### Latest recommendations

Id▲ | Title * | Authors * | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date | |
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18 Apr 2023
## Cancer phylogenetic tree inference at scale from 1000s of single cell genomesSohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côtéhttps://doi.org/10.1101/2020.05.06.058180
## Phylogenetic reconstruction from copy number aberration in large scale, low-depth genome-wide single-cell data.Recommended by
Amaury Lambert based on reviews by 3 anonymous reviewersThe paper [1] presents and applies a new Bayesian inference method of phylogenetic reconstruction for multiple sequence alignments in the case of low sequencing coverage but diverse copy number aberrations (CNA), with applications to single cell sequencing of tumors. The idea is to take advantage of CNA to reconstruct the topology of the phylogenetic tree of sequenced cells in a first step (the `sitka' method), and in a second step to assign single nucleotide variants (SNV) to tree edges (and then calibrate their lengths) (the `sitka-snv' method). The data are summarized into a binary-valued CxL matrix Y, where C is the number of cells and L is the number of loci (here, loci are segments of prescribed length called `bins'). The entry of Y at row i and column j is 1 (otherwise 0) iff in the ancestral lineage of cell i, at least one genomic rearrangement has occurred, and more specifically the gain or loss of a segment with at least one endpoint in locus j or in locus j+1. The authors expect the infinite-allele assumption to approximately hold (i.e., that at most one mutation occurs at any given marker and that 0 is the ancestral state). They refer to this assumption as the `perfect phylogeny assumption'. By only recording from CNA events the endpoints at which they occur, the authors lose the information on copy number, but they gain the assumption of independence of the mutational processes occurring at different sites, which approximately holds for CNA endpoints. The goal of sitka is to produce a posterior distribution on phylogenetic trees conditional on the matrix Y , where here a phylogenetic tree is understood as containing the information on 1) the topology of the tree but not its edge lengths, and 2) for each edge, the identity of markers having undergone a mutation, in the sense of the previous paragraph. The results of the method are tested against synthetic datasets simulated under various assumptions, including conditions violating the perfect phylogeny assumption and compared to results obtained under other baseline methods. The method is extended to assign SNV to edges of the tree inferred by sitka. It is also applied to real datasets of single cell genomes of tumors. The manuscript is very well-written, with a high degree of detail. The method is novel, scalable, fast and appears to perform favorably compared to other approaches. It has been applied in independent publications, for example to multi-year time-series single-cell whole-genome sequencing of tumors, in order to infer the fitness landscape and its dynamics through time, see [2]. The reviewing process has taken too long, mainly because of other commitments I had during the period and to the difficulty of finding reviewers. Let me apologize to the authors and thank them for their patience as well as for the scientific rigor they brought to their revisions and answers to reviewers, who I also warmly thank for their quality work.
[1] Sohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côté. Cancer phylogenetic tree inference at scale from 1000s of single cell genomes (2023). bioRxiv, 2020.05.06.058180, ver. 4 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. [2] Sohrab Salehi, Farhia Kabeer, Nicholas Ceglia, Mirela Andronescu, Marc J. Williams, Kieran R. Campbell, Tehmina Masud, Beixi Wang, Justina Biele, Jazmine Brimhall, David Gee, Hakwoo Lee, Jerome Ting, Allen W. Zhang, Hoa Tran, Ciara O’Flanagan, Fatemeh Dorri, Nicole Rusk, Teresa Ruiz de Algara, So Ra Lee, Brian Yu Chieh Cheng, Peter Eirew, Takako Kono, Jenifer Pham, Diljot Grewal, Daniel Lai, Richard Moore, Andrew J. Mungall, Marco A. Marra, IMAXT Consortium, Andrew McPherson, Alexandre Bouchard-Côté, Samuel Aparicio & Sohrab P. Shah. Clonal fitness inferred from time-series modelling of single-cell cancer genomes (2021). Nature 595, 585–590. https://doi.org/10.1038/s41586-021-03648-3 | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes | Sohrab Salehi, Fatemeh Dorri, Kevin Chern, Farhia Kabeer, Nicole Rusk, Tyler Funnell, Marc J Williams, Daniel Lai, Mirela Andronescu, Kieran R. Campbell, Andrew McPherson, Samuel Aparicio, Andrew Roth, Sohrab Shah, and Alexandre Bouchard-Côté | <p style="text-align: justify;">A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction ... | Evolutionary Biology, Genetics and population Genetics, Genomics and Transcriptomics, Machine learning, Probability and statistics | Amaury Lambert | 2021-12-10 17:08:04 | View | ||

