A prominent challenge in computational biology is to distinguish microbial haplotypes -- closely related organisms with highly similar genomes -- due to small genomic differences that can cause significant phenotypic variations. Current genome assembly tools struggle with distinguishing these haplotypes, especially for long-read sequencing data with high error rates, such as PacBio or Oxford Nanopore Technology (ONT) reads. While existing methods work well for either viral or bacterial haplotypes, they often fail with low-abundance haplotypes and are computationally intensive.

This work by Faure, Lavenier, and Flot [1] introduces a new tool -- HairSplitter -- that offers a solution for both viral and bacterial haplotype separation, even with error-prone long reads. It does this by efficiently calling variants, clustering reads into haplotypes, creating new separated contigs, and resolving the assembly graph. A key advantage of HairSplitter is that it is entirely parameter-free and does not require prior knowledge of the organism's ploidy. HairSplitter is designed to handle both metaviromes and bacterial metagenomes, offering a more versatile and efficient solution than existing tools, like stRainy [2], Strainberry [3], and hifiasm-meta [4].

References

[1] Roland Faure, Dominique Lavenier, Jean-François Flot (2024) HairSplitter: haplotype assembly from long, noisy reads. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2024.02.13.580067

[2] Kazantseva E, A Donmez, M Pop, and M Kolmogorov (2023). stRainy: assembly-based metagenomic strain phasing using long reads. Bioinformatics. https://doi.org/10.1101/2023.01.31.526521

[3] Vicedomini R, C Quince, AE Darling, and R Chikhi (2021). Strainberry: automated strain separation in low complexity metagenomes using long reads. Nature Communications, 12, 4485. ISSN: 2041-1723. https://doi.org/10.1038/s41467-021-24515-9

[4] Feng X, H Cheng, D Portik, and H Li (2022). Metagenome assembly of high-fidelity long reads with hifiasm-meta. Nature Methods, 19, 1–4. https://doi.org/10.1038/s41592-022-01478-3

This article by Clenet et al. [1] tackles a fundamental mathematical model in ecology to understand the impact of the architecture of interactions on the equilibrium of the system.

The authors consider the classical Lotka-Volterra model, depicting the effect of interactions between species on their abundances. They focus on the case whenever there are numerous species, and where their interactions are compartmentalized in a block structure. Each block has a strength coefficient, applied to a random Gaussian matrix. This model aims at capturing the structure of interacting communities, with blocks describing the interactions within a community, and other blocks the interactions between communities.

In this general mathematical framework, the authors demonstrate sufficient conditions for the existence and uniqueness of a stable equilibrium, and conditions for which the equilibrium is feasible. Moreover, they derive statistical heuristics for the proportion, mean, and distribution of abundance of surviving species.
While the main text focuses on the case of two interacting communities, the authors provide generalizations to an arbitrary number of blocks in the appendix.

Overall, the article constitutes an original and solid contribution to the study of mathematical models in ecology. It combines mathematical analysis, dynamical system theory, numerical simulations, grounded with relevant hypothesis for the modeling of ecological systems.
The obtained results pave the way to further research, both towards further mathematical proofs on the model analysis, and towards additional model features relevant for ecology, such as spatial aspects.

References

[1] Maxime Clenet, François Massol, Jamal Najim (2023) Impact of a block structure on the Lotka-Volterra model. arXiv, ver.3 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.48550/arXiv.2311.09470

VDJ recombination is a crucial process in the immune system, where a V (variable) gene, a D (diversity) gene, and a J (joining) gene are randomly combined to create unique antigen receptor genes. This process generates a vast diversity of antibodies and T-cell receptors, essential for recognizing and combating a wide array of pathogens. By identifying and quantifying these VDJ recombinations, we can gain a deeper and more precise understanding of the immune response, enhancing our ability to monitor and manage immune-related conditions.

It is therefore important to develop efficient methods to identify and extract VDJ recombinations from large sequences (e.g., several millions/billions of nucleotides). The work by Borée, Giraud, and Salson [2] contributes one such algorithm. As in previous work, the proposed algorithm employs the Aho-Corasick automaton to simultaneously match several patterns against a string but, differently from other methods, it also combines the efficiency of spaced seeds. Working with seeds rather than the original string has the net benefit of speeding up the algorithm and reducing its memory usage, sometimes at the price of a modest loss in accuracy. Experiments conducted on five different datasets demonstrate that these features grant the proposed method excellent practical performance compared to the best previous methods, like Vidjil [3] (up to 5X faster) and MiXCR [1] (up to 30X faster), with no quality loss.

The method can also be considered an excellent example of a more general trend in scalable algorithmic design: adapt "classic" algorithms (in this case, the Aho-Corasick pattern matching algorithm) to work in sketch space (e.g., the spaced seeds used here), trading accuracy for efficiency. Sometimes, this compromise is necessary for the sake of scaling to very large datasets using modest computing power.

References

[1] D. A. Bolotin, S. Poslavsky, I. Mitrophanov, M. Shugay, I. Z. Mamedov, E. V. Putintseva, and D. M. Chudakov (2015). "MiXCR: software for comprehensive adaptive immunity profiling." Nature Methods 12, 380–381. ISSN: 1548-7091. https://doi.org/10.1038/nmeth.3364

[2] C. Borée, M. Giraud, M. Salson (2024) "Alignment-free detection and seed-based identification of multi-loci V(D)J recombinations in Vidjil-algo". https://hal.science/hal-04361907v2, version 2, peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology.

