pycofitness-Evaluating the fitness landscape of RNA and protein sequences.

Motivation: The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue.

Results: We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences.

CoCoNet-boosting RNA contact prediction by convolutional neural networks.

Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins.

Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set.

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction.

pydca v1.0: a comprehensive software for direct coupling analysis of RNA and protein sequences.

Motivation: The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction.

Biomolecular coevolution and its applications: Going from structure prediction toward signaling, epistasis, and function.

Evolution leads to considerable changes in the sequence of biomolecules, while their overall structure and function remain quite conserved. The wealth of genomic sequences, the 'Biological Big Data', modern sequencing techniques provide allows us to investigate biomolecular evolution with unprecedented detail. Sophisticated statistical models can infer residue pair mutations resulting from spatial proximity.

Effect of replication on epigenetic memory and consequences on gene transcription.

Gene activity in eukaryotes is in part regulated at the level of chromatin through the assembly of local chromatin states that are more or less permissive to transcription. How do these chromatin states achieve their functions and whether or not they contribute to the epigenetic inheritance of the transcriptional program remain to be elucidated. In cycling cells, stability is indeed strongly challenged by the periodic occurrence of replication and cell division.