No abstract

This study developed a new bioinformatics pipeline to acquire all the different copies of multi-copy gene families based on Oxford Nanopore Technologies sequencing of PCR products. We used this pipeline to acquire the sequences of highly similar copies of the cidA and cidB genes present in the genomes of Wolbachia pipientis (wPip) bacteria infecting the cells of Culex pipiens mosquitoes. The approach is based on read mapping, SNP calling and haplotyping, using our already wide existing reference database for the cid genes obtained by cloning and Sanger sequencing. We addressed problems commonly faced when using mapping approaches for multi-copy gene families with highly similar variants (or haplotypes). In addition, we confirmed that PCR amplification causes frequent chimeras which have to be carefully considered when working on families of recombinant genes. We tested the robustness of the pipeline through a combination of analyses of simulated reads and of gene sequence acquisitions through cloning and Sanger sequencing. For genes of which the haplotype cannot be reconstructed from short reads sequencing, this pipeline confers a high throughput acquisition, gives reliable results as well as insights of the relative copy numbers of the different variants.

Adaptive Games (AG) involve a controller agent that continuously feeds from player actions and game state to tweak a set of game parameters in order to maintain or achieve an objective function such as the flow measure defined by Csíkszentmihályi. This can be considered a Reinforcement Learning (RL) situation, so that classical Machine Learning (ML) approaches can be used. On the other hand, many games naturally exhibit an incremental gameplay where new actions and elements are introduced or removed progressively to enhance player's learning curve or to introduce variety within the game. This makes the RL situation unusual because the controller agent input/output signature can change over the course of learning. In this paper, we get interested in this unusual "protean" learning situation (PL). In particular, we assess how the learner can rely on its past shapes and experience to keep improving among signature changes without needing to restart the learning from scratch on each change. We first develop a rigorous formalization of the PL problem. Then, we address the first elementary signature change: "input addition", with Recurrent Neural Networks (RNNs) in an idealized PL situation. As a first result, we find that it is possible to benefit from prior learning in RNNs even if the past controller agent signature has less inputs. The use of PL in AG thus remains encouraged. Investigating output addition, input/output removal and translating these results to generic PL will be part of future works.