A review of bioinformatics platforms for comparative genomics. Recent developments of the EDGAR 2.0 platform and its utility for taxonomic and phylogenetic studies

Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review

This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.

Concatenator, a user-friendly program to concatenate DNA sequences, implementing graphical user interfaces for MAFFT and FastTree

Motivation

Phylogenetic and phylogenomic analyses require multi-gene input files in different formats, but there are few user-friendly programs facilitating the workflow of combining, concatenating or separating, aligning and exploring multi-gene datasets.

Results

We present Concatenator, a user-friendly GUI-driven program that accepts single-marker and multi-marker DNA sequences in different input formats, including Fasta, Phylip and Nexus, and that outputs concatenated sequences as single-marker or multi-marker Fasta, interleaved nexus or Phylip files, including command files for downstream model selection in IQ-TREE. It includes the option to (re)align markers with MAFFT and produces exploratory trees with FastTree. Although tailored for medium-sized phylogenetic projects, Concatenator is able to process phylogenomic datasets of up to 30 000 markers.

Availability and implementation

Concatenator is written in Python, with C extensions for MAFFT and FastTree. Compiled stand-alone executables of Concatenator for MS Windows and Mac OS along with a detailed manual can be downloaded from www.itaxotools.org; the source code is openly available on GitHub (https://github.com/iTaxoTools/ConcatenatorGui).

Metagenomics: A Tool for Exploring Key Microbiome With the Potentials for Improving Sustainable Agriculture

Microorganisms are immense in nature and exist in every imaginable ecological niche, performing a wide range of metabolic processes. Unfortunately, using traditional microbiological methods, most microorganisms remain unculturable. The emergence of metagenomics has resolved the challenge of capturing the entire microbial community in an environmental sample by enabling the analysis of whole genomes without requiring culturing. Metagenomics as a non-culture approach encompasses a greater amount of genetic information than traditional approaches. The plant root-associated microbial community is essential for plant growth and development, hence the interactions between microorganisms, soil, and plants is essential to understand and improve crop yields in rural and urban agriculture. Although some of these microorganisms are currently unculturable in the laboratory, metagenomic techniques may nevertheless be used to identify the microorganisms and their functional traits. A detailed understanding of these organisms and their interactions should facilitate an improvement of plant growth and sustainable crop production in soil and soilless agriculture. Therefore, the objective of this review is to provide insights into metagenomic techniques to study plant root-associated microbiota and microbial ecology. In addition, the different DNA-based techniques and their role in elaborating plant microbiomes are discussed. As an understanding of these microorganisms and their biotechnological potentials are unlocked through metagenomics, they can be used to develop new, useful and unique bio-fertilizers and bio-pesticides that are not harmful to the environment.

Discovery and mining of enzymes from the human gut microbiome

Advances in technological and bioinformatics approaches have led to the generation of a plethora of human gut metagenomic datasets. Metabolomics has also provided substantial data regarding the small metabolites produced and modified by the microbiota. Comparatively, the microbial enzymes mediating the transformation of metabolites have not been intensively investigated. Here, we discuss the recent efforts and technologies used for discovering and mining enzymes from the human gut microbiota. The wealth of knowledge on metabolites, reactions, genome sequences, and structures of proteins, may drive the development of strategies for enzyme mining. Ongoing efforts to annotate gut microbiota enzymes will explain catalytic mechanisms that may guide the clinical applications of the gut microbiome for diagnostic and therapeutic purposes.

Genome analysis provides insights into the biocontrol ability of Mitsuaria sp. strain TWR114

Mitsuaria sp. TWR114 is a biocontrol agent against tomato bacterial wilt (TBW). We aimed to gain genomic insights relevant to the biocontrol mechanisms and colonization ability of this strain. The draft genome size was found to be 5,632,523 bp, with a GC content of 69.5%, assembled into 1144 scaffolds. Genome annotation predicted a total of 4675 protein coding sequences (CDSs), 914 pseudogenes, 49 transfer RNAs, 3 noncoding RNAs, and 2 ribosomal RNAs. Genome analysis identified multiple CDSs associated with various pathways for the metabolism and transport of amino acids and carbohydrates, motility and chemotactic capacities, protection against stresses (oxidative, antibiotic, and phage), production of secondary metabolites, peptidases, quorum-quenching enzymes, and indole-3-acetic acid, as well as protein secretion systems and their related appendages. The genome resource will extend our understanding of the genomic features related to TWR114's biocontrol and colonization abilities and facilitate its development as a new biopesticide against TBW.

Using affinity propagation clustering for identifying bacterial clades and subclades with whole-genome sequences of Francisella tularensis

