Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context.

Benchmarking short-, long- and hybrid-read assemblers for metagenome sequencing of complex microbial communities.

Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.

DNA methylation in clonal duckweed (Lemna minor L.) lineages reflects current and historical environmental exposures.

Environmentally induced DNA methylation variants may mediate gene expression responses to environmental changes. If such induced variants are transgenerationally stable, there is potential for expression responses to persist over multiple generations. Our current knowledge in plants, however, is almost exclusively based on studies conducted in sexually reproducing species where the majority of DNA methylation changes are subject to resetting in germlines, limiting the potential for transgenerational epigenetics stress memory. Asexual reproduction circumvents germlines, and may therefore be more conducive to long-term inheritance of epigenetic marks. Taking advantage of the rapid clonal reproduction of the common duckweed Lemna minor, we hypothesize that long-term, transgenerational stress memory from exposure to high temperature can be detected in DNA methylation profiles. Using a reduced representation bisulphite sequencing approach (epiGBS), we show that temperature stress induces DNA hypermethylation at many CG and CHG cytosine contexts but not CHH. Additionally, differential methylation in CHG context that was observed was still detected in a subset of cytosines, even after 3-12 generations of culturing in a common environment. This demonstrates a memory effect of stress reflected in the methylome and that persists over multiple clonal generations. Structural annotation revealed that this memory effect in CHG methylation was enriched in transposable elements. The observed epigenetic stress memory is probably caused by stable transgenerational persistence of temperature-induced DNA methylation variants across clonal generations. To the extent that such epigenetic memory has functional consequences for gene expression and phenotypes, this result suggests potential for long-term modulation of stress responses in asexual plants.

Correction: Sallam et al. DNA Methylation Alterations in Fractionally Irradiated Rats and Breast Cancer Patients Receiving Radiotherapy. Int. J. Mol. Sci. 2022, 23, 16214.

Radia Tamarat and Susana Constantino Rosa Santos were not included as authors in the original publication [...].

DNA Methylation Alterations in Fractionally Irradiated Rats and Breast Cancer Patients Receiving Radiotherapy.

Radiation-Induced CardioVascular Disease (RICVD) is an important concern in thoracic radiotherapy with complex underlying pathophysiology. Recently, we proposed DNA methylation as a possible mechanism contributing to RICVD. The current study investigates DNA methylation in heart-irradiated rats and radiotherapy-treated breast cancer (BC) patients. Rats received fractionated whole heart X-irradiation (0, 0.92, 6.9 and 27.6 Gy total doses) and blood was collected after 1.5, 3, 7 and 12 months. Global and gene-specific methylation of the samples were evaluated; and gene expression of selected differentially methylated regions (DMRs) was validated in rat and BC patient blood. In rats receiving an absorbed dose of 27.6 Gy, DNA methylation alterations were detected up to 7 months with differential expression of cardiac-relevant DMRs. Of those, SLMAP showed increased expression at 1.5 months, which correlated with hypomethylation. Furthermore, E2F6 inversely correlated with a decreased global longitudinal strain. In BC patients, E2F6 and SLMAP exhibited differential expression directly and 6 months after radiotherapy, respectively. This study describes a systemic radiation fingerprint at the DNA methylation level, elucidating a possible association of DNA methylation to RICVD pathophysiology, to be validated in future mechanistic studies.

Soil microbial community structure and functionality changes in response to long-term metal and radionuclide pollution.

Microbial communities are essential for a healthy soil ecosystem. Metals and radionuclides can exert a persistent pressure on the soil microbial community. However, little is known on the effect of long-term co-contamination of metals and radionuclides on the microbial community structure and functionality. We investigated the impact of historical discharges of the phosphate and nuclear industry on the microbial community in the Grote Nete river basin in Belgium. Eight locations were sampled along a transect to the river edge and one location further in the field. Chemical analysis demonstrated a metal and radionuclide contamination gradient and revealed a distinct clustering of the locations based on all metadata. Moreover, a relation between the chemical parameters and the bacterial community structure was demonstrated. Although no difference in biomass was observed between locations, cultivation-dependent experiments showed that communities from contaminated locations survived better on singular metals than communities from control locations. Furthermore, nitrification, a key soil ecosystem process seemed affected in contaminated locations when combining metadata with microbial profiling. These results indicate that long-term metal and radionuclide pollution impacts the microbial community structure and functionality and provides important fundamental insights into microbial community dynamics in co-metal-radionuclide contaminated sites.

