Is it selfish to be filamentous in biofilms? Individual-based modeling links microbial growth strategies with morphology using the new and modular iDynoMiCS 2.0.

Microbial communities are found in all habitable environments and often occur in assemblages with self-organized spatial structures developing over time. This complexity can only be understood, predicted, and managed by combining experiments with mathematical modeling. Individual-based models are particularly suited if individual heterogeneity, local interactions, and adaptive behavior are of interest. Here we present the completely overhauled software platform, the individual-based Dynamics of Microbial Communities Simulator, iDynoMiCS 2.0, which enables researchers to specify a range of different models without having to program. Key new features and improvements are: (1) Substantially enhanced ease of use (graphical user interface, editor for model specification, unit conversions, data analysis and visualization and more). (2) Increased performance and scalability enabling simulations of up to 10 million agents in 3D biofilms. (3) Kinetics can be specified with any arithmetic function. (4) Agent properties can be assembled from orthogonal modules for pick and mix flexibility. (5) Force-based mechanical interaction framework enabling attractive forces and non-spherical agent morphologies as an alternative to the shoving algorithm. The new iDynoMiCS 2.0 has undergone intensive testing, from unit tests to a suite of increasingly complex numerical tests and the standard Benchmark 3 based on nitrifying biofilms. A second test case was based on the "biofilms promote altruism" study previously implemented in BacSim because competition outcomes are highly sensitive to the developing spatial structures due to positive feedback between cooperative individuals. We extended this case study by adding morphology to find that (i) filamentous bacteria outcompete spherical bacteria regardless of growth strategy and (ii) non-cooperating filaments outcompete cooperating filaments because filaments can escape the stronger competition between themselves. In conclusion, the new substantially improved iDynoMiCS 2.0 joins a growing number of platforms for individual-based modeling of microbial communities with specific advantages and disadvantages that we discuss, giving users a wider choice.

Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.

Anthropogenic environments such as those created by intensive farming of livestock, have been proposed to provide ideal selection pressure for the emergence of antimicrobial-resistant Escherichia coli bacteria and antimicrobial resistance genes (ARGs) and spread to humans. Here, we performed a longitudinal study in a large-scale commercial poultry farm in China, collecting E. coli isolates from both farm and slaughterhouse; targeting animals, carcasses, workers and their households and environment. By using whole-genome phylogenetic analysis and network analysis based on single nucleotide polymorphisms (SNPs), we found highly interrelated non-pathogenic and pathogenic E. coli strains with phylogenetic intermixing, and a high prevalence of shared multidrug resistance profiles amongst livestock, human and environment. Through an original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, we investigated the resistance to 26 different antimicrobials and identified 361 genes associated to antimicrobial resistance (AMR) phenotypes; 58 of these were known AMR-associated genes and 35 were associated to multidrug resistance. We uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments. We also found several human, livestock and environmental isolates sharing closely related mobile genetic elements carrying ARGs across host species and environments. In a scenario where no consensus exists on how antibiotic use in the livestock may affect antibiotic resistance in the human population, our findings provide novel insights into the broader epidemiology of antimicrobial resistance in livestock farming. Moreover, our original data analysis method has the potential to uncover AMR transmission pathways when applied to the study of other pathogens active in other anthropogenic environments characterised by complex interconnections between host species.

The role of multisystemic resilience in fostering critical agency: UK adolescents during the COVID-19 Pandemic.

Critical agency (CA) refers to an individual's feeling of power in relation to social inequalities. Research has demonstrated that high CA is associated with positive adolescent outcomes, however, less is known about what supports are important for its development. Moreover, a large majority of the literature is based on studies from the US and various countries in Africa; although the UK is saturated with inequalities there is little research within a UK context. In this paper we examine (a) the validity of using an existing measure of CA with a sample of UK adolescents and (b) the extent to which resilience supports account for variance in CA. Our analysis identified two distinct factors of CA: justice-oriented and community-oriented. High CA in both factors was explained by resilience supports associated with peer relationships (p < 0.01). Our findings push us towards new relational, ecological ways of understanding adolescent CA. We close by instantiating a translational framework for those devising policies in support of youth resilience and CA. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-023-04578-1.

A Model to Investigate the Impact of Farm Practice on Antimicrobial Resistance in UK Dairy Farms.

