Combinatorial selection strategies are powerful tools that allow researchers to simulate selective pressures over time on randomized sequence libraries. This is important for lead discovery and optimization and for understanding selection dynamics. Given the evolutionary nature of these experiments, high-fitness sequences will enrich, whereas low-fitness sequences will deplete. These experiments can generate large magnitudes of data, thus driving a need for high-throughput sequence (HTS) analyses that can utilize sequence-specific evolutionary trajectories. Recently, the selections field has benefitted from several software for HTS analysis. However, these software have a high entrance barrier for many users because they are only accessible through the command line or require extensive programming knowledge. FASTAptameR 2.0 (an R-based reimplementation of the original FASTAptamer) was designed to minimize this barrier yet maintain the ability to answer complex sequence- and population-level questions. This open-source toolkit features a user-friendly web server, interactive graphics, expanded module set, and a faster implementation of the original clustering algorithm (up to 100x faster in some use cases). Further, FASTAptameR 2.0 accepts biologically diverse inputs, such as evolving populations of cells, tumors, viruses, or organisms, as well as artificial biomolecules such as aptamers, peptides, ribozymes, and more. The FASTAptameR 2.0 user interface is available as a web server (https://fastaptamer2.missouri.edu/) or Docker image (skylerkramer/fastaptamer2:publicupload04), and the code can be accessed from https://github.com/SkylerKramer/FASTAptameR-2.0.
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