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Biology subjects

Elder, S.

Publications and source records attributed to Elder, S..

2 recordsLinked to original sources

k-nonical space: sketching with reverse complements

Sequences equivalent to their reverse complements (i.e., double-stranded DNA) have no analogue in text analysis and non-biological string algorithms. Despite this striking difference, algorithms designed for computational biology (e.g., sketching algorithms) are designed and tested in the same way as classical string algorithms. Then, as a post-processing step, these algorithms are adapted to work with genomic sequences by folding a k-mer and its reverse complement into a single sequence: the canonical representation (k-nonical space). The effect of using the canonical representation with sketching methods is understudied and not understood. As a first step, we use context-free sketching methods to illustrate the potentially detrimental effects of using canonical k-mers with string algorithms not designed to accommodate for them. In particular, we show that large stretches of the genome ("sketching deserts") are undersampled or entirely skipped by context-free sketching methods, effectively making these genomic regions invisible to subsequent algorithms using these sketches. We provide empirical data showing these effects and develop a theoretical framework explaining the appearance of sketching deserts. Finally, we propose two schemes to accommodate for these effects: (1) a new procedure that adapts existing sketching methods to k-nonical space and (2) an optimization procedure to directly design new sketching methods for k-nonical space. The code used in this analysis is freely available at https://github.com/Kingsford-Group/mdsscope.

bioinformatics↗

Cross-platform Bayesian optimization system for autonomous biological assay development

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the NCATS ASPIRE Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof-of-concept for biological assay development. Compared to a brute force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing only 21 assay conditions on average, with up to 20 conditions being tested simultaneously. The algorithm could achieve a seven-fold reduction in costs for lab supplies and high-throughput experimentation run-time, all while being controlled from a remote site through a secure connection. Based on this proof-of-concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

biochemistry↗