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Motivation 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). Results 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: (i) a new procedure that adapts existing sketching methods to k -nonical space and (ii) an optimization procedure to directly design new sketching methods for k -nonical space. Availability and implementation The code used in this analysis is available under a permissive license at https://github.com/Kingsford-Group/mdsscope. [ABSTRACT FROM AUTHOR]

The Turnpike problem aims to reconstruct a set of one-dimensional points from their unordered pairwise distances. Turnpike arises in biological applications such as molecular structure determination, genomic sequencing, tandem mass spectrometry, and molecular error-correcting codes. Under noisy observation of the distances, the Turnpike problem is NP-hard and can take exponential time and space to solve when using traditional algorithms. To address this, we reframe the noisy Turnpike problem through the lens of optimization, seeking to simultaneously find the unknown point set and a permutation that maximizes similarity to the input distances. Our core contribution is a suite of algorithms that robustly solve this new objective. This includes a bilevel optimization framework that can efficiently solve Turnpike instances with up to 100,000 points. We show that this framework can be extended to scenarios with domain-specific constraints that include duplicated, missing, and partially labeled distances. Using these, we also extend our algorithms to work for points distributed on a circle (the Beltway problem). For small-scale applications that require global optimality, we formulate an integer linear program (ILP) that (i) accepts an objective from a generic family of convex functions and (ii) uses an extended formulation to reduce the number of binary variables. On synthetic and real partial digest data, our bilevel algorithms achieved state-of-the-art scalability across challenging scenarios with performance that matches or exceeds competing baselines. On small-scale instances, our ILP efficiently recovered ground-truth assignments and produced reconstructions that match or exceed our alternating algorithms. Our implementations are available at https://github.com/Kingsford-Group/turnpikesolvermm.

Background: Microbial contamination of aerosol facemasks could be a source of nosocomial infections during nebulization therapy in hospital, prompting efforts to identify these contaminants. Identification of micro-organisms in medical devices has traditionally relied on culture-dependent methods, which are incapable of detecting the majority of these microbial contaminants. This challenge could be overcome with culture-independent sequencing-based techniques that are suited for the profiling of complex microbiomes., Aim: To characterize the microbial contaminants in aerosol facemasks used for nebulization therapy, and identify factors influencing the composition of these microbial contaminants with the acquisition and analysis of comprehensive microbiome-scale profiles using culture-independent high-throughput sequencing., Methods: Used aerosol facemasks collected from hospitalized patients were analysed with culture-independent 16S rRNA gene-based amplicon sequencing to acquire microbiome-scale comprehensive profiles of the microbial contaminants. Microbiome-based analysis was performed to identify potential sources of microbial contamination in facemasks., Findings: Culture-independent high-throughput sequencing was demonstrated for the capacity to acquire microbiome-scale profiles of microbial contaminants on aerosol facemasks. Microbial source identification enabled by the microbiome-scale profiles linked microbial contamination on aerosol facemasks to the human skin and oral microbiota. Antibiotic treatment with levofloxacin was found to reduce contamination of the facemasks by oral microbiota., Conclusion: Sequencing-based microbiome-scale analysis is capable of providing comprehensive characterization of microbial contamination in aerosol facemasks. Insight gained from microbiome-scale analysis facilitates the development of effective strategies for the prevention and mitigation of the risk of nosocomial infections arising from exposure to microbial contamination of aerosol facemasks, such as targeted elimination of potential sources of contamination., (Copyright © 2023 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.)

Nebulizers are essential for the delivery of aerosolized medication for respiratory patients in hospital. Microbial contamination of nebulizers increases the risk of healthcare-associated infections, presenting the critical need to identify sources of contamination in order to develop effective infection prevention and control practices in hospitals. Using an innovative microbiome-based cultivation-independent microbial source identification technique, the hospital indoor environment was identified as a significant source contributing to microbial contaminants in nebulizers, providing important information to develop strategies for targeted decontamination and enhance the effectiveness of infection prevention and control practices., (Copyright © 2022 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.)