02 May 2023
## Population genetics: coalescence rate and demographic parameters inferenceOlivier Mazet, Camille Noûshttps://doi.org/10.48550/arXiv.2207.02111
## Estimates of Effective Population Size in Subdivided PopulationsRecommended by
Alan Rogers based on reviews by 2 anonymous reviewersWe often use genetic data from a single site, or even a single individual, to estimate the history of effective population size, Consider for example the estimates of archaic population size in Fig. 1, which show an apparent decline in population size between roughly 700 kya and 300 kya. It is tempting to interpret this as evidence of a declining number of individuals, but that is not the only plausible interpretation. Each of these estimates is based on the genome of a single diploid individual. As we trace the ancestry of that individual backwards into the past, the ancestors are likely to remain in the same locale for at least a generation or two. Being neighbors, there’s a chance they will mate. This implies that in the recent past, the ancestors of a sampled individual lived in a population of small effective size. As we continue backwards into the past, there is more and more time for the ancestors to move around on the landscape. The farther back we go, the less likely they are to be neighbors, and the less likely they are to mate. In this more remote past, the ancestors of our sample lived in a population of larger effective size, even if neither the number of individuals nor the rate of gene flow has changed. For awhile then, This simple story gets more complex if there is change in either the census size or the rate of gene flow. Mazet and Noûs [2] have shown that one can mimic real estimates of population history using models in which the rate of gene flow varies, but census size does not. This implies that the curves in Fig. 1 are ambiguous. The observed changes in For this reason, Mazet and Noûs [2] would like to replace the term “effective population size” with an alternative, the “inverse instantaneous coalescent rate,” or IIRC. I don’t share this preference, because the same critique could be made of all definitions of Figure 1: PSMC estimates of the history of population size based on three archaic genomes: two Neanderthals and a Denisovan [1]. Mazet and Noûs [2] also show that estimates of In summary, this article describes several processes that can affect estimates of the history of effective population size. This makes existing estimates ambiguous. For example, should we interpret Fig. 1 as evidence of a declining number of archaic individuals, or in terms of gene flow among archaic subpopulations? But these questions also present research opportunities. If the observed decline reflects gene flow, what does this imply about the geographic structure of archaic populations? Can we resolve the ambiguity by integrating samples from different locales, or using archaeological estimates of population density or interregional trade?
[1] Fabrizio Mafessoni et al. “A high-coverage Neandertal genome from Chagyrskaya Cave”. Proceedings of the National Academy of Sciences, USA 117.26 (2020), pp. 15132–15136. https://doi.org/10.1073/pnas.2004944117. [2] Olivier Mazet and Camille Noûs. “Population genetics: coalescence rate and demographic parameters inference”. arXiv, ver. 2 peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology (2023). https://doi.org/10.48550/ARXIV.2207.02111. [3] Sewall Wright. “Evolution in mendelian populations”. Genetics 16 (1931), pp. 97–159. https://doi.org/10.48550/ARXIV.2207.0211110.1093/genetics/16.2.97. | Population genetics: coalescence rate and demographic parameters inference | Olivier Mazet, Camille Noûs | <p style="text-align: justify;">We propose in this article a brief description of the work, over almost a decade, resulting from a collaboration between mathematicians and biologists from four different research laboratories, identifiable as the c... | Genetics and population Genetics, Probability and statistics | Alan Rogers | Joseph Lachance, Anonymous | 2022-07-11 14:03:04 | View | |

