The Naïve Bayes Classifier ++ for Metagenomic Taxonomic Classification-Query Evaluation.

Motivation: This study examines the query performance of the NBC ++ (Incremental Naive Bayes Classifier) program for variations in canonicality, k-mer size, databases, and input sample data size. We demonstrate that both NBC ++ and Kraken2 are influenced by database depth, with macro measures improving as depth increases. However, fully capturing the diversity of life, especially viruses, remains a challenge.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.

Purpose Of Review: This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

Recent Findings: In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD).

Fragment databases from screened ligands for drug discovery (FDSL-DD).

Fragment-based drug design (FBDD) is one major drug discovery method employed in computer-aided drug discovery. Due to its inherent limitations, this process experiences long processing times and limited success rates. Here we present a new Fragment Databases from Screened Ligands Drug Design method (FDSL-DD) that intelligently incorporates information about fragment characteristics into a fragment-based design approach to the drug development process.

Physiological and evolutionary contexts of a new symbiotic species from the nitrogen-recycling gut community of turtle ants.

While genome sequencing has expanded our knowledge of symbiosis, role assignment within multi-species microbiomes remains challenging due to genomic redundancy and the uncertainties of in vivo impacts. We address such questions, here, for a specialized nitrogen (N) recycling microbiome of turtle ants, describing a new genus and species of gut symbiont-Ischyrobacter davidsoniae (Betaproteobacteria: Burkholderiales: Alcaligenaceae)-and its in vivo physiological context. A re-analysis of amplicon sequencing data, with precisely assigned Ischyrobacter reads, revealed a seemingly ubiquitous distribution across the turtle ant genus Cephalotes, suggesting ≥50 million years since domestication.

Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing.

Background: Early-onset renal cell carcinoma (eoRCC) is typically associated with pathogenic germline variants (PGVs) in RCC familial syndrome genes. However, most eoRCC patients lack PGVs in familial RCC genes and their genetic risk remains undefined.

Methods: Here, we analyzed biospecimens from 22 eoRCC patients that were seen at our institution for genetic counseling and tested negative for PGVs in RCC familial syndrome genes.

Complet+: a computationally scalable method to improve completeness of large-scale protein sequence clustering.

A major challenge for clustering algorithms is to balance the trade-off between homogeneity, , the degree to which an individual cluster includes only related sequences, and completeness, the degree to which related sequences are broken up into multiple clusters. Most algorithms are conservative in grouping sequences with other sequences. Remote homologs may fail to be clustered together and instead form unnecessarily distinct clusters.

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity.

Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation.

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports.

Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences.

Next-generation sequencing has been essential to the global response to the COVID-19 pandemic. As of January 2022, nearly 7 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences are available to researchers in public databases. Sequence databases are an abundant resource from which to extract biologically relevant and clinically actionable information.

MetaMutationalSigs: comparison of mutational signature refitting results made easy.

Motivation: The analysis of mutational signatures is becoming increasingly common in cancer genetics, with emerging implications in cancer evolution, classification, treatment decision and prognosis. Recently, several packages have been developed for mutational signature analysis, with each using different methodology and yielding significantly different results. Because of the non-trivial differences in tools' refitting results, researchers may desire to survey and compare the available tools, in order to objectively evaluate the results for their specific research question, such as which mutational signatures are prevalent in different cancer types.

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences.

Examining Cultural Structures and Functions in Biology.

Scientific culture and structure organize biological sciences in many ways. We make choices concerning the systems and questions we study. Our research then amplifies these choices into factors that influence the directions of future research by shaping our hypotheses, data analyses, interpretation, publication venues, and dissemination via other methods.

Teaching Microbiome Analysis: From Design to Computation Through Inquiry.

In this article, we present our three-class course sequence to educate students about microbiome analysis and metagenomics through experiential learning by taking them from inquiry to analysis of the microbiome: Molecular Ecology Lab, Bioinformatics, and Computational Microbiome Analysis. Students developed hypotheses, designed lab experiments, sequenced the DNA from microbiomes, learned basic python/R scripting, became proficient in at least one microbiome analysis software, and were able to analyze data generated from the microbiome experiments. While over 150 students (graduate and undergraduate) were impacted by the development of the series of courses, our assessment was only on undergraduate learning, where 45 students enrolled in at least one of the three courses and 4 students took all three.

Healthcare Shift Workers' Temporal Habits for Eating, Sleeping, and Light Exposure: A Multi-Instrument Pilot Study.

Background: Circadian misalignment can impair healthcare shift workers' physical and mental health, resulting in sleep deprivation, obesity, and chronic disease. This multidisciplinary research team assessed eating patterns and sleep/physical activity of healthcare workers on three different shifts (day, night, and rotating-shift). To date, no study of real-world shift workers' daily eating and sleep has utilized a largely-objective measurement.

Amino Acid -mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights.

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide -mers have been proposed for presenting DNA sequences to the model.

Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization.

We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies.

Emerging Priorities for Microbiome Research.

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology.

Exploring thematic structure and predicted functionality of 16S rRNA amplicon data.

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state).

Correction to: Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Following publication of the original article [1], the authors would like to highlight the following two corrections.

16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses.

Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding ("embedding") each sequence into a dense, low-dimensional, numeric vector space.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood.

Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.

Background: One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited.

Results: In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers.