Marco Falda

Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms

BackgroundPredicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods.ResultsArgot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.ConclusionsThe revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.

Genomic comparative analysis and gene function prediction in infectious diseases: application to the investigation of a meningitis outbreak

BackgroundNext generation sequencing (NGS) is being increasingly used for the detection and characterization of pathogens during outbreaks. This technology allows rapid sequencing of pathogen full genomes, useful not only for accurate genotyping and molecular epidemiology, but also for identification of drug resistance and virulence traits.MethodsIn this study, an approach based on whole genome sequencing by NGS, comparative genomics, and gene function prediction was set up and retrospectively applied for the investigation of two N. meningitidis serogroup C isolates collected from a cluster of meningococcal disease, characterized by a high fatality rate.ResultsAccording to conventional molecular typing methods, all the isolates had the same typing results and were classified as outbreak isolates within the same N. meningitidis sequence type ST-11, while full genome sequencing demonstrated subtle genetic differences between the isolates. Looking for these specific regions by means of 9 PCR and cycle sequencing assays in other 7 isolates allowed distinguishing outbreak cases from unrelated cases. Comparative genomics and gene function prediction analyses between outbreak isolates and a set of reference N. meningitidis genomes led to the identification of differences in gene content that could be relevant for pathogenesis. Most genetic changes occurred in the capsule locus and were consistent with recombination and horizontal acquisition of a set of genes involved in capsule biosynthesis.ConclusionsThis study showed the added value given by whole genome sequencing by NGS over conventional sequence-based typing methods in the investigation of an outbreak. Routine application of this technology in clinical microbiology will significantly improve methods for molecular epidemiology and surveillance of infectious disease and provide a bulk of data useful to improve our understanding of pathogens biology.

Enhancing protein function prediction with taxonomic constraints--The Argot2.5 web server.

Argot2.5 (Annotation Retrieval of Gene Ontology Terms) is a web server designed to predict protein function. It is an updated version of the previous Argot2 enriched with new features in order to enhance its usability and its overall performance. The algorithmic strategy exploits the grouping of Gene Ontology terms by means of semantic similarity to infer protein function. The tool has been challenged over two independent benchmarks and compared to Argot2, PANNZER, and a baseline method relying on BLAST, proving to obtain a better performance thanks to the contribution of some key interventions in critical steps of the working pipeline. The most effective changes regard: (a) the selection of the input data from sequence similarity searches performed against a clustered version of UniProt databank and a remodeling of the weights given to Pfam hits, (b) the application of taxonomic constraints to filter out annotations that cannot be applied to proteins belonging to the species under investigation. The taxonomic rules are derived from our in-house developed tool, FunTaxIS, that extends those provided by the Gene Ontology consortium. The web server is free for academic users and is available online at http://www.medcomp.medicina.unipd.it/Argot2-5/.

Quantitative label-free redox proteomics of reversible cysteine oxidation in red blood cell membranes

Reversible oxidation of cysteine residues is a relevant posttranslational modification of proteins. However, the low activation energy and transitory nature of the redox switch and the intrinsic complexity of the analysis render quite challenging the aim of a rigorous high-throughput screening of the redox status of redox-sensitive cysteine residues. We describe here a quantitative workflow for redox proteomics, where the ratio between the oxidized forms of proteins in the control vs treated samples is determined by a robust label-free approach. We critically present the convenience of the procedure by specifically addressing the following aspects: (i) the accurate ratio, calculated from the whole set of identified peptides rather than just isotope-tagged fragments; (ii) the application of a robust analytical pipeline to frame the most consistent data averaged over the biological variability; (iii) the relevance of using stringent criteria of analysis, even at the cost of losing potentially interesting but statistically uncertain data. The pipeline has been assessed on red blood cell membrane challenged with diamide as a model of a mild oxidative condition. The cluster of identified proteins encompassed components of the cytoskeleton more oxidized. Indirectly, our analysis confirmed the previous observation that oxidized hemoglobin binds to membranes while oxidized peroxiredoxin 2 loses affinity.

Redox status in a model of cancer stem cells.