14 Mar 2023
## Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesisLoïc Paulevéhttps://doi.org/10.48550/arXiv.2207.13307
## Reprogramming of locally-monotone Boolean networks with BoNesisRecommended by
Sergiu Ivanov based on reviews by Ismail Belgacem and 1 anonymous reviewerReprogramming of cellular networks is a well known challenge in computational biology consisting first of all in properly representing an ensemble of networks having a role in a phenomenon of interest, and secondly in designing strategies to alter the functioning of this ensemble in the desired direction. Important applications involve disease study: a therapy can be seen as a reprogramming strategy, and the disease itself can be considered a result of a series of adversarial reprogramming actions. The origins of this domain go back to the seminal paper by Barabási et al. [1] which formalized the concept of network medicine. An abstract tool which has gathered considerable success in network medicine and network biology are Boolean networks: sets of Boolean variables, each equipped with a Boolean update function describing how to compute the next value of the variable from the values of the other variables. Despite apparent dissimilarity with the biological systems which involve varying quantities and continuous processes, Boolean networks have been very effective in representing biological networks whose entities are typically seen as being on or off. Particular examples are protein signalling networks as well as gene regulatory networks. The paper [2] by Loïc Paulevé presents a versatile tool for tackling reprogramming of Boolean networks seen as models of biological networks. The problem of reprogramming is often formulated as the problem of finding a set of perturbations which guarantee some properties on the attractors. The work [2] relies on the most permissive semantics [3], which together with the modelling assumption allows for considerable speed-up in the practically relevant subclass of locally-monotone Boolean networks. The paper is structured as a tutorial. It starts by introducing the formalism, defining 4 different general variants of reprogramming under the most permissive semantics, and presenting evaluations of their complexity in terms of the polynomial hierarchy. The author then describes the software tool BoNesis which can handle different problems related to Boolean networks, and in particular the 4 reprogramming variants. The presentation includes concrete code examples with their output, which should be very helpful for future users. The paper [2] introduces a novel scenario: reprogramming of ensembles of Boolean networks delineated by some properties, including for example the property of having a given interaction graph. Ensemble reprogramming looks particularly promising in situations in which the biological knowledge is insufficient to fully determine all the update functions, i.e. in the majority of modelling situations. Finally, the author also shows how BoNesis can be used to deal with sequential reprogramming, which is another promising direction in computational controllability, potentially enabling more efficient therapies [4,5]. REFERENCES- Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12, 56–68. https://doi.org/10.1038/nrg2918
- Paulevé L (2023) Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis. arXiv, ver. 2 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.48550/arXiv.2207.13307
- Paulevé L, Kolčák J, Chatain T, Haar S (2020) Reconciling qualitative, abstract, and scalable modeling of biological networks. Nature Communications, 11, 4256. https://doi.org/10.1038/s41467-020-18112-5
- Mandon H, Su C, Pang J, Paul S, Haar S, Paulevé L (2019) Algorithms for the Sequential Reprogramming of Boolean Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16, 1610–1619. https://doi.org/10.1109/TCBB.2019.2914383
- Pardo J, Ivanov S, Delaplace F (2021) Sequential reprogramming of biological network fate. Theoretical Computer Science, 872, 97–116. https://doi.org/10.1016/j.tcs.2021.03.013
| Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis | Loïc Paulevé | <p style="text-align: justify;">Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinat... | Combinatorics, Computational complexity, Dynamical systems, Molecular Biology, Systems biology | Sergiu Ivanov | Ismail Belgacem, Anonymous | 2022-08-31 15:00:21 | View | |

