Abstract: Disclosed is a computer-implemented method for sequence design, including: obtaining a candidate target sequence set that translates into a source sequence, based on the source sequence; and searching in the candidate target sequence set for a desired target sequence having a sequence structure that minimizes an objective function. The objective function comprises a minimum free energy (MFE) of a candidate target sequence and an additive regularization term.

Abstract: A messenger RNA (mRNA) vaccine has emerged as a promising direction to combat the COVID-19 pandemic. This requires an mRNA sequence that is stable and highly productive in protein expression, features to benefit from greater mRNA secondary structure folding stability and optimal codon usage. Sequence design remains challenging due to the exponentially many synonymous mRNA sequences encoding the same protein. The present disclosure presents embodiments of a linear-time approximation (LinearDesign) reducing the design to an intersection between a Stochastic Context Free Grammar (SCFG) and a Deterministic Finite Automaton (DFA). Embodiments of the LinearDesign may implement an mRNA sequence design using much reduced time with very limited loss. Various methodologies, e.g., finding alternative sequences based on k-best parsing or directly incorporating codon optimality, are presented for incorporating the codon optimality into the design.

Abstract: COVID-19 has become a global pandemic after its inception in late 2019. SARS-CoV-2 genomes are sequenced and shared on public repositories at a fast pace. To keep up with these updates, datasets need to be refreshed and re-cleaned frequently. It may be difficult to analyze SARS-CoV-2 genomes for scientists with limited bioinformatics or programming knowledge. In the present disclosure, system and method embodiments for genome analysis and visualization are developed to address these challenges. A webserver may be used to enable simple and rapid analysis of genomes. Given a new sequence, the system may automatically predict gene boundaries and identify genetic variants, which are presented in an interactive genome visualizer and are downloadable for analysis. A command-line interface may be available for high throughput processing.