PaSiT: a novel approach based on short-oligonucleotide frequencies for efficient bacterial identification and typing.

MOTIVATION: One of the most widespread methods used in taxonomy studies to distinguish between strains or taxa is the calculation of average nucleotide identity. It requires a computationally expensive alignment step and is therefore not suitable for large-scale comparisons. Short oligonucleotide-based methods do offer a faster alternative but at the expense of accuracy. Here, we aim to address this shortcoming by providing a software that implements a novel method based on short-oligonucleotide frequencies to compute inter-genomic distances. RESULTS: Our tetranucleotide and hexanucleotide implementations, which were optimized based on a taxonomically well-defined set of over 200 newly sequenced bacterial genomes, are as accurate as the short oligonucleotide-based method TETRA and average nucleotide identity, for identifying bacterial species and strains, respectively. Moreover, the lightweight nature of this method makes it applicable for large-scale analyses. AVAILABILITY AND IMPLEMENTATION: The method introduced here was implemented, together with other existing methods, in a dependency-free software written in C, GenDisCal, available as source code from https://github.com/LM-UGent/GenDisCal. The software supports multithreading and has been tested on Windows and Linux (CentOS). In addition, a Java-based graphical user interface that acts as a wrapper for the software is also available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of the bacterial communities on recent Icelandic volcanic deposits of different ages.

BACKGROUND: Basalt is the most common igneous rock on the Earth's surface covering. Basalt-associated microorganisms drive the cycling and sequestration of different elements such as nitrogen, carbon and other nutrients, which facilitate subsequent pioneer and plant development, impacting long-term regulation of the Earth's temperature and biosphere. The initial processes of colonization and subsequent rock weathering by microbial communities are still poorly understood and relatively few data are available on the diversity and richness of the communities inhabiting successive and chronological lava flows. In this study, the bacterial communities present on lava deposits from different eruptions of the 1975-84 Krafla Fires (32-, 35- and 39-year old, respectively) at the Krafla, Iceland, were determined. RESULTS: Three sites were sampled for each deposit (32-, 35- and 39-year old), two proximal sites (at 10 m distance) and one more distant site (at 100 m from the two other sites). The determined chemical composition and metal concentrations were similar for the three basalt deposits. No significant differences were observed in the total number of cells in each flow. 16S rRNA gene amplicon sequencing showed that the most abundant classified phylum across the 3 flows was Proteobacteria, although predominance of Acidobacteria, Actinobacteria and Firmicutes was observed for some sampling sites. In addition, a considerable fraction of the operational taxonomic units remained unclassified. Alpha diversity (Shannon, inverse Simpson and Chao), HOMOVA and AMOVA only showed a significant difference for Shannon between the 32- and 39-year old flow (p < 0.05). Nonmetric multidimensional scaling (NMDS) analysis showed that age significantly (p = 0.026) influenced the leftward movement along NMDS axis 1. CONCLUSIONS: Although NMDS indicated that the (relatively small) age difference of the deposits appeared to impact the bacterial community, this analysis was not consistent with AMOVA and HOMOVA, indicating no significant difference in community structure. The combined results drive us to conclude that the (relatively small) age differences of the deposits do not appear to be the main factor shaping the microbial communities. Probably other factors such as spatial heterogeneity, associated carbon content, exogenous rain precipitations and wind also affect the diversity and dynamics.

IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNA gene amplicon sequencing data.

