In recent years, video games have surged in popularity, attracting millions of players across platforms. Citizen science games (CSGs) leverage the processing power of gamers to solve computational and scientific problems. Borderlands Science (BLS) is a mini-game within the mass market game Borderlands 3 that turns multiple sequence alignment (MSA) problems into puzzles. Parallel research demonstrated that BLS players outperformed classical approaches solving small sequence alignment tasks. This study aims to analyze the strategical differences in player solutions in BLS as they gain experience. Through the many collected player solutions from players of different experience level, we gained insights into players’ strategies, differences between expert and non-expert players, and how strategies evolve. We developed a Markov chain trained on solutions from players of different experience levels to understand their actions and outcomes. Results indicate that expert players utilize more gaps and achieve more matches, gradually improving and converging toward unique strategies. Our findings reveal distinct and evolving player strategies. For future citizen science projects, it will be important to consider the identification of player strategies and their evolution over time to improve the game design and data processing.

MOTIVATION: Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. RESULTS: We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES' usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. AVAILABILITY AND IMPLEMENTATION: PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).

Over the past two decades, scientists have increasingly realized the importance of the three-dimensional (3D) genome organization in regulating cellular activity. Hi-C and related experiments yield 2D contact matrices that can be used to infer 3D models of chromosome structure. Visualizing and analyzing genomes in 3D space remains challenging. Here, we present ARGV, an augmented reality 3D Genome Viewer. ARGV contains more than 350 pre-computed and annotated genome structures inferred from Hi-C and imaging data. It offers interactive and collaborative visualization of genomes in 3D space, using standard mobile phones or tablets. A user study comparing ARGV to existing tools demonstrates its benefits.

With nearly three billion players, video games are more popular than ever. Casual puzzle games are among the most played categories. These games capitalize on the players’ analytical and problem-solving skills. Can we leverage these abilities to teach ourselves how to solve complex combinatorial problems? In this study, we harness the collective wisdom of millions of players to tackle the classical NP-hard problem of multiple sequence alignment, relevant to many areas of biology and medicine. We show that Borderlands Science players propose solutions to multiple sequence alignment tasks that perform as well or better than standard approaches, while exploring a much larger area of the Pareto-optimal solution space. We also show the strategies of the players, although highly heterogeneous, follow a collective logic that can be mimicked with Behavioral Cloning with minimal performance loss, allowing the players’ collective wisdom to be leveraged for alignment of any sequences.

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.