10 Apr 2024
## Revisiting pangenome openness with k-mersLuca Parmigiani, Roland Wittler, Jens Stoyehttps://doi.org/10.1101/2022.11.15.516472
## Faster method for estimating the openness of speciesRecommended by
Leo van Iersel based on reviews by Guillaume Marçais, Abiola Akinnubi and 1 anonymous reviewerWhen sequencing more and more genomes of a species (or a group of closely related species), a natural question to ask is how quickly the total number of distinct sequences grows as a function of the total number of sequenced genomes. A similar question can be asked about the number of distinct genes or the number of distinct
[1] Parmigiani L., Wittler, R. and Stoye, J. (2024) "Revisiting pangenome openness with k-mers". bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology. https://doi.org/10.1101/2022.11.15.516472 | Revisiting pangenome openness with k-mers | Luca Parmigiani, Roland Wittler, Jens Stoye | <p style="text-align: justify;">Pangenomics is the study of related genomes collectively, usually from the same species or closely related taxa. Originally, pangenomes were defined for bacterial species. After the concept was extended to eukaryoti... | Combinatorics, Genomics and Transcriptomics | Leo van Iersel | Guillaume Marçais, Yadong Zhang | 2022-11-22 14:48:18 | View | |

12 Oct 2023
## When Three Trees Go to WarLeo van Iersel and Mark Jones and Mathias Wellerhttps://hal.science/hal-04013152v3
## Bounding the reticulation number for three phylogenetic treesRecommended by
Simone Linz based on reviews by Guillaume Scholz and Stefan GrünewaldReconstructing a phylogenetic network for a set of conflicting phylogenetic trees on the same set of leaves remains an active strand of research in mathematical and computational phylogenetic since 2005, when Baroni et al. [1] showed that the Since 2005, many papers have been published that develop exact algorithms and heuristics to solve the above NP-hard minimisation problem in practice, which is often referred to as In [2], van Iersel, Jones, and Weller establish the first lower bound for the minimum reticulation number for more than two rooted binary phylogenetic trees, with a focus on exactly three trees. The above-mentioned connection between the minimum number of reticulations and maximum acyclic agreement forests does not extend to three (or more) trees. Instead, to establish their result, the authors use multi-labelled trees as an intermediate structure between phylogenetic trees and phylogenetic networks to show that, for each
[1] Baroni, M., Grünewald, S., Moulton, V., and Semple, C. (2005) "Bounding the number of hybridisation events for a consistent evolutionary history". J. Math. Biol. 51, 171–182. https://doi.org/10.1007/s00285-005-0315-9 | When Three Trees Go to War | Leo van Iersel and Mark Jones and Mathias Weller | <p style="text-align: justify;">How many reticulations are needed for a phylogenetic network to display a given set of k phylogenetic trees on n leaves? For k = 2, Baroni, Semple, and Steel [Ann. Comb. 8, 391-408 (2005)] showed that the answer is ... | Combinatorics, Evolutionary Biology, Graph theory | Simone Linz | 2023-03-07 18:49:21 | View | ||

18 Sep 2023
## Minimal encodings of canonical k-mers for general alphabets and even k-mer sizesRecommended by
Paul Medvedev based on reviews by 2 anonymous reviewersAs part of many bioinformatics tools, one encodes a k-mer, which is a string, into an integer. The natural encoding uses a bijective function to map the k-mers onto the interval [0, s^k - ], where s is the alphabet size. This encoding is However, often one is only interested in encoding canonical k-mers. One common definition is that a k-mer is In [1], the author presents a minimal encoding for canonical k-mers that works for general alphabets and both odd and even k. They also give an efficient bit-based representation for the DNA alphabet. This paper fills a theoretically interesting and often overlooked gap in how to encode k-mers as integers. It is not yet clear what practical applications this encoding will have, as the author readily acknowledges in the manuscript. Neither the author nor the reviewers are aware of any practical situations where the lack of a minimal encoding "leads to serious limitations." However, even in an applied field like bioinformatics, it would be short-sighted to only value theoretical work that has an immediate application; often, the application is several hops away and not apparent at the time of the original work. In fact, I would speculate that there may be significant benefits reaped if there was more theoretical attention paid to the fact that k-mers are often restricted to be canonical. Many papers in the field sweep under the rug the fact that k-mers are made canonical, leaving it as an implementation detail. This may indicate that the theory to describe and analyze this situation is underdeveloped. This paper makes a step forward to develop this theory, and I am hopeful that it may lead to substantial practical impact in the future.
[1] Roland Wittler (2023) "General encoding of canonical k-mers. bioRxiv, ver.2, peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology https://doi.org/10.1101/2023.03.09.531845 | General encoding of canonical *k*-mers | Roland Wittler | <p style="text-align: justify;">To index or compare sequences efficiently, often <em>k</em>-mers, i.e., substrings of fixed length <em>k</em>, are used. For efficient indexing or storage, <em>k</em>-mers are encoded as integers, e.g., applying som... | Combinatorics, Computational complexity, Genomics and Transcriptomics | Paul Medvedev | Anonymous | 2023-03-13 17:01:37 | View | |

