Bioinformatics tools for analysing viral genomic data

The field of viral genomics and bioinformatics is experiencing a strong resurgence due to high-throughput sequencing (HTS) technology, which enables the rapid and cost-effective sequencing and subsequent assembly of large numbers of viral genomes. In addition, the unprecedented power of HTS technologies has enabled the analysis of intra-host viral diversity and quasispecies dynamics in relation to important biological questions on viral transmission, vaccine resistance and host jumping. HTS also enables the rapid identification of both known and potentially new viruses from field and clinical samples, thus adding new tools to the fields of viral discovery and metagenomics. Bioinformatics has been central to the rise of HTS applications because new algorithms and software tools are continually needed to process and analyse the large, complex datasets generated in this rapidly evolving area. In this paper, the authors give a brief overview of the main bioinformatics tools available for viral genomic research, with a particular emphasis on HTS technologies and their main applications. They summarise the major steps in various HTS analyses, starting with quality control of raw reads and encompassing activities ranging from consensus and de novo genome assembly to variant calling and metagenomics, as well as RNA sequencing

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined "true tree" using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons

Introducing W.A.T.E.R.S.: a Workflow for the Alignment, Taxonomy, and Ecology of Ribosomal Sequences

<p>Abstract</p> <p>Background</p> <p>For more than two decades microbiologists have used a highly conserved microbial gene as a phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA sequence analysis has increased correspondingly.</p> <p>Results</p> <p>We present WATERS, an integrated approach for 16 S rDNA analysis that bundles a suite of publicly available 16 S rDNA analysis software tools into a single software package. The "toolkit" includes sequence alignment, chimera removal, OTU determination, taxonomy assignment, phylogentic tree construction as well as a host of ecological analysis and visualization tools. WATERS employs a flexible, collection-oriented 'workflow' approach using the open-source Kepler system as a platform.</p> <p>Conclusions</p> <p>By packaging available software tools into a single automated workflow, WATERS simplifies 16 S rDNA analyses, especially for those without specialized bioinformatics, programming expertise. In addition, WATERS, like some of the newer comprehensive rRNA analysis tools, allows researchers to minimize the time dedicated to carrying out tedious informatics steps and to focus their attention instead on the biological interpretation of the results. One advantage of WATERS over other comprehensive tools is that the use of the Kepler workflow system facilitates result interpretation and reproducibility via a data provenance sub-system. Furthermore, new "actors" can be added to the workflow as desired and we see WATERS as an initial seed for a sizeable and growing repository of interoperable, easy-to-combine tools for asking increasingly complex microbial ecology questions.</p

From Genes to Ecosystems: Resource Availability and DNA Methylation Drive the Diversity and Abundance of Restriction Modification Systems in Prokaryotes

Together, prokaryotic hosts and their viruses numerically dominate the planet and are engaged in an eternal struggle of hosts evading viral predation and viruses overcoming defensive mechanisms employed by their hosts. Prokaryotic hosts have been found to carry several viral defense systems in recent years with Restriction Modification systems (RMs) were the first discovered in the 1950s. While we have biochemically elucidated many of these systems in the last 70 years, we still struggle to understand what drives their gain and loss in prokaryotic genomes. In this work, we take a computational approach to understand the underlying evolutionary drivers of RMs by assessing ‘big data’ signals of RMs in prokaryotic genomes and incorporating molecular data in trait-based mathematical models. Focusing on the Cyanobacteria, we found a large discrepancy in the frequency of RMs per genome in different environmental contexts, where Cyanobacteria that live in oligotrophic nutrient conditions have few to no RMs and those in nutrient-rich conditions consistently have many RMs. While our models agree with the observation that increased nutrient inputs make the selective pressure of RMs more intense, they were unable to reconcile the high numbers of RMs per genome with their potent defensive properties- a situation of apparent overkill. By incorporating viral methylation, an unavoidable effect of RMs, we were able to explain how organisms could carry over 15 RMs. With this discovery, we then tried and reassess the distribution of methyltransferases, an essential component of RMs that can also have alternate physiological rolls in the cell. We expand on conventional wisdom, that methyltransferases that are widely phylogenetically conserved are associated with global cellular regulation. However, we also find that organisms with high numbers of RMs also have a surprising amount of conservation in the methyltransferases that they carry. This data suggests caution should be used in associating phylogenic signals with functional rolls in methyltransferases as different functional rolls seem to overlap in their phylogenetic signal. Indeed, we suggest trait-based modeling may be the best tool in elucidating why organisms with a high selective pressure to maintain RMs appear to have conserved methyltransferase

The Qphyl System: a web-based interactive system for phylogenetic analysis

Phylogenetic tree reconstruction is a prominent problem in computational biology. Currently, all computational methods have their limitations and work well only for simple problems of small size. No existing method can guarantee that trees constructed for real-world problems are true phylogenetic trees for large and complex problems mainly because the existing computational models are not very biologically realistic. It has become a serious issue for many important real-life applications which often desire accurate results from phylogenetic analysis. Thus, it is very crucial to effectively incorporate multi-disciplinary analyses and synthesize results from various sources when answering real-life questions. In this thesis, a novel web-based phylogeny reconstruction system with a real-time interactive environment, called Qphyl (short for quartet-based phylogenetic analysis) is introduced. The Qphyl system uses a new interactive approach to enable biologists to greatly improve the final results through effectively dynamic interaction with the computation, e.g., to move the computation back and forth to different stages so users can check the intermediate results, compare results from different methods and carry out certain manual refinements using their biological domain-specific knowledge in the decision making on how a tree should be reconstructed. Currently the alpha version of this web-based interactive system has been released and accessible through the URL: http://ww-test.it.usyd.edu.au/sogrid/qphyl/

