### Latest recommendations

Id | Title * | Authors * | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers▲ | Submission date | |
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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 | |

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 | |

07 Dec 2021
## The emergence of a birth-dependent mutation rate in asexuals: causes and consequencesFlorian Patout, Raphaël Forien, Matthieu Alfaro, Julien Papaïx, Lionel Roqueshttps://doi.org/10.1101/2021.06.11.448026
## A new perspective in modeling mutation rate for phenotypically structured populationsRecommended by
Yuan Lou based on reviews by Hirohisa Kishino and 1 anonymous reviewerIn standard mutation-selection models for describing the dynamics of phenotypically structured populations, it is often assumed that the mutation rate is constant across the phenotypes. In particular, this assumption leads to a constant diffusion coefficient for diffusion approximation models (Perthame, 2007 and references therein). Patout et al (2021) study the dependence of the mutation rate on the birth rate, by introducing some diffusion approximations at the population level, derived from the large population limit of a stochastic, individual-based model. The reaction-diffusion model in this article is of the “cross-diffusion” type: The form of “cross-diffusion” also appeared in ecological literature as a type of biased movement behaviors for organisms (Shigesada et al., 1979). The key underlying assumption for “cross-diffusion” is that the transition probability at the individual level depends solely upon the condition at the departure point. Patout et al (2021) envision that a higher birth rate yields more mutations per unit of time. One of their motivations is that during cancer development, the mutation rates of cancer cells at the population level could be correlated with reproduction success. The reaction-diffusion approximation model derived in this article illustrates several interesting phenomena: For the time evolution situation, their model predicts different solution trajectories under various assumptions on the fitness function, e.g. the trajectory could initially move towards the birth optimum but eventually end up at the survival optimum. Their model also predicts that the mean fitness could be flat for some period of time, which might provide another alternative to explain observed data. At the steady-state level, their model suggests that the populations are more concentrated around the survival optimum, which agrees with the evolution of the time-dependent solution trajectories. Perhaps one of the most interesting contributions of the study of Patout et al (2021) is to give us a new perspective to model the mutation rate in phenotypically structured populations and subsequently, and to help us better understand the connection between mutation and selection. More broadly, this article offers some new insights into the evolutionary dynamics of phenotypically structured populations, along with potential implications in empirical studies.
Perthame B (2007) Transport Equations in Biology Frontiers in Mathematics. Birkhäuser, Basel. https://doi.org/10.1007/978-3-7643-7842-4_2 Patout F, Forien R, Alfaro M, Papaïx J, Roques L (2021) The emergence of a birth-dependent mutation rate in asexuals: causes and consequences. bioRxiv, 2021.06.11.448026, ver. 3 peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology. https://doi.org/10.1101/2021.06.11.448026 Shigesada N, Kawasaki K, Teramoto E (1979) Spatial segregation of interacting species. Journal of Theoretical Biology, 79, 83–99. https://doi.org/10.1016/0022-5193(79)90258-3 | The emergence of a birth-dependent mutation rate in asexuals: causes and consequences | Florian Patout, Raphaël Forien, Matthieu Alfaro, Julien Papaïx, Lionel Roques | <p style="text-align: justify;">In unicellular organisms such as bacteria and in most viruses, mutations mainly occur during reproduction. Thus, genotypes with a high birth rate should have a higher mutation rate. However, standard models of asexu... | Dynamical systems, Evolutionary Biology, Probability and statistics, Stochastic dynamics | Yuan Lou | Anonymous, Hirohisa Kishino | 2021-06-12 13:59:45 | View | |

21 Oct 2024
## Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteinsIsmael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collinshttps://doi.org/10.1101/2023.12.15.571577
## Systematic investigation of software tools and design of a tailored pipeline for paleoproteomics researchRecommended by
Raquel Assis based on reviews by Shevan Wilkin and 1 anonymous reviewerPaleoproteomics is a rapidly growing field with numerous challenges, many of which are due to the highly fragmented, modified, and degraded nature of ancient proteins. Though there are established standards for analysis, it is unclear how different software tools affect the identification and quantification of peptides, proteins, and post-translational modifications. To address this knowledge gap, Rodriguez Palomo et al. design a controlled system by experimentally degrading and purifying bovine beta-lactoglobulin, and then systematically compare the performance of many commonly used tools in its analysis. They present comprehensive investigations of false discovery rates, open and narrow searches, de novo sequencing coverage bias and accuracy, and peptide chemical properties and bias. In each investigation, they explore wide ranges of appropriate tools and parameters, providing guidelines and recommendations for best practices. Based on their findings, Rodriguez Palomo et al. develop a proposed pipeline that is tailored for the analysis of ancient proteins. This pipeline is an important contribution to paleoproteomics and is likely to be of great value to the research community, as it is designed to enhance power, accuracy, and consistency in studies of ancient proteins.
Ismael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collins (2023) Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins. bioRxiv, ver.3 peer-reviewed and recommended by PCI Math Comp Biol https://doi.org/10.1101/2023.12.15.571577 | Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins | Ismael Rodriguez-Palomo, Bharath Nair, Yun Chiang, Joannes Dekker, Benjamin Dartigues, Meaghan Mackie, Miranda Evans, Ruairidh Macleod, Jesper V. Olsen, Matthew J. Collins | <p style="text-align: justify;">Palaeoproteomics is a rapidly evolving discipline, and practitioners are constantly developing novel strategies for the analyses and interpretations of complex, degraded protein mixtures. The community has also esta... | Genomics and Transcriptomics, Probability and statistics | Raquel Assis | Anonymous, Shevan Wilkin | 2024-03-12 15:17:08 | 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 | |

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 | |

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 |

# 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**