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The microbes present in the human gastrointestinal tract are regularly linked to humanhealth and disease outcomes. Thanks to technological and methodological advances in re-cent years, metagenomic sequencing data, and computational methods designed to analyzemetagenomic data, have contributed to improved understanding of the link between thehuman gut microbiome and disease. However, while numerous methods have been recentlydeveloped to extract quantitative and qualitative results from host-associated microbiomedata, improved computational tools are still needed to track microbiome dynamics withshort-read sequencing data. Previously we have proposed KOMB as ade novotool foridentifying copy number variations in metagenomes for characterizing microbial genomedynamics in response to perturbations. In this work, we present KombOver (KO), whichincludes four key contributions with respect to our previous work: (i) it scales to largemicrobiome study cohorts, (ii) it includes bothk-core andK-truss based analysis, (iii)we provide the foundation of a theoretical understanding of the relation between variousgraph-based metagenome representations, and (iv) we provide an improved user experiencewith easier-to-run code and more descriptive outputs/results. To highlight the aforemen-tioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring lessthan 10 minutes and 10 GB RAM per sample to process these data. Furthermore, wehighlight how graph-based approaches such ask-core andK-truss can be informative forpinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fa-tigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at:https://github.com/treangenlab/komb 

Abstract As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions). 

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.

 

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.