The ecological and human health impact of antibiotic use and the related antimicrobial resistance (AMR) in animal husbandry is poorly understood. In many countries, there has been considerable pressure to reduce overall antibiotic use in agriculture or to cease or minimise use of human critical antibiotics. However, a more nuanced approach would consider the differential impact of use of different antibiotic classes; for example, it is not known whether reduced use of bacteriostatic or bacteriolytic classes of antibiotics would be of greater value. We have developed an ordinary differential equation model to investigate the effects of farm practice on the spread and persistence of AMR in the dairy slurry tank environment. We model the chemical fate of bacteriolytic and bacteriostatic antibiotics within the slurry and their effect on a population of bacteria, which are capable of resistance to both types of antibiotic. Through our analysis, we find that changing the rate at which a slurry tank is emptied may delay the proliferation of multidrug-resistant bacteria by up to five years depending on conditions. This finding has implications for farming practice and the policies that influence waste management practices. We also find that, within our model, the development of multidrug resistance is particularly sensitive to the use of bacteriolytic antibiotics, rather than bacteriostatic antibiotics, and this may be cause for controlling the usage of bacteriolytic antibiotics in agriculture.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND: Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base. MAIN TEXT: One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy. CONCLUSIONS: We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Human dissemination of genes and microorganisms in Earth's Critical Zone.

Earth's Critical Zone sustains terrestrial life and consists of the thin planetary surface layer between unaltered rock and the atmospheric boundary. Within this zone, flows of energy and materials are mediated by physical processes and by the actions of diverse organisms. Human activities significantly influence these physical and biological processes, affecting the atmosphere, shallow lithosphere, hydrosphere, and biosphere. The role of organisms includes an additional class of biogeochemical cycling, this being the flow and transformation of genetic information. This is particularly the case for the microorganisms that govern carbon and nitrogen cycling. These biological processes are mediated by the expression of functional genes and their translation into enzymes that catalyze geochemical reactions. Understanding human effects on microbial activity, fitness and distribution is an important component of Critical Zone science, but is highly challenging to investigate across the enormous physical scales of impact ranging from individual organisms to the planet. One arena where this might be tractable is by studying the dynamics and dissemination of genes for antibiotic resistance and the organisms that carry such genes. Here we explore the transport and transformation of microbial genes and cells through Earth's Critical Zone. We do so by examining the origins and rise of antibiotic resistance genes, their subsequent dissemination, and the ongoing colonization of diverse ecosystems by resistant organisms.

Reconstructing promoter activity from Lux bioluminescent reporters.

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens-the source of modern Lux reporters-while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data.

BACKGROUND: Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. RESULTS AND DISCUSSION: Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large 'omics' datasets are increasingly being used in the area of rheumatology. CONCLUSIONS: Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery.

A non-independent energy-based multiple sequence alignment improves prediction of transcription factor binding sites.

MOTIVATION: Multiple sequence alignments (MSAs) are usually scored under the assumption that the sequences being aligned have evolved by common descent. Consequently, the differences between sequences reflect the impact of insertions, deletions and mutations. However, non-coding DNA binding sequences, such as transcription factor binding sites (TFBSs), are frequently not related by common descent, and so the existing alignment scoring methods are not well suited for aligning such sequences. RESULTS: We present a novel multiple MSA methodology that scores TFBS DNA sequences by including the interdependence of neighboring bases. We introduced two variants supported by different underlying null hypotheses, one statistically and the other thermodynamically generated. We assessed the alignments through their performance in TFBS prediction; both methods show considerable improvements when compared with standard MSA algorithms. Moreover, the thermodynamically generated null hypothesis outperforms the statistical one due to improved stability in the base stacking free energy of the alignment. The thermodynamically generated null hypothesis method can be downloaded from http://sourceforge.net/projects/msa-edna/. CONTACT: dov.stekel@nottingham.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AtMYB93 is a novel negative regulator of lateral root development in Arabidopsis.

Plant root system plasticity is critical for survival in changing environmental conditions. One important aspect of root architecture is lateral root development, a complex process regulated by hormone, environmental and protein signalling pathways. Here we show, using molecular genetic approaches, that the MYB transcription factor AtMYB93 is a novel negative regulator of lateral root development in Arabidopsis. We identify AtMYB93 as an interaction partner of the lateral-root-promoting ARABIDILLO proteins. Atmyb93 mutants have faster lateral root developmental progression and enhanced lateral root densities, while AtMYB93-overexpressing lines display the opposite phenotype. AtMYB93 is expressed strongly, specifically and transiently in the endodermal cells overlying early lateral root primordia and is additionally induced by auxin in the basal meristem of the primary root. Furthermore, Atmyb93 mutant lateral root development is insensitive to auxin, indicating that AtMYB93 is required for normal auxin responses during lateral root development. We propose that AtMYB93 is part of a novel auxin-induced negative feedback loop stimulated in a select few endodermal cells early during lateral root development, ensuring that lateral roots only develop when absolutely required. Putative AtMYB93 homologues are detected throughout flowering plants and represent promising targets for manipulating root systems in diverse crop species.

