Automated ensemble assembly and validation of microbial genomes

The continued democratization of DNA sequencing has sparked a new wave of development of genome assembly and assembly validation methods. As individual research labs, rather than centralized centers, begin to sequence the majority of new genomes, it is important to establish best practices for genome assembly. However, recent evaluations such as GAGE and the Assemblathon have concluded that there is no single best approach to genome assembly. Instead, it is preferable to generate multiple assemblies and validate them to determine which is most useful for the desired analysis; this is a labor-intensive process that is often impossible or unfeasible. To encourage best practices supported by the community, we present iMetAMOS, an automated ensemble assembly pipeline; iMetAMOS encapsulates the process of running, validating, and selecting a single assembly from multiple assemblies. iMetAMOS packages several leading open-source tools into a single binary that automates parameter selection and execution of multiple assemblers, scores the resulting assemblies based on multiple validation metrics, and annotates the assemblies for genes and contaminants. We demonstrate the utility of the ensemble process on 225 previously unassembled Mycobacterium tuberculosis genomes as well as a Rhodobacter sphaeroides benchmark dataset. On these real data, iMetAMOS reliably produces validated assemblies and identifies potential contamination without user intervention. In addition, intelligent parameter selection produces assemblies of R. sphaeroides comparable to or exceeding the quality of those from the GAGE-B evaluation, affecting the relative ranking of some assemblers. Ensemble assembly with iMetAMOS provides users with multiple, validated assemblies for each genome. Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to select an assembly best tailored to their specific needs.https://doi.org/10.1186/1471-2105-15-12

Recovering complete and draft population genomes from metagenome datasets.

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health

Robust high-throughput prokaryote de novo assembly and improvement pipeline for Illumina data.

The rapidly reducing cost of bacterial genome sequencing has lead to its routine use in large-scale microbial analysis. Though mapping approaches can be used to find differences relative to the reference, many bacteria are subject to constant evolutionary pressures resulting in events such as the loss and gain of mobile genetic elements, horizontal gene transfer through recombination and genomic rearrangements. De novo assembly is the reconstruction of the underlying genome sequence, an essential step to understanding bacterial genome diversity. Here we present a high-throughput bacterial assembly and improvement pipeline that has been used to generate nearly 20 000 annotated draft genome assemblies in public databases. We demonstrate its performance on a public data set of 9404 genomes. We find all the genes used in multi-locus sequence typing schema present in 99.6 % of assembled genomes. When tested on low-, neutral- and high-GC organisms, more than 94 % of genes were present and completely intact. The pipeline has been proven to be scalable and robust with a wide variety of datasets without requiring human intervention. All of the software is available on GitHub under the GNU GPL open source license

Exploring the Diversity of Bacillus Whole Genome Sequencing Projects Using Peasant, the Prokaryotic Assembly and Annotation Tool

The persistent decrease in cost and difficulty of whole genome sequencing of microbial organisms has led to a dramatic increase in the number of species and strains characterized from a wide variety of environments. Microbial genome sequencing can now be conducted by small laboratories and as part of undergraduate curriculum. While sequencing is routine in microbiology, assembly, annotation and downstream analyses still require computational resources and expertise, often necessitating familiarity with programming languages. To address this problem, we have created a light-weight, user-friendly tool for the assembly and annotation of microbial sequencing projects. The Prokaryotic Assembly and Annotation Tool, Peasant, automates the processes of read quality control, genome assembly, and annotation for microbial sequencing projects. High-quality assemblies and annotations can be generated by Peasant without the need of programming expertise or high-performance computing resources. Furthermore, statistics are calculated so that users can evaluate their sequencing project. To illustrate the computational speed and accuracy of Peasant, the SRA records of 322 Illumina platform whole genome sequencing assays for Bacillus species were retrieved from NCBI, assembled and annotated on a single desktop computer. From the assemblies and annotations produced, a comprehensive analysis of the diversity of over 200 high-quality samples was conducted, looking at both the 16S rRNA phylogenetic marker as well as the Bacillus core genome. Peasant provides an intuitive solution for high-quality whole genome sequence assembly and annotation for users with limited programing experience and/or computational resources. The analysis of the Bacillus whole genome sequencing projects exemplifies the utility of this tool. Furthermore, the study conducted here provides insight into the diversity of the species, the largest such comparison conducted to date

Assessment of Next Generation Sequencing Technologies for \u3ci\u3eDe novo\u3c/i\u3e and Hybrid Assemblies of Challenging Bacterial Genomes

