Self-generating autocatalytic networks: structural results, algorithms and their relevance to early biochemistry.

The concept of an autocatalytic network of reactions that can form and persist, starting from just an available food source, has been formalized by the notion of a reflexively autocatalytic and food-generated (RAF) set. The theory and algorithmic results concerning RAFs have been applied to a range of settings, from metabolic questions arising at the origin of life, to ecological networks, and cognitive models in cultural evolution. In this article, we present new structural and algorithmic results concerning RAF sets, by studying more complex modes of catalysis that allow certain reactions to require multiple catalysts (or to not require catalysis at all), and discuss the differing ways catalysis has been viewed in the literature. We also focus on the structure and analysis of minimal RAFs and derive structural results and polynomial-time algorithms. We then apply these new methods to a large metabolic network to gain insights into possible biochemical scenarios near the origin of life.

NeighborNet: improved algorithms and implementation.

NeighborNet constructs phylogenetic networks to visualize distance data. It is a popular method used in a wide range of applications. While several studies have investigated its mathematical features, here we focus on computational aspects. The algorithm operates in three steps. We present a new simplified formulation of the first step, which aims at computing a circular ordering. We provide the first technical description of the second step, the estimation of split weights. We review the third step by constructing and drawing the network. Finally, we discuss how the networks might best be interpreted, review related approaches, and present some open questions.

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter.

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Visualizing incompatibilities in phylogenetic trees using consensus outlines.

Phylogenetic analysis frequently leads to the creation of many phylogenetic trees, either from using multiple genes or methods, or through bootstrapping or Bayesian analysis. A consensus tree is often used to summarize what the trees have in common. Consensus networks were introduced to also allow the visualization of the main incompatibilities among the trees. However, in practice, such networks often contain a large number of nodes and edges, and can be non-planar, making them difficult to interpret. Here, we introduce the new concept of a phylogenetic consensus outline, which provides a planar visualization of incompatibilities in the input trees, without the complexities of a consensus network. Furthermore, we present an effective algorithm for its computation. We demonstrate its usage and explore how it compares to other methods on a Bayesian phylogenetic analysis of languages using data from a published database and on multiple gene trees from a published study on water lilies.

DIAMOND + MEGAN Microbiome Analysis.

Metagenomics is the study of microbiomes using DNA sequencing technologies. Basic computational tasks are to determine the taxonomic composition (who is out there?), the functional composition (what can they do?), and also to correlate changes of composition to changes in external parameters (how do they compare?). One approach to address these issues is to first align all sequences against a protein reference database such as NCBI-nr and to then perform taxonomic and functional binning of all sequences based on their alignments. The resulting classifications can then be interactively analyzed and compared. Here we illustrate how to pursue this approach using the DIAMOND+MEGAN pipeline, on two different publicly available datasets, one containing short-read samples and other containing long-read samples.

MAIRA: Protein-based Analysis of MinION Reads on a Laptop.

Third-generation sequencing technologies are being increasingly used in microbiome research and this has given rise to new challenges in computational microbiome analysis. Oxford Nanopore's MinION is a portable sequencer that streams data that can be basecalled on-the-fly. Here we give an introduction to the MAIRA software, which is designed to analyze MinION sequencing reads from a microbiome sample, as they are produced in real-time, on a laptop. The software processes reads in batches and updates the presented analysis after each batch. There are two analysis steps: First, protein alignments are calculated to determine which genera might be present in a sample. When strong evidence for a genus is found, then, in a second step, a more detailed analysis is performed by aligning the reads against the proteins of all species in the detected genus. The program presents a detailed analysis of species, antibiotic resistance genes, and virulence factors.

MeganServer: facilitating interactive access to metagenomic data on a server.

MOTIVATION: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. AVAILABILITY AND IMPLEMENTATION: MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of functional classification systems.

In microbiome analysis, functional profiling is based on assigning reads or contigs to terms or nodes in a functional classification system. There are a number of large, general-purpose functional classifications that are in use, such as eggNOG, KEGG, InterPro and SEED. Smaller, special-purpose classifications include CARD, EC, MetaCyc and VFDB. Here, we compare the different classifications in terms of their overlap, redundancy, structure and assignment rates. We also provide mappings between main concepts in different classifications. For the large classifications, we find that eggNOG performs the best with respect to sequence redundancy and structure, SEED has the cleanest hierarchy, whereas KEGG and InterPro:BP might be more informative for medical applications. We illustrate the practical assignment rates for different classifications using a number of metagenomic samples.

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interactive analysis of biosurfactants in fruit-waste fermentation samples using BioSurfDB and MEGAN.

Agroindustrial waste, such as fruit residues, are a renewable, abundant, low-cost, commonly-used carbon source. Biosurfactants are molecules of increasing interest due to their multifunctional properties, biodegradable nature and low toxicity, in comparison to synthetic surfactants. A better understanding of the associated microbial communities will aid prospecting for biosurfactant-producing microorganisms. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were subjected to autochthonous fermentation, so as to promote the growth of their associated microbiota, followed by short-read metagenomic sequencing. Using the DIAMOND+MEGAN analysis pipeline, taxonomic analysis shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. Functional analysis indicates high similarity among samples and a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. To simplify the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. These results indicate that microbiota obtained from autochthonous fermentation have the genetic potential for biosynthesis of biosurfactants, suggesting that fruit wastes may provide a source of biosurfactant-producing microorganisms, with applications in the agricultural, chemical, food and pharmaceutical industries.

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Easy efficient HDR-based targeted knock-in in Saccharomyces cerevisiae genome using CRISPR-Cas9 system.

