SUMMARY: scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). AVAILABILITY AND IMPLEMENTATION: scFates is released as open-source software under the BSD 3-Clause 'New' License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: During the past decade, new classes of non-coding RNAs (ncRNAs) and their unexpected functions were discovered. Stable secondary structure is the key feature of many non-coding RNAs. Taking into account huge amounts of genomic data, development of computational methods to survey genomes for structured RNAs remains an actual problem, especially when homologous sequences are not available for comparative analysis. Existing programs scan genomes with a fixed window by efficiently constructing a matrix of RNA minimum free energies. A wide range of lengths of structured RNAs necessitates the use of many different window lengths that substantially increases the output size and computational efforts. RESULTS: In this article, we present an algorithm RNASurface to efficiently scan genomes by constructing a matrix of significance of RNA secondary structures and to identify all locally optimal structured RNA segments up to a predefined size. RNASurface significantly improves precision of identification of known ncRNA in Bacillus subtilis. AVAILABILITY AND IMPLEMENTATION: RNASurface C source code is available from http://bioinf.fbb.msu.ru/RNASurface/downloads.html.