Machine learning approaches for improved accuracy and speed in sequence annotation: supplement for software enhancement

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Summary The goal of this parent grant for this supplement request is to develop Machine Learning approaches to improve both accuracy and speed of highly-sensitive sequence database search and alignment. We have developed three software tools associated with this effort of correctly annotating genomes: (i) ULTRA, which labels repetitive sequence, (ii) PolyA which integrates such labels with other sequence annotations in a probabilistic framework, computing uncertainty and improving accuracy, and (iii) SODA, which aids in visualization of annotations and supporting evidence. Here, we describe a plan to refactor these software tools and their documentation to improve robustness and reliability, and to improve their availability through package management systems and incorporation into cloud-based analysis frameworks.