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Creevey, C. J.

Publications and source records attributed to Creevey, C. J..

At least 19 recordsLinked to original sources

Ptolemaea: consensus, comprehensive annotation of antiviral defence systems in bacterial genomes

MotivationBacteria carry a large repertoire of antiviral defence systems, our knowledge of which is expanding rapidly. Several bioinformatics tools now exist to identify them. Though powerful, these tools can differ in the models they use and the nomenclature they return, thus a single tool could both miss an annotation and disagree with its peers. ResultsHere we describe Ptolemaea, a pipeline for harmonising phage-defence annotations across multiple tools by reconciling PADLOC, DefenseFinder, and a bidirectional BLAST. Over a common predicted set of proteins, Ptolemaea provides a consensus annotation list per genome. The pipeline is not intended to outperform or replace its component tools; its purpose is to maximise the number of defence systems recovered from a genome and to make disagreements between tools explicit and resolvable. We demonstrate the pipeline on 700 complete genomes spanning the ESKAPE pathogens and Escherichia coli, recovering 32,509 defence annotations, of which 50.6% were supported by more than one annotation source. AvailabilityThe Ptolemaea pipeline is freely available at https://github.com/ecampbell50/Ptolemaea. Supplementary informationGenome accessions used in this analysis can be found in S1, and script for genome retrieval in S2. Collated consensus annotation counts can be found in S3, while all raw tool outputs and curated decisions for each species can be found in S4-10.

bioinformatics↗

Development and Refinement of Microbial DNA Extraction Protocol from Bovine Milk

Milk is a challenging matrix to extract sufficient microbial DNA from for downstream analysis. This study assessed fourteen DNA extraction protocols for their DNA outputs. An adaptation of the QIAGEN DNeasy PowerSoil kit, which increased initial sample volume and maintained all volume of lysate following bead beating proved most effective.

molecular biology↗

Dissecting Phylogenetic Support: Unified Decay Indices, AU Tests, and Branch-Site Specific Visualizations.

Phylogenetic inference relies on robust measures of branch support to assess the reliability of evolutionary relationships. While bootstrap resampling and Bayesian probabilities have become the predominant support metrics in molecular phylogenetics, decay indices (also known as Bremer support) provide an alternative perspective by quantifying the amount of evidence required to collapse specific clades. We present panDecay, a unified software framework that extends the classical parsimony-based decay index concept to maximum likelihood (ML) and Bayesian inference approaches. panDecay automates the calculation of ML-based decay indices using log-likelihood differences, performs Approximately Unbiased (AU) statistical tests, and computes Bayesian decay indices through marginal likelihood comparisons using stepping-stone sampling. The software integrates with established phylogenetic tools (PAUP* and MrBayes) and provides comprehensive output including site-specific support analysis, multiple annotated tree formats, and detailed statistical reports. panDecay supports diverse data types (DNA, protein, and discrete morphological characters), offers flexible model specification, and provides parallel processing capabilities for computationally intensive Bayesian analyses. By unifying decay index calculations across parsimony, likelihood, and Bayesian frameworks within a single command-line interface, panDecay enables researchers to obtain complementary support metrics that provide deeper insights into phylogenetic signal strength and distribution across sequence alignments. The software is implemented in Python 3.8+, distributed via PyPI, and released under the MIT license.

evolutionary biology↗

Whole-Genome Sequencing Uncovers Chromosomal and Plasmid-Borne Multidrug Resistance and Virulence Genes in Poultry-Associated Escherichia coli from Nigeria

