A novel semi-supervised algorithm for the taxonomic assignment of metagenomic reads

Evaluation of tools for taxonomic classification of viruses

Viruses are the most abundant infectious agents on earth, and they infect living organisms such as bacteria, plants and animals, among others. They play an important role in the balance of different ecosystems by modulating microbial populations. In humans, they are responsible for some common diseases and may cause severe illnesses. Viral metagenomic studies have become essential and offer the possibility to understand and extend the knowledge of virus diversity and functionality. For these approaches, an essential step is the classification of viral sequences. In this work, 11 taxonomic classification tools were compared by analysing their performances, in terms of sensitivity and precision, to classify reads at the species and family levels using the same (viral and nonviral) datasets and evaluation metrics, as well as their processing times and memory requirements. The results showed that factors such as richness (numbers of viral species in samples), taxonomic level in the classification and read length influence tool performance. High values of viral richness in samples decreased the performances of most tools. Additionally, the classifications were better at higher taxonomic levels, such as families, compared to lower taxonomic levels, such as species, and were more evident in short reads. The results also indicated that BLAST and Kraken2 were the best tools for classifying all types of reads, while FastViromeExplorer and VirusFinder were only good when used for long reads and Centrifuge, DIAMOND, and One Codex when used for short reads. Regarding nonviral datasets (human and bacterial), all tools correctly classified them as nonviral.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Active semi-supervised learning for biological data classification

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semi-supervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.

High-resolution characterization of the human microbiome

The human microbiome plays an important and increasingly recognized role in human health. Studies of the microbiome typically use targeted sequencing of the 16S rRNA gene, whole metagenome shotgun sequencing, or other meta-omic technologies to characterize the microbiome's composition, activity, and dynamics. Processing, analyzing, and interpreting these data involve numerous computational tools that aim to filter, cluster, annotate, and quantify the obtained data and ultimately provide an accurate and interpretable profile of the microbiome's taxonomy, functional capacity, and behavior. These tools, however, are often limited in resolution and accuracy and may fail to capture many biologically and clinically relevant microbiome features, such as strain-level variation or nuanced functional response to perturbation. Over the past few years, extensive efforts have been invested toward addressing these challenges and developing novel computational methods for accurate and high-resolution characterization of microbiome data. These methods aim to quantify strain-level composition and variation, detect and characterize rare microbiome species, link specific genes to individual taxa, and more accurately characterize the functional capacity and dynamics of the microbiome. These methods and the ability to produce detailed and precise microbiome information are clearly essential for informing microbiome-based personalized therapies. In this review, we survey these methods, highlighting the challenges each method sets out to address and briefly describing methodological approaches.