In past decade, tremendous progress has been made in DNA sequencing methodologies in terms of throughput, speed, read-lengths, along with a sharp decrease in per base cost. These technologies, commonly referred to as next-generation sequencing (NGS) are complimented by the development of hybrid assembly approaches which can utilize multiple NGS platforms. In the first part of my dissertation I performed systematic evaluations and optimizations of nine de novo and hybrid assembly protocols across four novel microbial genomes. While each had strengths and weaknesses, via optimization using multiple strategies I obtained dramatic improvements in overall assembly size and quality. To select the best assembly, I also proposed the novel rDNA operon validation approach to evaluate assembly accuracy. Additionally, I investigated the ability of third-generation PacBio sequencing platform and achieved automated finishing of Clostridium autoethanogenum without any accessory data. These complete genome sequences facilitated comparisons which revealed rDNA operons as a major limitation for short read technologies, and also enabled comparative and functional genomics analysis. To facilitate future assessment and algorithms developments of NGS technologies we publically released the sequence datasets for C. autoethanogenum which span three generations of sequencing technologies, containing six types of data from four NGS platforms. To assess limitations of NGS technologies, assessment of unassembled regions within Illumina and PacBio assemblies was performed using eight microbial genomes. This analysis confirmed rDNA operons as major breakpoints within Illumina assembly while gaps within PacBio assembly appears to be an unaccounted for event and assembly quality is cumulative effect of read-depth, read-quality, sample DNA quality and presence of phage DNA or mobile genetic elements. In a final collaborative study an enrichment protocol was applied for isolation of live endophytic bacteria from roots of the tree Populus deltoides. This protocol achieved a significant reduction in contaminating plant DNA and enabled use these samples for single-cell genomics analysis for the first time. Whole genome sequencing of selected single-cell genomes was performed, assembly and contamination removal optimized, and followed by the bioinformatics, phylogenetic and comparative genomics analyses to identify unique characteristics of these uncultured microorganisms

Resources for the analysis of bacterial and microbial genomic data with a focus on antibiotic resistance