BackgroundUnregulated antibiotic use in poultry farming drives the emergence of multidrug-resistant (MDR) bacteria, which can spread to humans through the food chain and environment, posing serious public health risks. Whole-genome sequencing (WGS), combined with antimicrobial susceptibility testing (AST), enables detailed characterization of resistance mechanisms and supports antimicrobial stewardship. This study investigated the phenotypic and genotypic antimicrobial resistance (AMR), plasmid content, and virulence factors of an MDR Escherichia coli strain isolated from chicken droppings in Enugu State, Nigeria. ResultsDisk diffusion AST showed resistance to six of seven antibiotics tested (cefotaxime, ampicillin, erythromycin, gentamicin, ciprofloxacin, and doxycycline). Broth microdilution confirmed elevated minimum inhibitory concentrations across multiple classes, indicating an MDR phenotype. Hybrid WGS (Illumina and Nanopore) produced a 5.33 Mb genome comprising one chromosome and four plasmid-associated contigs. Chromosomal antimicrobial resistance genes (ARGs), including aac(6)-Ib-cr, blaCTX-M-15, and blaOXA-1, conferred resistance to aminoglycosides, fluoroquinolones, cephalosporins, and penicillins. Additional resistance determinants included efflux pumps, transport-associated genes, regulatory elements, and membrane modification genes. Plasmid-borne ARGs conferring resistance to aminoglycosides, trimethoprim, macrolides, sulfonamides, penicillins, and tetracyclines were also identified. The presence of Col156 and IncF-type plasmids indicates strong potential for horizontal gene transfer. Virulence profiling revealed numerous chromosomally encoded factors related to adhesion, iron acquisition, and toxin production. These included the pap gene cluster encoding P fimbriae; adhesion-associated genes (yagW/ecpD, ykgK/ecpR); multiple iron acquisition systems (enterobactin, yersiniabactin, aerobactin, and heme uptake); and sat, which encodes an autotransporter toxin. Additionally, the plasmid-borne gene senB encodes an enterotoxin that induces intestinal fluid secretion and contributes to diarrheal disease. Multilocus sequence typing identified the strain as ST131, a globally disseminated high-risk lineage. ConclusionsThis study provides a comprehensive genomic characterization of an MDR E. coli strain from poultry, revealing multiple chromosomal and plasmid-borne ARGs, and a diverse virulence gene repertoire. The detection of ST131 in poultry waste highlights a complex public health issue involving zoonotic transmission, veterinary impact, and environmental spread of AMR. These findings underscore the need for prudent antibiotic use, continuous monitoring, and integrated genomic surveillance across agricultural and environmental sectors, alongside improved antimicrobial stewardship and strengthened farm biosecurity measures.

microbiology↗

Genomic constraints shape the evolution of alternative routes to drug resistance in prokaryotes.

Antimicrobial resistance (AMR) is often modelled as the accumulation of resistance genes leading to multidrug resistance (MDR). We show that gene co-occurrence patterns in two opportunistic pathogens are consistent with fitness trade-offs that constrain which combinations of resistance mechanisms coexist. We applied a combined pangenomic and machine-learning analysis to 9,584 Escherichia coli genomes (99.2% phylogroup B2) and 7,057 Pseudomonas aeruginosa genomes. In E. coli, we identified eight cases of mutually exclusive gene pairs that independently predicted the same MDR phenotype, suggesting alternative routes to resistance whose components are typically not co-inherited. In a separate dataset of 352 strains with paired minimum inhibitory concentration (MIC) data, these dissociated combinations co-occurred more often in resistant than susceptible strains, consistent with the constraints being conditional on antibiotic selection. 33 gene pairs showed opposing association patterns between the two species, with combinations significantly associated in one species and significantly dissociated in the other (e.g. associated in E. coli and dissociated in P. aeruginosa, or vice versa). This indicates that genomic context modifies the contribution of individual genes to resistance phenotypes, and offers one explanation for the observation that 106 ARGs are present in >95% of strains yet do not predict resistance phenotype on their own. The findings are consistent with resistance evolution being shaped by fitness trade-offs and suggest that the dissociation patterns we identify could be targets for follow-up experimental work on resistance-associated fitness costs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/671315v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@7a717corg.highwire.dtl.DTLVardef@b08e64org.highwire.dtl.DTLVardef@154820forg.highwire.dtl.DTLVardef@126b9ee_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPACT STATEMENTMultidrug resistance threatens global health; however, resistance acquisition remains poorly understood. We show that genomes cannot simply accumulate all available resistance mechanisms. Instead, species-specific grammatical rules render certain gene combinations typically incompatible. Analysing 9,584 Escherichia coli genomes, we identify eight mutually exclusive routes to resistance, demonstrating that resistance is more constrained than previously thought. Strikingly, 33 gene pairs that cooperate in E. coli actively disassociate in Pseudomonas aeruginosa, revealing that genomic context determines resistance outcomes. This explains why 106 resistance genes present in all strains fail to confer universal resistance, challenging current diagnostics. Our findings transform our understanding of resistance evolution from an unlimited accumulation model to a grammatically constrained pathways model, presenting new therapeutic strategies that exploit evolutionary dead ends.

