arXane › Quantitative Biology › Quantitative Biology
Wednesday, 4 November 2026: 2 new papers, 0 cross-lists and 1 replacement in Genomics (q-bio.GN), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Genomic foundation models are increasingly used to propose DNA sequences and edits. But successful design requires capabilities that current benchmarks do not measure. Here we introduce BEND 2.0 (BENchmark for DNA language models), which tests how well models predict the effects of DNA changes and whether these predictions can guide sequence design. Across eight tasks built on five experimental datasets, we find that model choice and how representations are accessed both affect performance. Linear readouts fitted to frozen representations can match or exceed models' native outputs, and functional post-training substantially improves the models' capabilities. Linear readouts transfer well across experimental assays and genes, suggesting that models capture functional patterns beyond the data used to fit individual predictors. At the same time, strong prediction of average effects can conceal weaker prediction of context-specific effects, and accurate prediction of combined mutation effects does not imply accurate prediction of their interactions. In design tasks, model predictions can reduce the number of candidates needed to find a desired response, but still leave substantial room for better design choices, particularly for context-specific and multi-cell objectives. Our benchmark reveals that genomic models now display signs of general utility but have yet to saturate on performance in even simple tasks.
Genes of the same family often lie close together on a chromosome, yet how far apart family members sit, and whether their spacing follows any pattern, has rarely been measured consistently across genomes. We built an interactive web application that places the members of any of the 1,396 FlyBase gene groups on the banded ideogram of the fruit fly (Drosophila melanogaster) and lists each member's neighbours. While browsing it, we found strikingly symmetric gene sets in which consecutive members are almost evenly spaced. In the RAB GTPases, two 5.08 Mb spacings differ by only 634 bp. We also found that distances between neighbouring members fall into two populations: short-range (median 1.8 kb) and long-range (median 2.4 Mb). Although an arm-matched random control suggests that the even spacing (without a minimum-distance filter) as well as the long-range population arise largely by chance, the short-range population turns out to be a genuine clustering signal. We then extended the analysis to human and six other model organisms (mouse, zebrafish, Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana and maize), and every genome showed the same two populations. The mixture curve of the two is nearly constant across chromosomes in some genomes (Type I: fly, maize, Arabidopsis, C. elegans) but varies across chromosomes in others (Type II: human, mouse, zebrafish, yeast). Thus, across eight eukaryotes, family members lie in two distance populations. The scale of the short-range population follows the spacing of genes in each genome, and the long-range population matches random placement in fly and is consistent with it in the other genomes. Our results also show that visual patterns such as even spacing must be tested against chance before they are interpreted. Our tool and approach give a simple way to explore and compare gene clustering across species.