In silico exploration of stx2a-positive (STEC) and stx-negative Escherichia coli (STEC-LST)

Escherichia coli er en harmløs bakterie som naturlig sameksisterer med mennesker og dyr i tarmen. E. coli-genomet er dynamisk og kan skape store interne endringer (rekombinasjon), overføre plasmider (konjugasjon), tilegne seg DNA (transformasjon) og virus eller fager (transduksjon). Dette betyr at det finnes mange varianter av E. coli og at bakterien er vanskelig å analysere siden rekombinasjon og genetiske forandringer skjer fra generasjon til generasjon. Noen endringer er til fordel, som inkorporering av plasmider med antibiotikaresistens. Andre endringer kan være til skade, som integrasjon av en bakteriofag eller toksiner. Det siste konseptet er av interesse når det omhandler shigatoksiner (Stx) produsert av shigatoksingener (stx). Toksinet observeres å bli overført mellom E. coli bakterier gjennom bakteriofager. Fagen er kartlagt ganske godt, men det er fortsatt karakteristisk informasjon som må avdekkes. Siden stx er veldig smittsomme for mennesker og dyr (som ofte er asymptomatiske), er det et behov for å forstå hvordan fagene overfører toksinene til E. coli. I dette studiet er det av interesse å undersøke E. coli bakterier som er like. Noen E. coli i samme serogruppe med identisk multi-locus VNTR analyse-profil (MLVA-profil) produserer Stx, mens andre avstår. Siden stx er overført ved stx-fager, er det et behov for å analysere om det er rester fra fagen som kan ha forsvunnet fra genomet, eller andre varierende genetiske områder med spesifikke DNA-elementer hvor fager lett kan integreres.Escherichia coli is a harmless bacterium that naturally coexists with humans and animals in their intestinal tracts. The E. coli genome is dynamic and can make larger internal changes (recombination) like exchange plasmids (conjugation), add DNA (transformation) and virus or phages (transduction). This means that E. coli have many variants and are hard to analyze since recombination and genomic changes can happen from generation to generation. Some changes are beneficial, while others can be harmful. An example of a benefit could be incorporation of plasmids with antibiotic resistance genes, a disadvantage could be insertion of phages with toxins. The last concept is of special interest regarding shiga toxins (Stx) produced by shiga toxin genes (stx). The toxin seems to be transferred between E. coli bacteria through bacteriophages. The phages are characterized relatively well, but there is still a lot of information left to reveal. Since stx is very infectious to humans and animals (often asymptomatic), there is a need to understand how the phages transfer these toxins to E. coli. In this study there is an interest in researching E. coli bacteria that are similar. Some E. coli within the same serotype with identical multilocus VNTR analysis (MLVA) profile produce Stx, while others do not. Since stx is transferred through stx-phages, there exists a need to examine if any traces are left from the phage (that could be missing from the genome) or various genetic regions with certain DNA patterns where the phages easily integrate.M-K

High throughput prediction of inter-protein coevolution

Inter-protein co-evolution analysis can reveal in/direct functional or physical protein interactions. Inter-protein co-evolutionary analysis compares the correlation of evolutionary changes between residues on aligned orthologous sequences. On the other hand, modern methods used in experimental cell biological research to screen for protein-protein interaction, often based on mass spectrometry, often lead to identification of large amount of possible interacting proteins. If automatized, inter-protein co-evolution analysis can serve as a valuable step in refining the results, typically containing hundreds of hits, for further experiments. Manual retrieval of tens of orthologous sequences, alignment and phylogenetic tree preparations of such amounts of data is insufficient. The aim of this thesis is to create an assembly of scripts that automatize high-throughput inter-protein co-evolution analysis. Scripts were written in Python language. Scripts are using API client interface to access online databases with sequences of input protein identifiers. Through matched identifiers, over 85 representative orthologous sequences from vertebrate species are retrieved from OrthoDB orthologues database. Scripts align these sequences with PRANK MSA algorithm and create corresponding phylogenetic tree. All protein pairs are structured for multicore computation with CAPS programme on CSC supercomputer. Multiple CAPS outputs are abstracted into comprehensive form for comparison of relative co-adaptive co-evolution between proposed protein pairs. In this work, I have developed automatization for a protein-interactome screen done by proximity labelling of B cell receptor and plasma membrane associated proteins under activating or non-activating conditions. Applying high-throughput co-evolutionary analysis to this data provides a completely new approach to identify new players in B cell activation, critical for autoimmunity, hypo-immunity or cancer. Results showed unsatisfying performance of CAPS, explanation and alternatives were given

A machine learning based framework to identify and classify long terminal repeat retrotransposons

Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. Accurate identification and classification of TEs present in a genome is an important step towards understanding their effects on genes and their role in genome evolution. We introduce TE-LEARNER, a framework based on machine learning that automatically identifies TEs in a given genome and assigns a classification to them. We present an implementation of our framework towards LTR retrotransposons, a particular type of TEs characterized by having long terminal repeats (LTRs) at their boundaries. We evaluate the predictive performance of our framework on the well-annotated genomes of Drosophila melanogaster and Arabidopsis thaliana and we compare our results for three LTR retrotransposon superfamilies with the results of three widely used methods for TE identification or classification: REPEATMASKER, CENSOR and LTRDIGEST. In contrast to these methods, TE-LEARNER is the first to incorporate machine learning techniques, outperforming these methods in terms of predictive performance , while able to learn models and make predictions efficiently. Moreover, we show that our method was able to identify TEs that none of the above method could find, and we investigated TE-LEARNER'S predictions which did not correspond to an official annotation. It turns out that many of these predictions are in fact strongly homologous to a known TE