Tracking antimicrobial resistance transmission in urban and rural communities in Bangladesh: a One Health study of genomic diversity of ESBL-producing and carbapenem-resistant Escherichia coli.

Antimicrobial resistance (AMR) poses a significant threat to global health and sustainable development goals, especially in low- and middle-income countries (LMICs). This study aimed to understand the transmission of AMR between poultry, humans, and the environment in Bangladesh using a One Health approach. We analyzed the whole genome sequences (WGS) of 117 extended-spectrum ß-lactamase-producing Escherichia coli (ESBL-Ec) isolates, with 46 being carbapenem resistant. These isolates were obtained from human (n = 20) and poultry feces (n = 12), as well as proximal environments (wastewater) (n = 85) of three different study sites, including rural households (n = 48), rural poultry farms (n = 20), and urban wet markets (n = 49). The WGS of ESBL-Ec isolates were compared with 58 clinical isolates from global databases. No significant differences in antibiotic resistance genes (ARGs) were observed in ESBL-Ec isolated from humans with and without exposure to poultry. Environmental isolates showed higher ARG diversity than human and poultry isolates. No clonal transmission between poultry and human isolates was found, but wastewater was a reservoir for ESBL-Ec for both. Except for one human isolate, all ESBL-Ec isolates were distinct from clinical isolates. Most isolates (77.8%) carried at least one plasmid replicon type, with IncFII being the most prevalent. IncFIA was predominant in human isolates, while IncFII, Col(MG828), and p0111 were common in poultry. We observed putative sharing of ARG-carrying plasmids among isolates, mainly from wastewater. However, in most cases, bacterial isolates sharing plasmids were also clonally related, suggesting clonal spread was more probable than just plasmid transfer. IMPORTANCE: Our study underscores that wastewater discharged from households and wet markets carries antibiotic-resistant organisms from both human and animal sources. Thus, direct disposal of wastewater into the environment not only threatens human health but also endangers food safety by facilitating the spread of antimicrobial resistance (AMR) to surface water, crops, vegetables, and subsequently to food-producing animals. In regions with intensive poultry production heavily reliant on the prophylactic use of antibiotics, compounded by inadequate waste management systems, such as Bangladesh, the ramifications are particularly pronounced. Wastewater serves as a pivotal juncture for the dissemination of antibiotic-resistant organisms and functions as a pathway through which strains of human and animal origin can infiltrate the environment and potentially colonize new hosts. Further research is needed to thoroughly characterize wastewater isolates/populations and understand their potential impact on interconnected environments, communities, and wildlife.

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance.

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.

The long and short of it: benchmarking viromics using Illumina, Nanopore and PacBio sequencing technologies.

Viral metagenomics has fuelled a rapid change in our understanding of global viral diversity and ecology. Long-read sequencing and hybrid assembly approaches that combine long- and short-read technologies are now being widely implemented in bacterial genomics and metagenomics. However, the use of long-read sequencing to investigate viral communities is still in its infancy. While Nanopore and PacBio technologies have been applied to viral metagenomics, it is not known to what extent different technologies will impact the reconstruction of the viral community. Thus, we constructed a mock bacteriophage community of previously sequenced phage genomes and sequenced them using Illumina, Nanopore and PacBio sequencing technologies and tested a number of different assembly approaches. When using a single sequencing technology, Illumina assemblies were the best at recovering phage genomes. Nanopore- and PacBio-only assemblies performed poorly in comparison to Illumina in both genome recovery and error rates, which both varied with the assembler used. The best Nanopore assembly had errors that manifested as SNPs and INDELs at frequencies 41 and 157 % higher than found in Illumina only assemblies, respectively. While the best PacBio assemblies had SNPs at frequencies 12 and 78 % higher than found in Illumina-only assemblies, respectively. Despite high-read coverage, long-read-only assemblies recovered a maximum of one complete genome from any assembly, unless reads were down-sampled prior to assembly. Overall the best approach was assembly by a combination of Illumina and Nanopore reads, which reduced error rates to levels comparable with short-read-only assemblies. When using a single technology, Illumina only was the best approach. The differences in genome recovery and error rates between technology and assembler had downstream impacts on gene prediction, viral prediction, and subsequent estimates of diversity within a sample. These findings will provide a starting point for others in the choice of reads and assembly algorithms for the analysis of viromes.

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms.

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.