bioinformatics↗

Insights into Plant Interactions and the Biogeochemical Role of the Globally Widespread Acidobacteriota Phylum

The prevalence and abundance of Acidobacteriota raise concerns about their ecological function and metabolic activity in the environment. Studies have reported the potential of some members of Acidobacteriota to interact with plants and play a significant role in biogeochemical cycles. However, their role in this context has not been extensively studied. Here, we performed a comprehensive genomic analysis of 758 metagenome-assembled genomes (MAGs) and 121 RefSeq genomes of Acidobacteriota. Our analysis revealed a high frequency of plant growth-promoting traits (PGPTs) genes in the Acidobacteriaceae, Bryobacteraceae, Koribacteraceae, and Pyrinomonadaceae families. These PGPTs include genes involved in nitrogen fixation, phosphorus solubilization, exopolysaccharide production, siderophore production, and plant growth hormone production. Expression of such genes was found to be transcriptionally active in different environments. In addition, we identified numerous carbohydrate-active enzymes and peptidases involved in plant polymer degradation. By applying an in-depth insight into the diversity of the phylum, we expanded previous role in carbon, nitrogen, sulfur, and trace metal cycling. This study underscores the distinct potential ecological roles of each of these taxonomic groups, providing valuable insights for future research.

genomics↗

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystems function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

systems biology↗

Accessory genes define species-specific pathways to antibiotic resistance.

BackgroundThe rise of antimicrobial resistance (AMR) is a growing concern globally and a deeper understanding of AMR gene carriage vs usage is vital for future responses to reduce the spread of AMR. Identification of AMR phenotype by laboratory-based assays are often hindered by difficulties in establishing cultures. This issue could be resolved by rapid computational assessment of an organisms genome, however, AMR gene finder tools are not intended to infer AMR phenotype which is likely to be a product of multiple gene interactions. MethodsTo understand the importance of multi-gene interactions to the relationship between AMR genotype and AMR phenotype, we applied machine learning approaches to 16,950 genomes from microbial isolates representing 28 different genera with 1.2 million corresponding laboratory-determined MICs for 23 different antibiotics. We then elucidated the genomic paths to phenotypic antimicrobial resistance with the aim of allowing for the development of rapid determination of AMR phenotype from genomes or even whole microbiomes. ResultsThe application of machine learning models resulted in a >1.5-fold increase in average prediction accuracy of AMR phenotype across the 23 antibiotic models. Interpretation of these models revealed 528 distinct genomic pathways to phenotypic resistance, many of which were species-specific and involved genes which have not previously been associated with AMR phenotype. This is the first study to demonstrate the utility of machine learning models in the prediction of AMR phenotype for a wide range of clinically relevant organisms and antibiotics. This could be applied as a rapid and affordable alternative to culture-based techniques, estimating taxonomy in addition to AMR phenotype, and providing real-time monitoring of multi-drug resistant pathogens. Availability and implementationO_ST_ABSContactC_ST_ABSldillon05@qub.ac.uk View supplementary information at this linkhttps://osf.io/cj4bq/?view_only=c0ee87b7609543b688953089be4c376f See Code Availability for scripts used.

