Efficient Non-Binary Gene Tree Resolution with Weighted Reconciliation Cost

Polytomies in gene trees are multifurcated nodes corresponding to unresolved parts of the tree, usually due to insufficient differentiation between sequences of homologous gene copies. Apart from gene sequences, other information such as that contained in the species tree can be used to resolve such intricate parts of a gene tree. The problem of resolving a multifurcated tree has been considered by many authors, the objective function often being the number of duplications and losses reflected by the reconciliation of the resolved gene tree with the species tree. Here, we present PolytomySolver, an algorithm accounting for a more general model allowing different costs for duplications and losses per species. The time complexity of this algorithm is linear for the unit cost and is quadratic for the general cost, which outperforms the best known solutions so far by a linear factor. We show on simulated trees that the gain in theoretical complexity has a real practical impact on running times

Méthodes et algorithmes pour l’amélioration de l’inférence de l’histoire évolutive des génomes

Les phylogénies de gènes offrent un cadre idéal pour l’étude comparative des génomes. Non seulement elles incorporent l’évolution des espèces par spéciation, mais permettent aussi de capturer l’expansion et la contraction des familles de gènes par gains et pertes de gènes. La détermination de l’ordre et de la nature de ces événements équivaut à inférer l’histoire évolutive des familles de gènes, et constitue un prérequis à plusieurs analyses en génomique comparative. En effet, elle est requise pour déterminer efficacement les relations d’orthologies entre gènes, importantes pour la prédiction des structures et fonctions de protéines et les analyses phylogénétiques, pour ne citer que ces applications. Les méthodes d’inférence d’histoires évolutives de familles de gènes supposent que les phylogénies considérées sont dénuées d’erreurs. Ces phylogénies de gènes, souvent recons- truites à partir des séquences d’acides aminés ou de nucléotides, ne représentent cependant qu’une estimation du vrai arbre de gènes et sont sujettes à des erreurs provenant de sources variées, mais bien documentées. Pour garantir l’exactitude des histoires inférées, il faut donc s’assurer de l’absence d’erreurs au sein des arbres de gènes. Dans cette thèse, nous étudions cette problématique sous deux aspects. Le premier volet de cette thèse concerne l’identification des déviations du code génétique, l’une des causes d’erreurs d’annotations se propageant ensuite dans les phylogénies. Nous développons à cet effet, une méthodologie pour l’inférence de déviations du code génétique standard par l’analyse des séquences codantes et des ARNt. Cette méthodologie est cen- trée autour d’un algorithme de prédiction de réaffectations de codons, appelé CoreTracker. Nous montrons tout d’abord l’efficacité de notre méthode, puis l’utilisons pour démontrer l’évolution du code génétique dans les génomes mitochondriaux des algues vertes. Le second volet de la thèse concerne le développement de méthodes efficaces pour la correction et la construction d’arbres phylogénétiques de gènes. Nous présentons deux méthodes exploitant l’information sur l’évolution des espèces. La première, ProfileNJ , est déterministe et très rapide. Elle corrige les arbres de gènes en ciblant exclusivement les sous-arbres présentant un support statistique faible. Son application sur les familles de gènes d’Ensembl Compara montre une amélioration nette de la qualité des arbres, par comparaison à ceux proposés par la base de données. La seconde, GATC, utilise un algorithme génétique et traite le problème comme celui de l’optimisation multi-objectif de la topologie des arbres de gènes, étant données des contraintes relatives à l’évolution des familles de gènes par mutation de séquences et par gain/perte de gènes. Nous montrons qu’une telle approche est non seulement efficace, mais appropriée pour la construction d’ensemble d’arbres de référence.Gene trees offer a proper framework for comparative genomics. Not only do they provide information about species evolution through speciation events, but they also capture gene family expansion and contraction by gene gains and losses. They are thus used to infer the evolutionary history of gene families and accurately predict the orthologous relationship between genes, on which several biological analyses rely. Methods for inferring gene family evolution explicitly assume that gene trees are known without errors. However, standard phylogenetic methods for tree construction based on se- quence data are well documented as error-prone. Gene trees constructed using these methods will usually introduce biases during the inference of gene family histories. In this thesis, we present new methods aiming to improve the quality of phylogenetic gene trees and thereby the accuracy of underlying evolutionary histories of their corresponding gene families. We start by providing a framework to study genetic code deviations, one possible reason of annotation errors that could then spread to the phylogeny reconstruction. Our framework is based on analysing coding sequences and tRNAs to predict codon reassignments. We first show its efficiency, then apply it to green plant mitochondrial genomes. The second part of this thesis focuses on the development of efficient species tree aware methods for gene tree construction. We present ProfileNJ , a fast and deterministic correction method that targets weakly supported branches of a gene tree. When applied to the gene families of the Ensembl Compara database, ProfileNJ produces an arguably better set of gene trees compared to the ones available in Ensembl Compara. We later use a different strategy, based on a genetic algorithm, allowing both construction and correction of gene trees. This second method called GATC, treats the problem as a multi-objective optimisation problem in which we are looking for the set of gene trees optimal for both sequence data and information of gene family evolution through gene gain and loss. We show that this approach yields accurate trees and is suitable for the construction of reference datasets to benchmark other methods