09 Nov 2023
## A mechanistic-statistical approach to infer dispersal and demography from invasion dynamics, applied to a plant pathogenMéline Saubin, Jérome Coville, Constance Xhaard, Pascal Frey, Samuel Soubeyrand, Fabien Halkett, Frédéric Fabrehttps://doi.org/10.1101/2023.03.21.533642
## A mechanistic-statistical approach for the field-based study of invasion dynamicsRecommended by
Hirohisa Kishino based on reviews by 2 anonymous reviewersTo study the annual invasion of a tree pathogen ( The observed temporal variation in the spatial pattern of infection rates allowed Saubin et al (2023) to estimate the key factors that determine the speed of the invasion dynamics. In particular, it is crucial to estimate the probability and extent of long-distance dispersal. The dynamics of the macroscale population density was formulated by the reaction-diffusion (R.D.) model and by the integro-difference (I.D.) model. Both consist of the diffusion/dispersal component and the reaction component. In the I.D. model, the kernel function represents the distribution of the dispersion. The likelihood function was obtained by coupling the mathematical model of the population dynamics and the statistical model of the observational process. Saubin et al (2023) considered a thin-tailed Gaussian kernel, a heavy-tailed exponential kernel, and a fat-tailed exponential power kernel. The numerical simulation reflecting the above survey confirmed the identifiability of the propagation kernel and the accuracy of the parameter estimation. In particular, the above survey had the high power to identify the model with frequent long-distance dispersal. The data from the survey selected the exponential power kernel with confidence. The mean dispersal distance was estimated to be 2.01 km. The exponential power was 0.24. This parameter value predicts that 5% of the dispersals will have a distance > 14.3 km and 1% will have a distance > 36.0 km. The mechanistic-statistical approach presented here may become a new standard for the field-based studies of invasion dynamics.
Saubin, M., Coville, J., Xhaard, C., Frey, P., Soubeyrand, S., Halkett, F., and Fabre, F. (2023). A mechanistic-statistical approach to infer dispersal and demography from invasion dynamics, applied to a plant pathogen. bioRxiv, ver. 5 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2023.03.21.533642 Xhaard, C., Barrès, B., Andrieux, A., Bousset, L., Halkett, F., and Frey, P. (2012). Disentangling the genetic origins of a plant pathogen during disease spread using an original molecular epidemiology approach. Molecular Ecology, 21(10):2383-2398. https://doi.org/10.1111/j.1365-294X.2012.05556.x | A mechanistic-statistical approach to infer dispersal and demography from invasion dynamics, applied to a plant pathogen | Méline Saubin, Jérome Coville, Constance Xhaard, Pascal Frey, Samuel Soubeyrand, Fabien Halkett, Frédéric Fabre | <p style="text-align: justify;">Dispersal, and in particular the frequency of long-distance dispersal (LDD) events, has strong implications for population dynamics with possibly the acceleration of the colonisation front, and for evolution with po... | Dynamical systems, Ecology, Epidemiology, Probability and statistics | Hirohisa Kishino | 2023-05-10 09:57:25 | View | ||