bioinformatics↗

Bursts of novel composite gene families at major nodes in animal evolution

A molecular level perspective on how novel phenotypes evolve is contingent on our understanding of how genomes evolve through time, and of particular interest is how novel elements emerge or are lost. Mechanisms of protein evolution such as gene duplication have been well established. Studies of gene fusion events show they often generate novel functions and adaptive benefits. Identifying gene fusion and fission events on a genome scale allows us to establish the mode and tempo of emergence of composite genes across the animal tree of life, and allows us to test the repeatability of evolution in terms of determining how often composite genes can arise independently. Here we show that [~]5% of all animal gene families are composite, and their phylogenetic distribution suggests an abrupt, rather than gradual, emergence during animal evolution. We find that gene fusion occurs at a higher rate than fission (73.3% vs 25.4%) in animal composite genes, but many gene fusions (79% of the 73.3%) have more complex patterns including subsequent fission or loss. We demonstrate that nodes such as Bilateria, Euteleostomi, and Eutheria, have significantly higher rates of accumulation of composite genes. We observe that in general deuterostomes have a greater amount of composite genes as compared to protostomes. Intriguingly, up to 41% of composite gene families have evolved independently in different clades showing that the same solutions to protein innovation have evolved time and again in animals. Significance statementNew genes emerge and are lost from genomes over time. Mechanisms that can produce new genes include, but are not limited to, gene duplication, retrotransposition, de novo gene genesis, and gene fusion/fission. In this work, we show that new genes formed by fusing distinct homologous gene families together comprise a significant portion of the animal proteome. Their pattern of emergence through time is not gradual throughout the animal phylogeny - it is intensified on nodes of major transition in animal phylogeny. Interestingly, we see that evolution replays the tape frequently in these genes with 41% of gene fusion/fission events occurring independently throughout animal evolution.

evolutionary biology↗

FrameRate: learning the coding potential of unassembled metagenomic reads

MotivationMetagenomic assembly is a slow and computationally intensive process and despite needing iterative rounds for improvement and completeness the resulting assembly often fails to incorporate many of the input sequencing reads. This is further complicated when there is reduced read-depth and/or artefacts which result in chimeric assemblies both of which are especially prominent in the assembly of metagenomic datasets. Many of these limitations could potentially be overcome by exploiting the information content stored in the reads directly and thus eliminating the need for assembly in a number of situations. ResultsWe explored the prediction of coding potential of DNA reads by training a machine learning model on existing protein sequences. Named FrameRate, this model can predict the coding frame(s) from unassembled DNA sequencing reads directly, thus greatly reducing the computational resources required for genome assembly and similarity-based inference to pre-computed databases. Using the eggNOG-mapper function annotation tool, the predicted coding frames from FrameRate were functionally verified by comparing to the results from full-length protein sequences reconstructed with an established metagenome assembly and gene prediction pipeline from the same metagenomic sample. FrameRate captured equivalent functional profiles from the coding frames while reducing the required storage and time resources significantly. FrameRate was also able to annotate reads that were not represented in the assembly, capturing this missing information. As an ultra-fast read-level assembly-free coding profiler, FrameRate enables rapid characterisation of almost every sequencing read directly, whether it can be assembled or not, and thus circumvent many of the problems caused by contemporary assembly workflows. Availabilityhttps://github.com/NickJD/FrameRate Contactliuwei.wang@fu-berlin.de and nicholas@dimonaco.co.uk

bioinformatics↗

StORF-Reporter: Finding Genes between Genes

Large regions of prokaryotic genomes are currently without any annotation, in part due to well-established limitations of annotation tools. For example, it is routine for annotation tools to misreport or completely omit genes using alternative start codons. Therefore, we present StORF-Reporter, a tool that takes an annotated genome and returns missing CDS genes from unannotated regions. StORF-Reporter consists of two parts. The first begins with the extraction of unannotated regions from an annotated genome. Next, Stop-ORFs (StORFs) are identified in these unannotated regions. StORFs are open reading frames that are delimited by stop codons and thus can capture those genes most often missing in genome annotations. We show that this methodology recovers genes missing from canonical genome annotations. We inspected the results of the genomes of model organisms, the pangenome of Escherichia coli, and a further 6,223 prokaryotic genomes of 179 genera from the Ensembl Bacteria database. StORF-Reporter was able to extend the core, soft-core and accessory gene-collections, identify novel gene families and extend families into additional genera. The high levels of sequence conservation observed between genera suggest that many of these StORF sequences are likely to be functional genes that must now be added to the canonical annotations.

bioinformatics↗

Enriching for orthologs increases support for Xenacoelomorpha and Ambulacraria sister relationship

