Statistical bioinformatic methods in microbial genome analysis.

7 - Analysis of Microbial Genomic Macrorestriction Patterns by Pulsed-Field Gel Electrophoresis (PFGE) Typing

B. licheniformis DSM13 is a Gram-positive, ubiquitous, endospore forming bacterium that is extensively used for industrial production of exoenzymes as it can secrete high amounts of protein. The genome was sequenced in 2004 at the Goettingen Genomics Laboratory. Since the sequencing of the model organism B. subtilis 168 in 1997 and its resequencing and -annotation in 2009, numerous other representatives of the species Bacillus have been sequenced. The aim of the present dissertation is a detailed genomic understanding of B. licheniformis DSM13 and the generation of predictions about which parts of the sequence are essential and non-essential for the enormous production of proteases by bioinformatic comparison of protein sequences. Therefore, the focus lies on the analysis of the B. licheniformis DSM13 competence system since this strain s genetic transformation is difficult compared to the laboratory-optimised B. subtilis 168. A softwaretool for the comparative whole genome analysis was implemented, which automatically identifies shared proteins in different organisms. Local bidirectional best hits for the orthologues identification, the evaluation with a global alignment algorithm and the adequate output for further analyses make the pipeline a widespread tool in microbial genome analysis. It was used in a broad comparative analysis of B. licheniformis DSM13. The combination of in silico predictions and experimental micro-array analyses generated deletion targets for further biological analysis. The present work provides a contribution to the design of an optimal genome of B. licheniformis DSM13.

Genome analysis of microbial pathogens has provided unique insights into their virulence, host adaptation and evolution. Common themes have emerged, including lateral gene transfer among enteric pathogens, genome decay among obligate intracellular pathogens and antigenic variation among mucosal pathogens. The advent of post-genomic approaches and the sequencing of the human genome will enable scientists to investigate the complex and dynamic interplay between host and pathogen. This wealth of information will catalyse the development of new intervention strategies to reduce the burden of microbial-related disease.

For the past 20 years, the Clusters of Orthologous Genes (COG) database had been a popular tool for microbial genome annotation and comparative genomics. Initially created for the purpose of evolutionary classification of protein families, the COG have been used, apart from straightforward functional annotation of sequenced genomes, for such tasks as (i) unification of genome annotation in groups of related organisms; (ii) identification of missing and/or undetected genes in complete microbial genomes; (iii) analysis of genomic neighborhoods, in many cases allowing prediction of novel functional systems; (iv) analysis of metabolic pathways and prediction of alternative forms of enzymes; (v) comparison of organisms by COG functional categories; and (vi) prioritization of targets for structural and functional characterization. Here we review the principles of the COG approach and discuss its key advantages and drawbacks in microbial genome analysis.

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.

With the rapid development of genomic sequencing technologies, there is an increasing demand for efficient and accurate sequence analysis methods. However, existing methods face challenges in handling long, variable-length sequences and large-scale datasets. To address these issues, we propose a novel encoding method—Energy Entropy Vector (EEV). This method encodes gene sequences of arbitrary length into fixed-dimensional vector representations by modeling nucleotide energy characteristics based on information entropy. Experiments conducted on five microbial datasets demonstrate that, compared to traditional alignment-free methods, EEV achieves higher accuracy in convex hull classification and species classification tasks, with improvements of 15% to 30% in family-level classification. In phylogenetic tree construction, EEV significantly accelerates the process relative to multiple sequence alignment methods while maintaining high tree quality, enabling rapid and accurate phylogenetic reconstruction. Moreover, EEV supports flexible dimensional expansion by superimposing nucleotide energies, enhancing its ability to represent complex genomic sequences while effectively alleviating sparsity issues in high-dimensional representations. This study provides an efficient gene encoding strategy for large-scale genomic analysis and evolutionary research.

Under this award, numerous genome sequences were generated, analyzed, published and made publicly available.

Neanderthals and anatomically modern humans made the Middle-Upper Paleolithic technological transition together. Perhaps more accurately stated, the technological innovations reflected in Early Upper Paleolithic archaeological assemblages were developed, adopted, and spread by a western Eurasian metapopulation that encompassed variable admixture histories. This is the unavoidable implication of robust analyses of ancient human, omnivorous prey, and microbial genomes, which document long-term—if sporadic—interaction and successful family formation between geographically expanding anatomically modern humans and indigenous Neanderthals. This social and population interaction occurred within a broad time-frame, likely ca. 120–40 ka, involving complex, multi-scalar niche construction and biocultural evolutionary dynamics. This chapter reconsiders theoretical, methodological, and empirical issues surrounding the study of lithic assemblages that define the Middle-Upper Paleolithic transition, considering how we can better answer a key question. If the Middle-Upper Paleolithic transition was an indirect consequence of anatomically modern human dispersal and interaction with Neanderthals, then what, if anything, did technological change have to do with Neanderthal extinction, ca. 40 ka?