27 Sep 2024
## In silico identification of switching nodes in metabolic networksFrancis Mairethttps://doi.org/10.1101/2023.05.17.541195
## A computational method to identify key players in metabolic rewiringRecommended by
Claudine Chaouiya based on reviews by 2 anonymous reviewersSignificant progress has been made in developing computational methods to tackle the analysis of the numerous (genome-wide scale) metabolic networks that have been documented for a wide range of species. Understanding the behaviours of these complex reaction networks is crucial in various domains such as biotechnology and medicine. Metabolic rewiring is essential as it enables cells to adapt their metabolism to changing environmental conditions. Identifying the metabolites around which metabolic rewiring occurs is certainly useful in the case of metabolic engineering, which relies on metabolic rewiring to transform micro-organisms into cellular factories [1], as well as in other contexts. This paper by F. Mairet [2] introduces a method to disclose these metabolites, named switch nodes, relying on the analysis of the flux distributions for different input conditions. Basically, considering fluxes for different inputs, which can be computed using e.g. Parsimonious Flux Balance Analysis (pFBA), the proposed method consists in identifying metabolites involved in reactions whose different flux vectors are not collinear. The approach is supported by four case studies, considering core and genome-scale metabolic networks of Whilst identified switch nodes may be biased because computed flux vectors satisfying given objectives are not necessarily unique, the proposed method has still a relevant predictive potential, complementing the current array of computational methods to study metabolism.
[1] Tao Yu, Yasaman Dabirian, Quanli Liu, Verena Siewers, Jens Nielsen (2019) Strategies and challenges for metabolic rewiring. Current Opinion in Systems Biology, Vol 15, pp 30-38. https://doi.org/10.1016/j.coisb.2019.03.004. [2] Francis Mairet (2024) In silico identification of switching nodes in metabolic networks. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2023.05.17.541195 | In silico identification of switching nodes in metabolic networks | Francis Mairet | <p>Cells modulate their metabolism according to environmental conditions. A major challenge to better understand metabolic regulation is to identify, from the hundreds or thousands of molecules, the key metabolites where the re-orientation of flux... | Graph theory, Physiology, Systems biology | Claudine Chaouiya | Anonymous | 2023-05-26 17:24:26 | View | |

26 Feb 2024
## A workflow for processing global datasets: application to intercroppingRémi Mahmoud, Pierre Casadebaig, Nadine Hilgert, Noémie Gaudiohttps://hal.science/hal-04145269
## Collecting, assembling and sharing data in crop sciencesRecommended by
Eric Tannier based on reviews by Christine Dillmann and 2 anonymous reviewersIt 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.
[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 | A workflow for processing global datasets: application to intercropping | Rémi Mahmoud, Pierre Casadebaig, Nadine Hilgert, Noémie Gaudio | <p>Field experiments are a key source of data and knowledge in agricultural research. An emerging practice is to compile the measurements and results of these experiments (rather than the results of publications, as in meta-analysis) into global d... | Agricultural Science | Eric Tannier | 2023-06-29 15:38:28 | View | ||

28 Jun 2024
## Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome OrganizationThéotime Grohens, Sam Meyer, Guillaume Beslonhttps://doi.org/10.1101/2022.09.23.509185
## Understanding the impact of the transcription-supercoiling coupling on bacterial genome evolutionRecommended by
Nelle Varoquaux based on reviews by Ivan Junier and 1 anonymous reviewerDNA 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.
[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 | Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization | Théotime Grohens, Sam Meyer, Guillaume Beslon | <p>DNA supercoiling -- the level of twisting and writhing of the DNA molecule around itself -- plays a major role in the regulation of gene expression in bacteria by modulating promoter activity. The level of DNA supercoiling is a dynamic property... | Biophysics, Evolutionary Biology, Systems biology | Nelle Varoquaux | 2023-06-30 10:34:28 | View |

# MANAGING BOARD

Caroline Colijn

Caroline Colijn

**Christophe Dessimoz**

**Barbara Holland**

**Hirohisa Kishino**

**Anita Layton**

**Wolfram Liebermeister**

**Paul Medvedev**

**Christian Robert**

**Celine Scornavacca**

**Donate Weghorn**

**RECOMMENDERS**