Reversible oxidation of Cys residues is a crucial element of redox homeostasis and signaling. According to a popular concept in oxidative stress signaling, the oxidation of targets of signals can only take place following an overwhelming of the cellular antioxidant capacity. This concept, however, ignores the activation of feedback mechanisms possibly leading to a paradoxical effect. In a model of cancer stem cells (CSC), stably overexpressing the TAZ oncogene, we observed that the increased formation of oxidants is associated with a globally more reduced state of proteins. Redox proteomics revealed that several proteins, capable of undergoing reversible redox transitions, are indeed more reduced while just few are more oxidized. Among the proteins more oxidized, G6PDH emerges as both more expressed and activated by oxidation. This accounts for the observed more reduced state of the NADPH/NADP+ couple. The dynamic redox flux generating this apparently paradoxical effect is rationalized in a computational system biology model highlighting the crucial role of G6PDH activity on the rate of redox transitions eventually leading to the reduction of reversible redox switches.

Discriminating exanthematic diseases from temporal patterns of patient symptoms

The temporal dimension is a characterizing factor of many diseases, in particular, of the exanthematic diseases. Therefore, the diagnosis of this kind of diseases can be based on the recognition of the typical temporal progression and duration of different symptoms. To this aim, we propose to apply a temporal reasoning system we have developed. The system is able to handle both qualitative and metric temporal knowledge affected by vagueness and uncertainty. In this preliminary work, we show how the fuzzy temporal framework allows us to represent typical temporal structures of different exanthematic diseases (e.g. Scarlet Fever, Measles, Rubella et c.) thus making possible to find matches with data coming from the patient disease.

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms

Summary: Pathway Inspector is an easy‐to‐use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology‐based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Availability and Implementation: Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact: paolo.fontana@fmach.it

SimBioNeT: A Simulator of Biological Network Topology

Studying biological networks at topological level is a major issue in computational biology studies and simulation is often used in this context, either to assess reverse engineering algorithms or to investigate how topological properties depend on network parameters. In both contexts, it is desirable for a topology simulator to reproduce the current knowledge on biological networks, to be able to generate a number of networks with the same properties and to be flexible with respect to the possibility to mimic networks of different organisms. We propose a biological network topology simulator, SimBioNeT, in which module structures of different type and size are replicated at different level of network organization and interconnected, so to obtain the desired degree distribution, e.g., scale free, and a clustering coefficient constant with the number of nodes in the network, a typical characteristic of biological networks. Empirical assessment of the ability of the simulator to reproduce characteristic properties of biological network and comparison with E. coli and S. cerevisiae transcriptional networks demonstrates the effectiveness of our proposal.

keeSeek: Searching distant non-existing words in genomes for PCR-based applications

UNLABELLED The search for short words that are absent in the genome of one or more organisms (neverwords, also known as nullomers) is attracting growing interest because of the impact they may have in recent molecular biology applications. keeSeek is able to find absent sequences with primer-like features, which can be used as unique labels for exogenously inserted DNA fragments to recover their exact position into the genome using PCR techniques. The main differences with respect to previously developed tools for neverwords generation are (i) calculation of the distance from the reference genome, in terms of number of mismatches, and selection of the most distant sequences that will have a low probability to anneal unspecifically; (ii) application of a series of filters to discard candidates not suitable to be used as PCR primers. KeeSeek has been implemented in C++ and CUDA (Compute Unified Device Architecture) to work in a General-Purpose Computing on Graphics Processing Units (GPGPU) environment. AVAILABILITY AND IMPLEMENTATION Freely available under the Q Public License at http://www.medcomp.medicina.unipd.it/main_site/doku.php?id=keeseek.

Tractable Fragments of Fuzzy Qualitative Algebra

Abstract In this paper we study the computational complexity of Fuzzy Qualitative Temporal Algebra (QA fuz ), a framework that combines qualitative temporal constraints between points and intervals, and allows modelling vagueness and uncertainty. Its tractable fragments can be identified by generalizing the results obtained for crisp Constraint Satisfaction Problems (CSPs) to fuzzy CSPs (FCSPs); to do this, we apply a general methodology based on the notion of α-cut. In particular, the results concerning the tractability of Qualitative Algebra QA, obtained in a recent study by different authors, can be extended to identify the tractable algebras of the fuzzy Qualitative Algebra QA fuz in such a way that the obtained set is maximal, namely any maximal tractable fuzzy algebra belongs to this set.