During the last two decades, yeast has been used as a biological tool to produce various small molecules, biofuels, etc., using an inexpensive bioprocess. The application of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated protein (Cas) techniques in yeast genetic and metabolic engineering has made a paradigm shift, particularly with a significant improvement in targeted chromosomal integration using synthetic donor constructs, which was previously a challenge. This study reports the CRISPR-Cas9-based highly efficient strategy for targeted chromosomal integration and in-frame expression of a foreign gene in the genome of Saccharomyces cerevisiae (S. cerevisiae) by homology-dependent recombination (HDR); our optimized methods show that CRISPR-Cas9-based chromosomal targeted integration of small constructs at multiple target sites of the yeast genome can be achieved with an efficiency of 74%. Our study also suggests that 15 bp microhomology flanked arms are sufficient for 50% targeted knock-in at minimal knock-in construct concentration. Whole-genome sequencing confirmed that there is no off-target effect. This study provides a comprehensive and streamlined protocol that will support the targeted integration of essential genes into the yeast genome for synthetic biology and other industrial purposes.Highlights• CRISPR-Cas9 based in-frame expression of foreign protein in Saccharomyces cerevisiae using Homology arm without a promoter.• As low as 15 base pairs of microhomology (HDR) are sufficient for targeted integration in Saccharomyces cerevisiae.• The methodology is highly efficient and very specific as no off-targeted effects were shown by the whole-genome sequence.

Microbial Phylogenetic Context Using Phylogenetic Outlines.

Microbial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the Genome Taxonomy Database taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general than a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.

Interplay of Various Evolutionary Modes in Genome Diversification and Adaptive Evolution of the Family Sulfolobaceae.

Sulfolobaceae family, comprising diverse thermoacidophilic and aerobic sulfur-metabolizing Archaea from various geographical locations, offers an ideal opportunity to infer the evolutionary dynamics across the members of this family. Comparative pan-genomics coupled with evolutionary analyses has revealed asymmetric genome evolution within the Sulfolobaceae family. The trend of genome streamlining followed by periods of differential gene gains resulted in an overall genome expansion in some species of this family, whereas there was reduction in others. Among the core genes, both Sulfolobus islandicus and Saccharolobus solfataricus showed a considerable fraction of positively selected genes and also higher frequencies of gene acquisition. In contrast, Sulfolobus acidocaldarius genomes experienced substantial amount of gene loss and strong purifying selection as manifested by relatively lower genome size and higher genome conservation. Central carbohydrate metabolism and sulfur metabolism coevolved with the genome diversification pattern of this archaeal family. The autotrophic CO2 fixation with three significant positively selected enzymes from S. islandicus and S. solfataricus was found to be more imperative than heterotrophic CO2 fixation for Sulfolobaceae. Overall, our analysis provides an insight into the interplay of various genomic adaptation strategies including gene gain-loss, mutation, and selection influencing genome diversification of Sulfolobaceae at various taxonomic levels and geographical locations.

Normalising phylogenetic networks.

Rooted phylogenetic networks provide a way to describe species' relationships when evolution departs from the simple model of a tree. However, networks inferred from genomic data can be highly tangled, making it difficult to discern the main reticulation signals present. In this paper, we describe a natural way to transform any rooted phylogenetic network into a simpler canonical network, which has desirable mathematical and computational properties, and is based only on the 'visible' vertices in the original network. The method has been implemented and we demonstrate its application to some examples.

DIAMOND+MEGAN: Fast and Easy Taxonomic and Functional Analysis of Short and Long Microbiome Sequences.

One main approach to computational analysis of microbiome sequences is to first align against a reference database of annotated protein sequences (NCBI-nr) and then perform taxonomic and functional binning of the sequences based on the resulting alignments. For both short and long reads (or assembled contigs), alignment is performed using DIAMOND, whereas taxonomic and functional binning, followed by inter- active exploration and analysis, is performed using MEGAN. We provide two step-by-step descriptions of this approach: © 2021 The Authors. Basic Protocol 1: Taxonomic and functional analysis of short read microbiome sequences Support Protocol 1: Preprocessing Basic Protocol 2: taxonomic and functional analysis of assembled long read microbiome sequences Support Protocol 2: Taxonomic binning and CheckM.

Recovery of complete genomes and non-chromosomal replicons from activated sludge enrichment microbial communities with long read metagenome sequencing.

New long read sequencing technologies offer huge potential for effective recovery of complete, closed genomes from complex microbial communities. Using long read data (ONT MinION) obtained from an ensemble of activated sludge enrichment bioreactors we recover 22 closed or complete genomes of community members, including several species known to play key functional roles in wastewater bioprocesses, specifically microbes known to exhibit the polyphosphate- and glycogen-accumulating organism phenotypes (namely Candidatus Accumulibacter and Dechloromonas, and Micropruina, Defluviicoccus and Candidatus Contendobacter, respectively), and filamentous bacteria (Thiothrix) associated with the formation and stability of activated sludge flocs. Additionally we demonstrate the recovery of close to 100 circularised plasmids, phages and small microbial genomes from these microbial communities using long read assembled sequence. We describe methods for validating long read assembled genomes using their counterpart short read metagenome-assembled genomes, and assess the influence of different correction procedures on genome quality and predicted gene quality. Our findings establish the feasibility of performing long read metagenome-assembled genome recovery for both chromosomal and non-chromosomal replicons, and demonstrate the value of parallel sampling of moderately complex enrichment communities to obtaining high quality reference genomes of key functional species relevant for wastewater bioprocesses.

The structure of autocatalytic networks, with application to early biochemistry.

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronized fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organization of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualize RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualize the core aspects of primitive biochemistry.