Antibiotics are drugs which inhibit the growth of bacterial cells. Their discovery was one of the most significant achievements in medicine: it allowed the development of successful treatment options for severe bacterial infections, which has helped to significantly increase our life expectancy. However, bacteria have the ability to adapt to changing environmental conditions through genetic modifications, and can, therefore, become resistant to an antibiotic. Extensive use of antibiotics promotes the development of antibiotic resistance and, since some genetic factors can be exchanged between the cells, emergence of new resistance mechanisms and their spread have become a serious global problem. Counteractive measures have been initiated, focusing on the different factors contributing to the antibiotic resistance crisis. These include the study of bacterial isolates and complete microbial communities using whole-genome sequencing (WGS) data. In both cases, there are specific challenges and requirements for different analytical approaches. The goal of the present thesis was the implementation of multiple resources which should facilitate further microbiological studies, with a focus on bacteria and antibiotic resistance. The main project, GEAR-base, included an analysis of WGS and resistance data of around eleven thousand bacterial clinical isolates covering the main human pathogens and antibiotics from different drug classes. The dataset consisted of WGS data, antibiotic susceptibility profiles and meta-information, along with additional taxonomic characterization of a sample subset. The analysis of this isolate collection allowed for the identification of bacterial species demonstrating increasing resistance rates, to construct species pan-genomes from the de novo assembled genomes, and to link gene presence or absence to the available antibiotic resistance profiles. The generated data and results were made available through the online resource GEAR-base. This resource provides access to the resistance information and genomic data, and implements functionality to compare submitted genes or genomes to the data included in the resource. In microbial community studies, the metagenome obtained through WGS is analyzed to determine its taxonomic composition. For this task, genomic sequences are clustered, or binned, to represent sequences belonging to specific organisms or closely-related organism groups. BusyBee Web was developed to provide an automatic binning pipeline using frequencies of k-mers (subsequences of length k) and bootstrapped supervised clustering. It also includes further data annotation, such as taxonomic classification of the input sequences, presence of know resistance factors, and bin quality. Plasmids, extra-chromosomal DNA molecules found in some bacteria, play an important role in antibiotic resistance spread. As the classification of sequences from WGS data as chromosomal or plasmid-derived is challenging, demonstrated by evaluating four tools implementing three different approaches, having a reference dataset to detect the plasmids which are already known is therefore desirable. To this end, an online resource for complete bacterial plasmids (PLSDB) was implemented. In summary, the herein described online resources represent valuable datasets and/or tools for the analysis of microbial genomic data and, especially, bacterial pathogens and antibiotic resistance.Antibiotika sind Medikamente, die das Wachstum von Bakterienzellen hemmen. Ihre Entdeckung war eine der bedeutendsten Leistungen der Medizin: Es erlaubte die Entwicklung von erfolgreichen Behandlungsmöglichkeiten von schwerwiegenden bakteriellen Infektionen, was geholfen hat, unsere Lebenserwartung zu erhöhen. Allerdings sind Bakterien in der Lage sich den wechselnden Umweltbedingungen anzupassen und können dadurch resistent gegen ein Antibiotikum werden. Der extensive Gebrauch von Antibiotika fördert die Entwicklung von Antibiotikaresistenzen und, da einige genetische Faktoren zwischen den Zellen ausgetauscht werden können, sind das Auftauchen von neuen Resistenzmechanismen und deren Verbreitung zu einem seriösen globalen Problem geworden. Gegenmaßnahmen wurden ergriffen, die sich auf die verschiedenen Faktoren fokussieren, die zur Antibiotikaresistenzkrise beitragen. Diese umfassen Studien von bakteriellen Isolaten und ganzen Mikrobengemeinschaften mithilfe von Gesamt-Genom-Sequenzierung (GGS). In beiden Fällen gibt es spezifische Herausforderungen und Bedürfnisse für verschiedene analytische Methoden. Das Ziel dieser Dissertation war die Implementierung von mehreren Ressourcen, die weitere mikrobielle Studien erleichtern sollen und einen Fokus auf Bakterien und Antibiotikaresistenz haben. Das Hauptprojekt, GEAR-base, beinhaltete eine Analyse von GGS- und Resistenzdaten von ungefähr elftausend klinischen Bakterienisolaten und umfasste die wichtigen menschlichen Pathogene und Antibiotika aus verschiedenen Medikamentenklassen. Neben den GGS-Daten, Empfindlichkeitsprofilen für die Antibiotika und Metainformation, beinhaltete der Datensatz zusätzliche taxonomische Charakterisierung von einer Teilmenge der Proben. Die Analyse dieser Sammlung an Isolaten erlaubte die Identifizierung von Spezies mit ansteigenden Resistenzraten, die Konstruktion von den Spezies-Pan-Genomen aus den de novo assemblierten Genomen und die Verknüpfung vom Vorhandensein oder Fehlen von Genen mit den Antibiotikaresistenzprofilen. Die generierten Daten und Ergebnisse wurden durch die Online-Ressource GEAR-base bereitgestellt. Diese Ressource bietet Zugang zur Resistenzinformation und den gesammelten genomischen Daten und implementiert Funktionen zum Vergleich von hochgeladenen Genen oder Genomen zu den Daten, die in der Ressource enthalten sind. In den Studien von Mikrobengemeinschaften wird das durch GGS erhaltene Metagenom analysiert, um seine taxonomische Zusammensetzung zu bestimmen. Dafür werden die genomischen Sequenzen in sogenannte Bins gruppiert (Binning), die die Zugehörigkeit von den Sequenzen zu bestimmten Organismen oder zu Gruppen von nah verwandten Organismen repräsentieren. BusyBee Web wurde entwickelt, um eine automatische Binning-Pipeline anzubieten, die die Häufigkeitsprofile von k-meren (Teilsequenzen der Länge k) und eine auf dem Bootstrap-Verfahren basierte Methode für die Gruppierung der Sequenzen nutzt. Zusätzlich wird eine Annotation der Daten durchgeführt, wie die taxonomische Klassifizierung der hochgeladenen Sequenzen, das Vorhandensein von bekannten Resistenzfaktoren und die Qualität der Bins. Plasmide, DNA-Moleküle, die zusätzlich zum Chromosom in einigen Bakterien vorhanden sind, spielen eine wichtige Rolle in der Verbreitung von Antibiotikaresistenzen. Die Klassifizierung von Sequenzen aus der GGS als von einem Chromosom oder einem Plasmid stammend ist herausfordernd, wie es in einer Evaluation von vier Tools, die drei verschiedene Ansätze implementieren, demonstriert wurde. Deshalb ist das Vorhandensein von einem Referenzdatensatz, um schon bekannte Plasmide zu detektieren, sehr wünschenswert. Zu diesem Zweck wurde eine Online-Ressource von vollständigen bakteriellen Plasmiden implementiert (PLSDB). Die hier beschriebenen Online-Ressourcen stellen nützliche Datensätze und/oder Werkzeuge dar, die für die Analyse von mikrobiellen genomischen Daten, insbesondere von bakteriellen Pathogenen und Antibiotikaresistenzen, eingesetzt werden können