EXCON: a scalable, reproducible Nextflow pipeline for CAFE-based gene EXpansion and CONtraction analysis
EXCON is a scalable, automated Nextflow pipeline that streamlines the entire workflow for CAFE-based gene family expansion and contraction analysis—from genome preprocessing and ortholog identification to birth-death modeling and functional enrichment—enabling reproducible evolutionary comparative genomics with minimal user configuration.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
Life is written in a code of genes, but the length of that code is not fixed. Just as a library might acquire new books or lose old ones over time, the genomes of living species gain and lose copies of their genes. Sometimes a gene duplicates, creating extra copies that can evolve new functions; other times, a gene is lost entirely. These shifts in gene copy numbers are a primary way that lineages adapt to new environments, develop complex traits, or engage in the endless biological arms races between hosts and pathogens. To understand how a species evolved, scientists often look for these specific expansions and contractions in the genetic record. However, finding them has historically been a difficult task, requiring a high level of technical skill to download data, clean it, and run complex statistical models that track these changes across the tree of life.
A team of researchers at University College London has now built a tool that removes this barrier, turning a multi-step, expert-only process into a single, automated workflow. They created a pipeline called EXCON, which acts as a complete guide for analyzing how gene families grow and shrink. Instead of requiring a researcher to manually download genome files, format them, and run separate programs to find related genes and model their evolution, EXCON handles every step from start to finish. It begins by gathering genome data, checks the quality of that data, identifies groups of related genes across different species, and then uses a statistical model to calculate exactly where and when gene families expanded or contracted. The system is designed to be portable, meaning it can run on a standard laptop, a powerful university supercomputer, or in the cloud, and it automatically adjusts its calculations to ensure the results are reliable.
To test if their new tool worked, the researchers applied it to a diverse group of twenty-four primate species, ranging from lemurs and lorises to monkeys, apes, and humans. This dataset covers roughly seventy-one million years of evolution. The pipeline successfully processed the data and identified three hundred and fifty-eight gene families that were evolving rapidly, showing significant changes in their copy numbers across the primate family tree. One of the most striking findings involved a specific gene family known as DUX, which is located in a highly unstable region of the genome. The analysis revealed that this gene family did not just change once in a single lineage; instead, it underwent repeated, independent bursts of expansion and contraction across many different branches of the primate tree. For instance, the gene family expanded dramatically in macaques and gorillas but contracted in baboons and other species. This pattern matched what scientists already knew about the physical instability of the DNA region where this gene lives, confirming that the automated pipeline could detect genuine, complex evolutionary signals without any prior human curation.
Beyond identifying specific genes, the researchers used the pipeline to understand the broader functions of the genes that were changing. They found that genes involved in the basic machinery of making proteins, such as those found in ribosomes, tended to expand across the entire group of primates. In contrast, genes related to energy production in the mitochondria showed signs of contraction, but only in specific lineages like humans and certain monkeys. A particularly clear signal emerged in the lemurs, where genes responsible for the sense of smell expanded significantly. This aligns with the known biology of these animals, which rely more heavily on their sense of smell than on their vision, a trait that distinguishes them from the other primates in the study. The researchers also noted that while the tool is robust, it can sometimes struggle with very fragmented or low-quality genome assemblies, which might create false signals of gene loss or gain, so users are advised to use the best available data.
The development of EXCON represents a shift toward making complex genomic analysis accessible to a wider community of biologists who may not have specialized training in computer science. By integrating the entire workflow into a single, reproducible package, the tool ensures that results can be verified and repeated by anyone, anywhere. The researchers demonstrated that the pipeline could handle datasets ranging from bacteria to insects and mammals, adapting to different levels of genetic diversity and genome quality. While the tool currently focuses on gene family evolution, the team plans to expand its capabilities in the future to include other types of genetic analysis. For now, EXCON stands as a fully automated solution that allows scientists to ask fundamental questions about how life changes over time, turning a once-daunting technical challenge into a straightforward, reliable process.
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