BACKGROUND: The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising, the removal of sequencing errors, is an important step in preprocessing amplicon sequencing data. The increasing popularity of the Illumina MiSeq platform for these applications requires the development of appropriate denoising methods. RESULTS: The newly proposed denoising algorithm IPED includes a machine learning method which predicts potentially erroneous positions in sequencing reads based on a combination of quality metrics. Subsequently, this information is used to group those error-containing reads with correct reads, resulting in error-free consensus reads. This is achieved by masking potentially erroneous positions during this clustering step. Compared to the second best algorithm available, IPED detects double the amount of errors. Reducing the error rate had a positive effect on the clustering of reads in operational taxonomic units, with an almost perfect correspondence between the number of clusters and the theoretical number of species present in the mock communities. CONCLUSION: Our algorithm IPED is a powerful denoising tool for correcting sequencing errors in Illumina MiSeq 16S rRNA gene amplicon sequencing data. Apart from significantly reducing the error rate of the sequencing reads, it has also a beneficial effect on their clustering into operational taxonomic units. IPED is freely available at http://science.sckcen.be/en/Institutes/EHS/MCB/MIC/Bioinformatics/ .

NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads.

BACKGROUND: The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision. RESULTS: The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units. CONCLUSIONS: NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.

CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies.

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units.

A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions.

Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations' variations, supported by genotypes' principal component analysis. Results show that the Egyptian's reference metabolic genes were clustered together with the Europeans', Ad-Mixed Americans', and South-Asians'. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations.

Limnospira indica PCC 8005 Supplementation Prevents Pelvic Irradiation-Induced Dysbiosis but Not Acute Inflammation in Mice.

Pelvic irradiation-induced mucositis secondarily leads to dysbiosis, which seriously affects patients' quality of life after treatment. No safe and effective radioprotector or mitigator has yet been approved for clinical therapy. Here, we investigated the potential protective effects of fresh biomass of Limnospira indica PCC 8005 against ionizing irradiation-induced mucositis and dysbiosis in respect to benchmark probiotic Lacticaseibacillus rhamnosus GG ATCC 53103. For this, mice were supplemented daily before and after 12 Gy X-irradiation of the pelvis. Upon sacrifice, food supplements' efficacy was assessed for intestinal barrier protection, immunomodulation and changes in the microbiota composition. While both could not confer barrier protection or significant immunomodulatory effects, 16S microbial profiling revealed that L. indica PCC 8005 and L. rhamnosus GG could prevent pelvic irradiation-induced dysbiosis. Altogether, our data show that-besides benchmarked L. rhamnosus GG-L. indica PCC 8005 is an interesting candidate to further explore as a radiomitigator counteracting pelvic irradiation-induced dysbiosis in the presented in vivo irradiation-gut-microbiota platform.

Dissecting the role of the gut microbiome and fecal microbiota transplantation in radio- and immunotherapy treatment of colorectal cancer.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and poses a major burden on the human health worldwide. At the moment, treatment of CRC consists of surgery in combination with (neo)adjuvant chemotherapy and/or radiotherapy. More recently, immune checkpoint blockers (ICBs) have also been approved for CRC treatment. In addition, recent studies have shown that radiotherapy and ICBs act synergistically, with radiotherapy stimulating the immune system that is activated by ICBs. However, both treatments are also associated with severe toxicity and efficacy issues, which can lead to temporary or permanent discontinuation of these treatment programs. There's growing evidence pointing to the gut microbiome playing a role in these issues. Some microorganisms seem to contribute to radiotherapy-associated toxicity and hinder ICB efficacy, while others seem to reduce radiotherapy-associated toxicity or enhance ICB efficacy. Consequently, fecal microbiota transplantation (FMT) has been applied to reduce radio- and immunotherapy-related toxicity and enhance their efficacies. Here, we have reviewed the currently available preclinical and clinical data in CRC treatment, with a focus on how the gut microbiome influences radio- and immunotherapy toxicity and efficacy and if these treatments could benefit from FMT.

Are Arabidopsis thaliana plants able to recover from exposure to gamma radiation? A molecular perspective.