Frontiers in molecular mycorrhizal research – genes, loci, dots and spins

How gene function evolves is a central question of evolutionary biology. It can be investigated by comparing functional genomics results between species and between genes. Most comparative studies of functional genomics have used pairwise comparisons. Yet it has been shown that this can provide biased results, as genes, like species, are phylogenetically related. Phylogenetic comparative methods should be used to correct for this, but they depend on strong assumptions, including unbiased tree estimates relative to the hypothesis being tested. Such methods have recently been used to test the “ortholog conjecture,” the hypothesis that functional evolution is faster in paralogs than in orthologs. Although pairwise comparisons of tissue specificity () provided support for the ortholog conjecture, phylogenetic independent contrasts did not. Our reanalysis on the same gene trees identified problems with the time calibration of duplication nodes. We find that the gene trees used suffer from important biases, due to the inclusion of trees with no duplication nodes, to the relative age of speciations and duplications, to systematic differences in branch lengths, and to non-Brownian motion of tissue specificity on many trees. We find that incorrect implementation of phylogenetic method in empirical gene trees with duplications can be problematic. Controlling for biases allows successful use of phylogenetic methods to study the evolution of gene function and provides some support for the ortholog conjecture using three different phylogenetic approaches.

Mol Syst Biol. 1: 2005.0030 Large potential benefits for systems biology reside in applications to human health and identity. To develop our community's skills in these directions, ready access to highly integrated and comprehensive human genome and phenome data sets is extremely important and increasingly feasible technically. The few human 'functional genomics' data sets available today tend to be isolated from one another. Some of the tools needed to break through this impasse are addressed below in the context of a Personal Genome Project (PGP) as a natural successor to the Human Genome Project (HGP)—two recent buds in the ancient field of genetics. From my first interaction with Wally Gilbert in 1976, it seemed that a large (but appealing) leap would be to go from his new method for sequencing 30 bp segments to a method to get everyone's full genome sequenced. Six billion base pairs for six billion people had a nice ring to it. This was still merely a fantasy when we published a paper called 'Genomic Sequencing' in 1984 (Church and Gilbert, 1984) and conspired to create a 3 billion dollar HGP later that year (Cook‐Deegan, 1989). For the subsequent 16 years, radical technology development (while kept alive in a few 'back‐rooms') was clearly a minor funding priority relative to 'production' sequencing. However, by 2001, the criticisms of the old technology grew and the call for affordable personal genomes became irresistible (Jonietz, 2001). In early 2004, the NIH‐NHGRI posted a request for applications, and in October 2004 and August 2005, announced grant awards totaling $70 million for technology leading to human genome sequences for $100 000 in 5 years and $1000 in 10 years (http://www.nih.gov/news/pr/aug2005/nhgri‐08.htm). As if the motivation were not already high enough, at the recent Genome Sequencing & Analysis Conference in Hilton Head (October …

Textual analysis of typical microbial genomes reveals that they have the statistical characteristics of a DNA sequence of a much shorter length. This peculiar property supports an evolutionary model in which a genome evolves by random mutation but primarily grows by random segmental duplication. That genomes grew mostly by duplication is consistent with the observation that repeat sequences in all genomes are widespread and intragenomic and intergenomic homologous genes are preponderant across all life forms.

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

Background: No predictive biomarkers have been identified for VEGFR- and mTOR-inhibitors treatments, the 2 standards of care in mRCC. The PREDICT project (Personalised RNA Interference to Enhance the Delivery of Individualised Cytotoxic and Targeted Therapeutics) is a EU-funded research program built on the Consortium of Academic Centers, Universities, Research Institutions, SME & industrial members. Two biomarker discovery clinical trials using Everolimus (E-PREDICT) and Sunitinib (S-PREDICT) treatment in patients with mRCC have been developped and are recruiting.The 3 cornerstones of the PREDICT approach are: (i) to acquire paired pre-treatment biopsies and on-treatment nephrectomy (NE) specimens for molecular analyses and integration with clinical efficacy data; (ii) to identify genes regulating drug resistance in ex vivo RCC cultures from trial patients by a novel personalized functional genomics screening approach; (iii) to integrate functional data with genome wide molecular analyses of pre- and on-treatment samples in order to identify clinically relevant mechanisms of drug resistance and the next generation of predictive biomarkers. Methods: International multicentre single arm phase II trials of EV or SU administered peri-NE in patients with untreated mRCC are ongoing. Fresh tissue is collected in a quality-controlled setting by core biopsy of primary tumours before treatment. Treatment (EV or SU) is administered for a 2 cycle period of time (EV: 10 mg/d po for 6 wks; SU: 2 cycles of 50 mg/d po for 4 wks followed by 2 wks off drug) followed by NE. On NE specimen, fresh tissue is collected with similar standardized tissue collection and processing protocols for both genomic analysis and ex-vivo expansion in order to develop RCC cell culture from any NE as well as mice-xenografts. Ex vivo cultures from NE specimens will be used for personalized functional genomic RNA interference screening. Patient resumes treatment after wound is healed until progression. Functional imaging (perfusion CT) as clinical data are collected. The primary clinical endpoint is the safety of peri-NE E/S. Secondary endpoints include efficacy and biomarker measurements. Sample size: 60 pts in both biomarker discovery and validation cohorts per trial will allow the identification and validation of several potential predictive biomarkers per drug with an expected AUC of >0.8 while controlling the false positive rate < 5%. Results: To date 33 patients have been enrolled in the S- PREDICT Trial. Preliminary analysis of 25 first paired pre- treatment biopsies and frozen nephrectomy specimen provided sufficient and quality controlled material for genomic analysis. Three RCC cultures have been tested for functional genomic RNA interference screening. Planned genomic analyses and functional genomic screens will be further discussed. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1746. doi:1538-7445.AM2012-1746