www.bigre.ulb.ac.be/