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1. Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

   https://doi.org/10.1186/s12859-020-03744-7  

   Zhao, Zhengqiao; Cristian, Alexandru; Rosen, Gail (December 2020, BMC Bioinformatics)

   null (Ed.)

   Abstract Background It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. Results We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4 t h of the non-incremental time with no accuracy loss. Conclusions It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources. 

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2. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses

   https://doi.org/10.1186/s40168-020-00990-y  

   Guo, Jiarong; Bolduc, Ben; Zayed, Ahmed A.; Varsani, Arvind; Dominguez-Huerta, Guillermo; Delmont, Tom O.; Pratama, Akbar Adjie; Gazitúa, M. Consuelo; Vik, Dean; Sullivan, Matthew B.; et al (December 2021, Microbiome)

   null (Ed.)

   Abstract Background Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools. Results Here, we introduce VirSorter2, a DNA and RNA virus identification tool that leverages genome-informed database advances across a collection of customized automatic classifiers to improve the accuracy and range of virus sequence detection. When benchmarked against genomes from both isolated and uncultivated viruses, VirSorter2 uniquely performed consistently with high accuracy (F1-score > 0.8) across viral diversity, while all other tools under-detected viruses outside of the group most represented in reference databases (i.e., those in the order Caudovirales ). Among the tools evaluated, VirSorter2 was also uniquely able to minimize errors associated with atypical cellular sequences including eukaryotic genomes and plasmids. Finally, as the virosphere exploration unravels novel viral sequences, VirSorter2’s modular design makes it inherently able to expand to new types of viruses via the design of new classifiers to maintain maximal sensitivity and specificity. Conclusion With multi-classifier and modular design, VirSorter2 demonstrates higher overall accuracy across major viral groups and will advance our knowledge of virus evolution, diversity, and virus-microbe interaction in various ecosystems. Source code of VirSorter2 is freely available ( https://bitbucket.org/MAVERICLab/virsorter2 ), and VirSorter2 is also available both on bioconda and as an iVirus app on CyVerse ( https://de.cyverse.org/de ). 

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3. Metalign: efficient alignment-based metagenomic profiling via containment min hash

   https://doi.org/10.1186/s13059-020-02159-0  

   LaPierre, Nathan; Alser, Mohammed; Eskin, Eleazar; Koslicki, David; Mangul, Serghei (December 2020, Genome Biology)

   null (Ed.)

   Abstract Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets. 

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4. Using high-abundance proteins as guides for fast and effective peptide/protein identification from human gut metaproteomic data

   https://doi.org/10.1186/s40168-021-01035-8  

   Stamboulian, Moses; Li, Sujun; Ye, Yuzhen (December 2021, Microbiome)

   null (Ed.)

   Abstract Background A few recent large efforts significantly expanded the collection of human-associated bacterial genomes, which now contains thousands of entities including reference complete/draft genomes and metagenome assembled genomes (MAGs). These genomes provide useful resource for studying the functionality of the human-associated microbiome and their relationship with human health and diseases. One application of these genomes is to provide a universal reference for database search in metaproteomic studies, when matched metagenomic/metatranscriptomic data are unavailable. However, a greater collection of reference genomes may not necessarily result in better peptide/protein identification because the increase of search space often leads to fewer spectrum-peptide matches, not to mention the drastic increase of computation time. Methods Here, we present a new approach that uses two steps to optimize the use of the reference genomes and MAGs as the universal reference for human gut metaproteomic MS/MS data analysis. The first step is to use only the high-abundance proteins (HAPs) (i.e., ribosomal proteins and elongation factors) for metaproteomic MS/MS database search and, based on the identification results, to derive the taxonomic composition of the underlying microbial community. The second step is to expand the search database by including all proteins from identified abundant species. We call our approach HAPiID (HAPs guided metaproteomics IDentification). Results We tested our approach using human gut metaproteomic datasets from a previous study and compared it to the state-of-the-art reference database search method MetaPro-IQ for metaproteomic identification in studying human gut microbiota. Our results show that our two-steps method not only performed significantly faster but also was able to identify more peptides. We further demonstrated the application of HAPiID to revealing protein profiles of individual human-associated bacterial species, one or a few species at a time, using metaproteomic data. Conclusions The HAP guided profiling approach presents a novel effective way for constructing target database for metaproteomic data analysis. The HAPiID pipeline built upon this approach provides a universal tool for analyzing human gut-associated metaproteomic data. 

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5. Microbiome research: Application of “omics” technology

   https://doi.org/10.26717/BJSTR.2022.46.007300  

   Flowers, L. (September 2022, Biomedical journal of scientific technical research)

   Microbiome research is a thriving field focused on characterizing the composition and functionality of microbial populations or microbiomes from a wide array of ecological niches. Microbiomes occupy living organisms, soil, the atmosphere, and bodies of water and exist in moderate and extreme climates. Understanding the intractable microbial universes in various environments is challenging and potentially rewarding to humankind. Historically, elucidating pathogenic microbes and their impact on host species has dominated microbiome-based studies. Moreover, a tiny percentage of microbes can be cultured using classical culturing methods. With advancements in high throughput experimentation and computational tools derived from microbial ecology, there is a driving force to gain insight into the entire microbial consortium from various environmental and biological locations. Metagenomics, the study of all the microbial genomes in a sample using sequencing techniques (e.g., 16s rRNA amplicon sequencing and shotgun sequencing), has so far dominated the types of investigations conducted in the field of microbiome research. More recently, however, researchers are becoming increasingly interested in better understanding the complex microbe-associated molecular network and specific protein and metabolite functions associated with microbial genetic potential. Metaproteomic, meta transcriptomics, and metabolomics are three potent methods to accumulate information about microbial proteins, messenger RNA, and metabolites in a microbial community. These methods are currently being applied in laboratory settings to address our general lack of understanding of microbe-microbe interactions and microbe-environment interactions. 

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