A model of antibiotic resistance genes accumulation through lifetime exposure from food intake and antibiotic treatment.

Antimicrobial resistant bacterial infections represent one of the most serious contemporary global healthcare crises. Acquisition and spread of resistant infections can occur through community, hospitals, food, water or endogenous bacteria. Global efforts to reduce resistance have typically focussed on antibiotic use, hygiene and sanitation and drug discovery. However, resistance in endogenous infections, e.g. many urinary tract infections, can result from life-long acquisition and persistence of resistance genes in commensal microbial flora of individual patients, which is not normally considered. Here, using individual based Monte Carlo models calibrated using antibiotic use data and human gut resistomes, we show that the long-term increase in resistance in human gut microbiomes can be substantially lowered by reducing exposure to resistance genes found food and water, alongside reduced medical antibiotic use. Reduced dietary exposure is especially important during patient antibiotic treatment because of increased selection for resistance gene retention; inappropriate use of antibiotics can be directly harmful to the patient being treated for the same reason. We conclude that a holistic approach to antimicrobial resistance that additionally incorporates food production and dietary considerations will be more effective in reducing resistant infections than a purely medical-based approach.

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics.

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

Multidrug-Resistant ESBL-Producing E. coli in Clinical Samples from the UK.

Globally, cephalosporin therapy failure is a serious problem for infection control. One causative agent of cephalosporin-resistant infections is multidrug-resistant (MDR) E. coli producing extended-spectrum ß-lactamases (ESBLs) and/or plasmid-encoded AmpC (pAmpC) ß-lactamases. We evaluated the occurrence of ESBL/pAmpC genetic determinants in phenotypically MDR E. coli isolated from clinical samples of blood, faeces, ear effusion, urine and sputum from a UK hospital. Phenotypic resistance profiling for 18 antibiotics (from seven classes) showed that 32/35 isolates were MDR, with resistance to 4-16 of the tested antibiotics. Of the isolates, 97.1% showed resistance to ampicillin, 71.4% showed resistance to co-amoxiclav, cefotaxime, ceftazidime and ceftiofur, and 68.5% showed resistance to cefquinome. blaCTX-M, blaTEM and blaOXA-1 genes were detected in 23, 13 and 12 strains, respectively, and Intl1 was detected in 17 isolates. The most common subtypes among the definite sequence types were CTX-M-15 (40%) and TEM-1 (75%). No E. coli isolates carried pAmpC genes. Significant correlations were seen between CTX-M carriage and cefotaxime, ceftiofur, aztreonam, ceftazidime and cefquinome resistance; between blaCTX-M, blaTEM and blaOXA-1 carriage and ciprofloxacin resistance; and between Intl1 carriage and trimethoprim/sulfamethoxazole resistance. Thus, MDR phenotypes may be conferred by a relatively small number of genes. The level and pattern of antibiotic resistance highlight the need for better antibiotic therapy guidelines, including reduced use and improved surveillance.

Impact of contrasting poultry exposures on human, poultry, and wastewater antibiotic resistomes in Bangladesh.

IMPORTANCE: Through the use of DNA sequencing, our study shows that there is no significant difference in the antibiotic resistance genes found in stool samples taken from individuals with high exposure to poultry routinely fed antibiotics and those without such exposure. This finding is significant as it suggests limited transmission of antibiotic resistance genes between poultry and humans in these circumstances. However, our research also demonstrates that commercially reared poultry are more likely to possess resistance genes to antibiotics commonly administered on medium-sized farms. Additionally, our study highlights the under-explored potential of wastewater as a source of various antibiotic resistance genes, some of which are clinically relevant.

Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction.

Prediction of transcription factor binding sites is an important challenge in genome analysis. The advent of next generation genome sequencing technologies makes the development of effective computational approaches particularly imperative. We have developed a novel training-based methodology intended for prokaryotic transcription factor binding site prediction. Our methodology extends existing models by taking into account base interdependencies between neighbouring positions using conditional probabilities and includes genomic background weighting. This has been tested against other existing and novel methodologies including position-specific weight matrices, first-order Hidden Markov Models and joint probability models. We have also tested the use of gapped and ungapped alignments and the inclusion or exclusion of background weighting. We show that our best method enhances binding site prediction for all of the 22 Escherichia coli transcription factors with at least 20 known binding sites, with many showing substantial improvements. We highlight the advantage of using block alignments of binding sites over gapped alignments to capture neighbouring position interdependencies. We also show that combining these methods with ChIP-on-chip data has the potential to further improve binding site prediction. Finally we have developed the ungapped likelihood under positional background platform: a user friendly website that gives access to the prediction method devised in this work.