[3] M. Giraud, M. Salson, M. Duez, C. Villenet, S. Quief, A. Caillault, N. Grardel, C. Roumier, C. Preudhomme, and M. Figeac (2014). "Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing". BMC Genomics 15, 409. https://doi.org/10.1186/1471-2164-15-409. 

DNA supercoiling, the under or overwinding of DNA, is known to strongly impact gene expression, as changes in levels of supercoiling directly influence transcription rates. In turn, gene transcription generates DNA supercoiling on each side of an advancing RNA polymerase. This coupling between DNA supercoiling and transcription may result in different outcomes, depending on neighboring gene orientations: divergent genes tend to increase transcription levels, convergent genes tend to inhibit each other, while tandem genes may exhibit more intricate relationships.

While several works have investigated the relationship between transcription and supercoiling, Grohens et al [1] address a different question: how does transcription-supercoiling coupling drive genome evolution? To this end, they consider a simple model of gene expression regulation where transcription level only depends on the local DNA supercoiling and where the transcription of one gene generates a linear profile of positive and negative DNA supercoiling on each side of it. They then make genomes evolve through genomic inversions only considering a fitness that reflects the ability of a genome to cope with two distinct environments for which different genes have to be activated or repressed.

Using this simple model, the authors illustrate how evolutionary adaptation via genomic inversions can adjust expression levels for enhanced fitness within specific environments, particularly with the emergence of relaxation-activated genes. Investigating the genomic organization of individual genomes revealed that genes are locally organized to leverage the transcription-supercoiling coupling for activation or inhibition, but larger-scale networks of genes are required to strongly inhibit genes (sometimes up to networks of 20 genes). Thus, supercoiling-mediated interactions between genes can implicate more than just local genes. Finally, they construct an "effective interaction graph" between genes by successively simulating gene knock-outs for all of the genes of an individual and observing the effect on the expression level of other genes. They observe a densely connected interaction network, implying that supercoiling-based regulation could evolve concurrently with genome organization in bacterial genomes.

References

[1] Théotime Grohens, Sam Meyer, Guillaume Beslon (2024) Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology  https://doi.org/10.1101/2022.09.23.509185

It is often the case that scientific knowledge exists but is scattered across numerous experimental studies. Because of this dispersion in different formats, it remains difficult to access, extract, reproduce, confirm or generalise. This is the case in crop science, where Mahmoud et al [1] propose to collect and assemble data from numerous field experiments on intercropping.

It happens that the construction of the global dataset requires a lot of time, attention and a well thought-out method, inspired by the literature on data science [2] and adapted to the specificities of crop science. This activity also leads to new possibilities that were not available in individual datasets, such as the detection of full factorial designs using graph theory tools developed on top of the global dataset.

The study by Mahmoud et al [1] has thus multiple dimensions:

• The description of the solutions given to this data assembly challenge.
• The illustration of the usefulness of such procedure in a case study of 37 field experiments on cereal-legume associations. The dataset is publicly available [3], while some results obtained from it have been independently published elsewhere [e.g. 4].
• The description of an algorithm able to detect complete factorial designs.
• An informed discussion of the merits of global datasets compared to alternatives, in particular meta-analyses
• A documented reflection on scientific practices in the era of big data, guided by the principles of open science.

I was particularly interested in the promotion of the FAIR principles, perhaps used a little too uncritically in my view, as an obvious solution to data sharing. On the one hand, I am admiring and grateful for the availability of these data, some of which have never been published, nor associated with published results. This approach is likely to unearth buried treasures. On the other hand, I can understand the reluctance of some data producers to commit to total, definitive sharing, facilitating automatic reading, without having thought about a certain reciprocity on the part of users and use by artificial intelligence. Reciprocity in terms of recognition, as is discussed by Mahmoud et al [1], but also in terms of contribution to the commons [5] or reading conditions for machine learning.
But this is another subject, to be dealt with in the years to come, and for which, perhaps, the contribution recommended here will be enlightening.

References

[1] Mahmoud R., Casadebaig P., Hilgert N., Gaudio N. A workflow for processing global datasets: application to intercropping. 2024. ⟨hal-04145269v2⟩ ver. 2 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://hal.science/hal-04145269

[2] Wickham, H. 2014. Tidy data. Journal of Statistical Software 59(10) https://doi.org/10.18637/jss.v059.i10

[3] Gaudio, N., R. Mahmoud, L. Bedoussac, E. Justes, E.-P. Journet, et al. 2023. A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops. https://doi.org/10.5281/zenodo.8081577

[4] Mahmoud, R., Casadebaig, P., Hilgert, N. et al. Species choice and N fertilization influence yield gains through complementarity and selection effects in cereal-legume intercrops. Agron. Sustain. Dev. 42, 12 (2022). https://doi.org/10.1007/s13593-022-00754-y

[5] Bernault, C. « Licences réciproques » et droit d'auteur : l'économie collaborative au service des biens communs ?. Mélanges en l'honneur de François Collart Dutilleul, Dalloz, pp.91-102, 2017, 978-2-247-17057-9. https://shs.hal.science/halshs-01562241