Conflicting studies place a group of bilaterian invertebrates containing xenoturbellids and acoelomorphs, the Xenacoelomorpha, as either the primary emerging bilaterian phylum, or within Deuterostomia, sister to Ambulacraria. While their placement as sister to the rest of Bilateria supports relatively simple morphology in the ancestral bilaterian, their alternative placement within Deuterostomia suggests a morphologically complex ancestral Bilaterian along with extensive loss of major phenotypic traits in the Xenacoelomorpha. More recently, further studies have brought into question whether Deuterostomia should be considered monophyletic at all. Hidden paralogy presents a major challenge for reconstructing species phylogenies. Here we assess whether hidden paralogy has contributed to the conflict over the placement of Xenacoelomorpha. Our approach assesses previously published datasets, enriching for orthogroups whose gene trees support well resolved clans elsewhere in the animal tree of life. We find that the majority of constituent genes in previously published datasets violate incontestable clans, suggesting that hidden paralogy is rife at this depth. We demonstrate that enrichment for genes with orthologous signal alters the final topology that is inferred, whilst simultaneously improving fit of the model to the data. We discover increased, but ultimately not conclusive, support for the existence of Xenambulacraria in our orthology enriched set of genes. At a time when we are steadily progressing towards sequencing all of life on the planet, we argue that long-standing contentious issues in the tree of life will be resolved using smaller amounts of better quality data that can be modelled adequately.

evolutionary biology↗

Beehives possess their own distinct microbiomes

Honey bees use plant material to manufacture their own food. These insect pollinators visit flowers repeatedly to collect nectar and pollen, which are shared with other hive bees to produce honey and beebread. While producing these products, beehives accumulate a tremendous amount of microbes, including bacteria that derive from plants and different parts of the honey bees body. In this study, we conducted 16S rDNA metataxonomic analysis on honey and beebread samples that were collected from 15 beehives in the southeast of England in order to quantify the bacteria associated with beehives. The results highlighted that honeybee products carry a significant variety of bacterial groups that comprise bee commensals, environmental bacteria and pathogens of plants and animals. Remarkably, this bacterial diversity differs amongst the beehives, suggesting a defined fingerprint that is affected, not only by the nectar and pollen gathered from local plants, but also from other environmental sources. In summary, our results show that every hive possesses their own distinct microbiome, and that honeybee products are valuable indicators of the bacteria present in the beehives and their surrounding environment.

microbiology↗

No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study

MotivationThe biases in Open Reading Frame (ORF) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any ORF prediction tool and allow them to choose the right tool for their analysis. ResultsWe present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of ORF prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availabilityhttps://github.com/NickJD/ORForise Contactnicholas@dimonaco.co.uk Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗

The Application of Countercurrent Chromatography for the study of Bacteriophages

1.Bacteriophages (phages) are viruses that target bacteria, with the ability to lyse and kill host bacterial cells. Due to this, they have been of some interest as a therapeutic since their discovery in the early 1900s, but with the recent increase in antibiotic resistance, phages have seen a resurgence in attention. Current methods of isolation and purification of phages can be long and tedious, with caesium chloride concentration gradients the gold standard for purifying a phage fraction. Isolation of novel phages requires centrifugation and ultrafiltration of mixed samples, such as water sources, effluent or faecal samples etc, to prepare phage filtrates for further testing. We propose countercurrent chromatography as a novel and alternative approach to use when studying phages, as a scalable and high-yield method for obtaining phage fractions. However, the full extent of the usefulness and resolution of separation with this technique has not been researched; it requires optimisation and ample testing before this can be revealed. Here we present an initial study to determine survivability of two phages, T4 and {phi}X174, using only water as a mobile phase in a Spectrum Series 20 HPCCC. Both phages were found to remain active once eluted from the column. Phages do not fully elute from the column and sodium hydroxide is necessary to flush the column between runs to deactivate remaining phages.

microbiology↗

In silico identification of novel peptides with antibacterial activity against multidrug resistant Staphylococcus aureus

