Background Bootstrap resampling is used throughout biology to perform critical statistical tasks, including estimating confidence intervals as part of statistical inference and learning. A key assumption of bootstrap resampling is that data are independent and identically distributed (i.i.d.), but this assumption is at odds with the inherently sequential nature of biomolecular sequences. To relax this simplifying assumption, RAWR (“RAndom Walk Resampling”) was introduced as a technique for sequence-aware statistical resampling. RAWR has been applied to several different biomolecular sequence analysis tasks to date, including phylo- genetic tree support estimation. In each of these tasks, RAWR produces comparable or superior results to the bootstrap method and state-of-the-art resampling methods. Methods and Results A comprehensive software suite for RAWR resampling of unaligned biomolecular sequence data has been developed. Version 1.0 of the distribution focuses on two essential applications in computational biology and bioinformatics: (1) phylogenetic support estimation and (2) estimating confidence intervals on multiple sequence alignments (MSAs). The software implementation includes a local Galaxy client, a desktop PC client with a graphical user interface (GUI), and a standalone web server application. Access to software library functionality is provided via a developer-friendly application programming interface (API). User and developer documentation and tutorials are also provided. Availability and Implementation The software suite, documentation, and tutorials are publicly available under an open-source copyleft license at https://github.com/kjl-msu/RAWR-web-software. The software and API are implemented in Python for use on macOS, Linux, and Windows operating systems.
Zheng et al. (Sat,) studied this question.