By combining a reference-independent SNP analysis and average nucleotide identity (ANI) with affinity propagation clustering (APC), we developed a significantly improved methodology allowing resolving phylogenetic relationships, based on objective criteria. These bioinformatics tools can be used as a general ruler to determine phylogenetic relationships and clustering of bacteria, exemplary done with Francisella (F.) tularensis. Molecular epidemiology of F. tularensis is currently assessed mostly based on laboratory methods and molecular analysis. The high evolutionary stability and the clonal nature makes Francisella ideal for subtyping with single nucleotide polymorphisms (SNPs). Sequencing and real-time PCR can be used to validate the SNP analysis. We investigate whole-genome sequences of 155 F. tularensis subsp. holarctica isolates. Phylogenetic testing was based on SNPs and average nucleotide identity (ANI) as reference independent, alignment-free methods taking small-scale and large-scale differences within the genomes into account. Especially the whole genome SNP analysis with kSNP3.0 allowed deciphering quite subtle signals of systematic differences in molecular variation. Affinity propagation clustering (APC) resulted in three clusters showing the known clades B.4, B.6, and B.12. These data correlated with the results of real-time PCR assays targeting canSNPs loci. Additionally, we detected two subtle sub-clusters. SplitsTree was used with standard-setting using the aligned SNPs from Parsnps. Together APC, HierBAPS, and SplitsTree enabled us to generate hypotheses about epidemiologic relationships between bacterial clusters and describing the distribution of isolates. Our data indicate that the choice of the typing technique can increase our understanding of the pathogenesis and transmission of diseases with the eventual for prevention. This is opening perspectives to be applied to other bacterial species. The data provide evidence that Germany might be the collision zone where the clade B.12, also known as the East European clade, overlaps with the clade B.6, also known as the Iberian clade. Described methods allow generating a new, more detailed perspective for F. tularensis subsp. holarctica phylogeny. These results may encourage to determine phylogenetic relationships and clustering of other bacteria the same way.

Codon Usage Optimization in the Prokaryotic Tree of Life: How Synonymous Codons Are Differentially Selected in Sequence Domains with Different Expression Levels and Degrees of Conservation

The prokaryotic genomes—the current heritage of the most ancient life forms on earth—are comprised of diverse gene sets, all characterized by varied origins, ancestries, and spatial-temporal expression patterns. Such genetic diversity has for a long time raised the question of how cells shape their coding strategies to optimize protein demands (i.e., product abundance) and accuracy (i.e., translation fidelity) through the use of the same genetic code in genomes with GC contents that range from less than 20 to more than 80%. Here, we present evidence on how codon usage is adjusted in the prokaryotic tree of life and on how specific biases have operated to improve translation. Through the use of proteome data, we characterized conserved and variable sequence domains in genes of either high or low expression level and quantitated the relative weight of efficiency and accuracy—as well as their interaction—in shaping codon usage in prokaryotes.

Prokaryote genomes exhibit a wide range of GC contents and codon usages, both resulting from an interaction between mutational bias and natural selection. In order to investigate the basis underlying specific codon changes, we performed a comprehensive analysis of 29 different prokaryote families. The analysis of core gene sets with increasing ancestries in each family lineage revealed that the codon usages became progressively more adapted to the tRNA pools. While, as previously reported, highly expressed genes presented the most optimized codon usage, the singletons contained the less selectively favored codons. The results showed that usually codons with the highest translational adaptation were preferentially enriched. In agreement with previous reports, a C bias in 2- to 3-fold pyrimidine-ending codons, and a U bias in 4-fold codons occurred in all families, irrespective of the global genomic GC content. Furthermore, the U biases suggested that U₃-mRNA–U₃₄-tRNA interactions were responsible for a prominent codon optimization in both the most ancestral core and the highly expressed genes. A comparative analysis of sequences that encode conserved (cr) or variable (vr) translated products, with each one being under high (HEP) and low (LEP) expression levels, demonstrated that the efficiency was more relevant (by a factor of 2) than accuracy to modeling codon usage. Finally, analysis of the third position of codons (GC3) revealed that in genomes with global GC contents higher than 35 to 40%, selection favored a GC3 increase, whereas in genomes with very low GC contents, a decrease in GC3 occurred. A comprehensive final model is presented in which all patterns of codon usage variations are condensed in four distinct behavioral groups.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Proposal to reclassify Streptobacillus hongkongensis into a novel genus as Pseudostreptobacillus hongkongensis gen. nov., comb. nov

The reclassification of Streptobacillus hongkongensis as Pseudostreptobacillus hongkongensis gen. nov., comb. nov. is proposed because of the separate phylogenetic position on the basis of the 16S rRNA gene sequence phylogeny, the combined analysis of the three protein-coding housekeeping genes groEL, gyrB and recA and a core genome sequence phylogeny to all other Streptobacillus species that is supported by phenotypic differences. The species Pseudostreptobacillus hongkongensis is the type species of the genus. The type strain is HKU33^T, JCM 18691^T, NCTC 13659^T, DSM 26322^T.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

AAI-profiler: fast proteome-wide exploratory analysis reveals taxonomic identity, misclassification and contamination

We present AAI-profiler, a web server for exploratory analysis and quality control in comparative genomics. AAI-profiler summarizes proteome-wide sequence search results to identify novel species, assess the need for taxonomic reclassification and detect multi-isolate and contaminated samples. AAI-profiler visualises results using a scatterplot that shows the Average Amino-acid Identity (AAI) from the query proteome to all similar species in the sequence database. Taxonomic groups are indicated by colour and marker styles, making outliers easy to spot. AAI-profiler uses SANSparallel to perform high-performance homology searches, making proteome-wide analysis possible. We demonstrate the efficacy of AAI-profiler in the discovery of a close relationship between two bacterial symbionts of an omnivorous pirate bug (Orius) and a thrip (Frankliniella occidentalis), an important pest in agriculture. The symbionts represent novel species within the genus Rosenbergiella so far described only in floral nectar. AAI-profiler is easy to use, the analysis presented only required two mouse clicks and was completed in a few minutes. AAI-profiler is available at http://ekhidna2.biocenter.helsinki.fi/AAI.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.