Most plant research focuses on the responses immediately after exposure to ionizing irradiation (IR). However, it is as important to investigate how plants recover after exposure since this has a profound effect on future plant growth and development and hence on the long-term consequences of exposure to stress. This study aimed to investigate the IR-induced responses after exposure and during recovery by exposing 1-week old A. thaliana seedlings to gamma dose rates ranging from 27 to 103.7 mGy/h for 2 weeks and allowing them to recover for 4 days. A high-throughput RNAsequencing analysis was carried out. An enrichment of GO terms related to the metabolism of hormones was observed both after irradiation and during recovery at all dose rates. While plants exposed to the lowest dose rate activate defence responses after irradiation, they recover from the IR by resuming normal growth during the recovery period. Plants exposed to the intermediate dose rate invest in signalling and defence after irradiation. During recovery, in the plants exposed to the highest dose rate, fundamental metabolic processes such as photosynthesis and RNA modification were still affected. This might lead to detrimental effects in the long-term or in the next generations of those irradiated plants.

A cellular screening platform, stably expressing DENV2 NS5, defines a novel anti-DENV mechanism of action of Apigenin based on STAT2 activation.

Type I interferon (IFN-I) evasion by Dengue virus (DENV) is key in DENV pathogenesis. The non-structural protein 5 (NS5) antagonizes IFN-I response through the degradation of the signal transducer and activator of transcription 2 (STAT2). We developed a K562 cell-based platform, for high throughput screening of compounds potentially counteracting the NS5-mediated antagonism of IFN-I signaling. Upon a screening with a library of 1220 approved drugs, 3 compounds previously linked to DENV inhibition (Apigenin, Chrysin, and Luteolin) were identified. Luteolin and Apigenin determined a significant inhibition of DENV2 replication in Huh7 cells and the restoration of STAT2 phosphorylation in both cell systems. Apigenin and Luteolin were able to stimulate STAT2 even in the absence of infection. Despite the "promiscuous" and "pan-assay-interfering" nature of Luteolin, Apigenin promotes STAT2 Tyr 689 phosphorylation and activation, highlighting the importance of screening for compounds able to interact with host factors, to counteract viral proteins capable of dampening innate immune responses.

Modeling the Effects of Cypermethrin Toxicity on Ovalbumin-Induced Allergic Pneumonitis Rats: Macrophage Phenotype Differentiation and p38/STAT6 Signaling Are Candidate Targets of Pirfenidone Treatment.

Although the classic form of asthma is characterized by chronic pneumonitis with eosinophil infiltration and steroid responsivity, asthma has multifactorial pathogenesis and various clinical phenotypes. Previous studies strongly suggested that chemical exposure could influence the severity and course of asthma and reduce its steroid responsiveness. Cypermethrin (CYP), a common pesticide used in agriculture, was investigated for the possible aggravation of the ovalbumin (OVA)-induced allergic pneumonitis and the possible induction of steroid resistance in rats. Additionally, it was investigated whether pirfenidone (PFD) could substitute dexamethasone, as an alternative treatment option, for the induced steroid resistance. Fifty-six male Wistar albino rats were randomly divided into seven groups: control, PFD alone, allergic pneumonitis, CYP alone, allergic pneumonitis/CYP-exposed, allergic pneumonitis/CYP/dexamethasone (Dex), and allergic pneumonitis/CYP/PFD-treated groups. Allergic pneumonitis was induced by three intraperitoneal OVA injections administered once a week, followed by an intranasal OVA instillation challenge. CYP (25 mg/kg/d), Dex (1 mg/kg/d), and PFD (100 mg/kg/d) were administered orally from day 15 to the end of the experiment. Bronchoalveolar lavage fluid (BALF) was analyzed for cytokine levels. Hematoxylin and eosin (H&E) and periodic acid Schiff (PAS)-stained lung sections were prepared. Immunohistochemical identification of p38 MAPK and lung macrophages was performed. The inflammatory/oxidative status of the lung and PCR-quantification of the STAT6, p38 MAPK, MUC5AC, and IL-13 genes were carried out. The allergic pneumonitis-only group showed eosinophil-mediated inflammation (p < 0.05). Further CYP exposure aggravated lung inflammation and showed steroid-resistant changes, p38 activation, neutrophil-mediated, M1 macrophage-related inflammation (p < 0.05). All changes were reversed (p < 0.05) by PFD, meanwhile not by dexamethasone treatment. Pirfenidone could replace dexamethasone treatment in the current rat model of CYP-induced severe steroid-resistant asthma via inhibiting the M1 macrophage differentiation through modulation of the STAT6/p38 MAPK pathway.

MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (ΔG).

The investigation of small interfering RNA (siRNA) and its posttranscriptional gene-regulation has become an extremely important research topic, both for fundamental reasons and for potential longer-term therapeutic benefits. Several factors affect the functionality of siRNA including positional preferences, target accessibility and other thermodynamic features. State of the art tools aim to optimize the selection of target siRNAs by identifying those that may have high experimental inhibition. Such tools implement artificial neural network models as Biopredsi and ThermoComposition21, and linear regression models as DSIR, i-Score and Scales, among others. However, all these models have limitations in performance. In this work, a neural-network trained new siRNA scoring/efficacy prediction model was developed based on combining two existing scoring algorithms (ThermoComposition21 and i-Score), together with the whole stacking energy (ΔG), in a multi-layer artificial neural network. These three parameters were chosen after a comparative combinatorial study between five well known tools. Our developed model, 'MysiRNA' was trained on 2431 siRNA records and tested using three further datasets. MysiRNA was compared with 11 alternative existing scoring tools in an evaluation study to assess the predicted and experimental siRNA efficiency where it achieved the highest performance both in terms of correlation coefficient (R(2)=0.600) and receiver operating characteristics analysis (AUC=0.808), improving the prediction accuracy by up to 18% with respect to sensitivity and specificity of the best available tools. MysiRNA is a novel, freely accessible model capable of predicting siRNA inhibition efficiency with improved specificity and sensitivity. This multiclassifier approach could help improve the performance of prediction in several bioinformatics areas. MysiRNA model, part of MysiRNA-Designer package [1], is expected to play a key role in siRNA selection and evaluation.

Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data.

The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome-assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.

Accurate prediction of metagenome-assembled genome completeness by MAGISTA, a random forest model built on alignment-free intra-bin statistics.

BACKGROUND: Although the total number of microbial taxa on Earth is under debate, it is clear that only a small fraction of these has been cultivated and validly named. Evidently, the inability to culture most bacteria outside of very specific conditions severely limits their characterization and further studies. In the last decade, a major part of the solution to this problem has been the use of metagenome sequencing, whereby the DNA of an entire microbial community is sequenced, followed by the in silico reconstruction of genomes of its novel component species. The large discrepancy between the number of sequenced type strain genomes (around 12,000) and total microbial diversity (106-1012 species) directs these efforts to de novo assembly and binning. Unfortunately, these steps are error-prone and as such, the results have to be intensely scrutinized to avoid publishing incomplete and low-quality genomes. RESULTS: We developed MAGISTA (metagenome-assembled genome intra-bin statistics assessment), a novel approach to assess metagenome-assembled genome quality that tackles some of the often-neglected drawbacks of current reference gene-based methods. MAGISTA is based on alignment-free distance distributions between contig fragments within metagenomic bins, rather than a set of reference genes. For proper training, a highly complex genomic DNA mock community was needed and constructed by pooling genomic DNA of 227 bacterial strains, specifically selected to obtain a wide variety representing the major phylogenetic lineages of cultivable bacteria. CONCLUSIONS: MAGISTA achieved a 20% reduction in root-mean-square error in comparison to the marker gene approach when tested on publicly available mock metagenomes. Furthermore, our highly complex genomic DNA mock community is a very valuable tool for benchmarking (new) metagenome analysis methods.