Herein we report the identification and characterisation of two linear antimicrobial peptides (AMPs), HG2 and HG4, with activity against a wide range of multidrug resistant (MDR) bacteria, especially methicillin resistant Staphylococcus aureus (MRSA) strains, a highly problematic group of Gram-positive bacteria in the hospital and community environment. To identify the novel AMPs presented here, we employed the classifier model design, a feature extraction method using molecular descriptors for amino acids for the analysis, visualization, and interpretation of AMP activities from a rumen metagenomic dataset. This allowed for the in silico discrimination of active and inactive peptides in order to define a small number of promising novel lead AMP test candidates for chemical synthesis and experimental evaluation. In vitro data suggest that the chosen AMPs are fast acting, show strong biofilm inhibition and dispersal activity and are efficacious in an in vivo model of MRSA USA300 infection, whilst showing little toxicity to human erythrocytes and human primary cell lines ex vivo. Observations from biophysical AMP-lipid-interactions and electron microscopy suggest that the newly identified peptides interact with the cell membrane and may be involved in the inhibition of other cellular processes. Amphiphilic conformations associated with membrane disruption are also observed in 3D molecular modelling of the peptides. HG2 and HG4 both preferentially bind to MRSA total lipids rather than with human cell lipids indicating that HG4 may form superior templates for safer therapeutic candidates for MDR bacterial infections.\n\nAuthor SummaryWe are losing our ability to treat multidrug resistant (MDR) bacteria, otherwise known as superbugs. This poses a serious global threat to human health as bacteria are increasingly acquiring resistance to antibiotics. There is therefore urgent need to intensify our efforts to develop new safer alternative drug candidates. We emphasise the usefulness of complementing wet-lab and in silico techniques for the rapid identification of new drug candidates from environmental samples, especially antimicrobial peptides (AMPs). HG2 and HG4, the AMPs identified in our study show promise as effective therapies for the treatment of methicillin resistant Staphylococcus aureus infections both in vitro and in vivo whilst having little cytotoxicity against human primary cells, a step forward in the fight against MDR infections.

microbiology↗

Computational haplotype recovery and long-read validation identifies novel isoforms of industrially relevant enzymes from natural microbial communities

Elucidation of population-level diversity of microbiomes is a significant step towards a complete understanding of the evolutionary, ecological and functional importance of microbial communities. Characterizing this diversity requires the recovery of the exact DNA sequence (haplotype) of each gene isoform from every individual present in the community. To address this, we present Hansel and Gretel: a freely-available data structure and algorithm, providing a software package that reconstructs the most likely haplotypes from metagenomes. We demonstrate recovery of haplotypes from short-read Illumina data for a bovine rumen microbiome, and verify our predictions are 100% accurate with long-read PacBio CCS sequencing. We show that Gretels haplotypes can be analyzed to determine a significant difference in mutation rates between core and accessory gene families in an ovine rumen microbiome. All tools, documentation and data for evaluation are open source and available via our repository: https://github.com/samstudio8/gretel

bioinformatics↗

Probabilistic Recovery Of Cryptic Haplotypes From Metagenomic Data

The cryptic diversity of microbial communities represent an untapped biotechnological resource for biomining, biorefining and synthetic biology. Revealing this information requires the recovery of the exact sequence of DNA bases (or \"haplotype\") that constitutes the genes and genomes of every individual present. This is a computationally difficult problem complicated by the requirement for environmental sequencing approaches (metagenomics) due to the resistance of the constituent organisms to culturing in vitro.\n\nHaplotypes are identified by their unique combination of DNA variants. However, standard approaches for working with metagenomic data require simplifications that violate assumptions in the process of identifying such variation. Furthermore, current haplotyping methods lack objective mechanisms for choosing between alternative haplotype reconstructions from microbial communities.\n\nTo address this, we have developed a novel probabilistic approach for reconstructing haplotypes from complex microbial communities and propose the \"metahaplome\" as a definition for the set of haplotypes for any particular genomic region of interest within a metagenomic dataset. Implemented in the twin software tools Hansel and Gretel, the algorithm performs incremental probabilistic haplotype recovery using Naive Bayes -- an efficient and effective technique.\n\nOur approach is capable of reconstructing the haplotypes with the highest likelihoods from metagenomic datasets without a priori knowledge or making assumptions of the distribution or number of variants. Additionally, the algorithm is robust to sequencing and alignment error without altering or discarding observed variation and uses all available evidence from aligned reads. We validate our approach using synthetic metahaplomes constructed from sets of real genes, and demonstrate its capability using metagenomic data from a complex HIV-1 strain mix. The results show that the likelihood framework can allow recovery from microbial communities of cryptic functional isoforms of genes with 100% accuracy.

bioinformatics↗