Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Whole genome analysis of the koa wilt pathogen (Fusarium oxysporum f. sp. koae) and the development of molecular tools for early detection and monitoring

BACKGROUND

Development and application of DNA-based methods to distinguish highly virulent isolates of Fusarium oxysporum f. sp. koae [Fo koae; cause of koa wilt disease on Acacia koa (koa)] will help disease management through early detection, enhanced monitoring, and improved disease resistance-breeding programs.

RESULTS

This study presents whole genome analyses of one highly virulent Fo koae isolate and one non-pathogenic F. oxysporum (Fo) isolate. These analyses allowed for the identification of putative lineage-specific DNA and predicted genes necessary for disease development on koa. Using putative chromosomes and predicted gene comparisons, Fo koae-exclusive, virulence genes were identified. The putative lineage-specific DNA included identified genes encoding products secreted in xylem (e. g., SIX1 and SIX6) that may be necessary for disease development on koa. Unique genes from Fo koae were used to develop pathogen-specific PCR primers. These diagnostic primers allowed target amplification in the characterized highly virulent Fo koae isolates but did not allow product amplification in low-virulence or non-pathogenic isolates of Fo. Thus, primers developed in this study will be useful for early detection and monitoring of highly virulent strains of Fo koae. Isolate verification is also important for disease resistance-breeding programs that require a diverse set of highly virulent Fo koae isolates for their disease-screening assays to develop disease-resistant koa.

CONCLUSIONS

These results provide the framework for understanding the pathogen genes necessary for koa wilt disease and the genetic variation of Fo koae populations across the Hawaiian Islands.

Digitalization in microbiology - Paving the path to sustainable circular bioeconomy

The transition to a sustainable bio-based circular economy requires cutting edge technologies that ensure economic growth with environmentally responsible action. This transition will only be feasible when the opportunities of digitalization are also exploited. Digital methods and big data handling have already found their way into life sciences and generally offer huge potential in various research areas. While computational analyses of microbial metagenome data have become state of the art, the true potential of bioinformatics remains mostly untapped so far. In this article we present challenges and opportunities of digitalization including multi-omics approaches in discovering and exploiting the microbial diversity of the planet with the aim to identify robust biocatalysts for application in sustainable bioprocesses as part of the transition from a fossil-based to a bio-based circular economy. This will contribute to solving global challenges, including utilization of natural resources, food supply, health, energy and the environment.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.