Gotta be SAFE: A New Framework for Molecular Design

Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unordered sequence of interconnected fragment blocks while maintaining compatibility with existing SMILES parsers. It streamlines complex generative tasks, including scaffold decoration, fragment linking, polymer generation, and scaffold hopping, while facilitating autoregressive generation for fragment-constrained design, thereby eliminating the need for intricate decoding or graph-based models. We demonstrate the effectiveness of SAFE by training an 87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE representations. Through targeted experimentation, we show that our SAFE-GPT model exhibits versatile and robust optimization performance. SAFE opens up new avenues for the rapid exploration of chemical space under various constraints, promising breakthroughs in AI-driven molecular design.Comment: Code, data and models available at: https://github.com/datamol-io/safe

GO-GA:Class experiences with offline inquiry learning spaces in go-lab

This paper reports on a study carried out in the framework of the Go-lab Goes Africa project, in which teachers implemented online and offline Inquiry Learning Spaces (ILS) in their classes using the Go-lab platform. After a brief description of the Inquiry Based Learning (IBL) methodology, of lab work and in particular virtual labs for STEM education, and of the process of preparing teachers for using IBL in class, we highlight the methodology used in this study, and finally report the results. Our results show that (i) the introduction and class enactment of a digital inquiry-based learning platform in Africa is possible (although challenging with respect to pedagogy and infrastructure), and (ii) does lead to student learning, (iii) for this to take place teacher training with respect to the Inquiry Based Learning methodology, and the development of an ILS are necessary, (iv) the digital infrastructure at school is sufficient for offline use, however, poses problems when online ILSs are used, and (v) a local partner needs to provide assistance, mainly to set up the infrastructure (installation of the ILS and the viewer) at the beginning of the lesson, and to assist students with computer related queries

Reconstructing the History of Syntenies Through Super-Reconciliation

Classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is clearly not suited for genes grouped in syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the Super-Reconciliation model, that extends the traditional Duplication-Loss model to the reconciliation of a set of trees, accounting for segmental duplications and losses. From a complexity point of view, we show that the associated decision problem is NP-hard. We then give an exact exponential-time algorithm for this problem, assess its time efficiency on simulated datasets, and give a proof of concept on the opioid receptor genes

Promoting and Implementing Digital STEM Education at Secondary Schools in Africa

This paper discusses an ongoing initiative aimed at promoting and implementing digital STEM education at secondary schools in Africa. This initiative, coined Go-Lab Goes Africa (GO-GA), is an innovation action supported by the European Commission through its H2020 Framework Programme for Research and Technological Development in Information and Communication Technologies (ICT). The general vision and the implementation strategy are outlined in detail, as well as the challenges faced and results achieved during its first year

GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution

Abstract Background Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately the best fit to sequence information can be lost during this process. Results We describe GATC, a new algorithm for reconstructing a binary gene tree with branch length. GATC returns optimal solutions according to a measure combining both tree likelihood (according to sequence evolution) and a reconciliation score under the Duplication-Transfer-Loss (DTL) model. It can either be used to construct a gene tree from scratch or to correct trees infered by existing reconstruction method, making it highly flexible to various input data types. The method is based on a genetic algorithm acting on a population of trees at each step. It substantially increases the efficiency of the phylogeny space exploration, reducing the risk of falling into local minima, at a reasonable computational time. We have applied GATC to a dataset of simulated cyanobacterial phylogenies, as well as to an empirical dataset of three reference gene families, and showed that it is able to improve gene tree reconstructions compared with current state-of-the-art algorithms. Conclusion The proposed algorithm is able to accurately reconstruct gene trees and is highly suitable for the construction of reference trees. Our results also highlight the efficiency of multi-objective optimization algorithms for the gene tree reconstruction problem. GATC is available on Github at: https://github.com/UdeM-LBIT/GATC