BITS 2015: The annual meeting of the Italian Society of Bioinformatics

This preface introduces the content of the BioMed Central journal Supplements related to the BITS 2015 meeting, held in Milan, Italy, from the 3th to the 5th of June, 2015

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Active Discovery of Network Roles for Predicting the Classes of Network Nodes

Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way that nodes link in a network exhibits a different, more complex relationship to their attributes. Here, we consider networks in which we know how the nodes are connected, but we do not know the class labels of the nodes or how class labels relate to the network links. We wish to identify the best subset of nodes to label in order to learn this relationship between node attributes and network links. We can then use this discovered relationship to accurately predict the class labels of the rest of the network nodes. We present a model that identifies groups of nodes with similar link patterns, which we call network roles, using a generative blockmodel. The model then predicts labels by learning the mapping from network roles to class labels using a maximum margin classifier. We choose a subset of nodes to label according to an iterative margin-based active learning strategy. By integrating the discovery of network roles with the classifier optimisation, the active learning process can adapt the network roles to better represent the network for node classification. We demonstrate the model by exploring a selection of real world networks, including a marine food web and a network of English words. We show that, in contrast to other network classifiers, this model achieves good classification accuracy for a range of networks with different relationships between class labels and network links

Application of genomic and quantitative genetic tools to identify candidate resistance genes for brown rot resistance in peach.

The availability of a complete peach genome assembly and three different peach genome sequences created by our group provide new opportunities for application of genomic data and can improve the power of the classical Quantitative Trait Loci (QTL) approaches to identify candidate genes for peach disease resistance. Brown rot caused by Monilinia spp., is the most important fungal disease of stone fruits worldwide. Improved levels of peach fruit rot resistance have been identified in some cultivars and advanced selections developed in the UC Davis and USDA breeding programs. Whole genome sequencing of the Pop-DF parents lead to discovery of high-quality SNP markers for QTL genome scanning in this experimental population. Pop-DF created by crossing a brown rot moderately resistant cultivar 'Dr. Davis' and a brown rot resistant introgression line, 'F8,1-42', derived from an initial almond × peach interspecific hybrid, was evaluated for brown rot resistance in fruit of harvest maturity over three seasons. Using the SNP linkage map of Pop-DF and phenotypic data collected with inoculated fruit, a genome scan for QTL identified several SNP markers associated with brown rot resistance. Two of these QTLs were placed on linkage group 1, covering a large (physical) region on chromosome 1. The genome scan for QTL and SNP effects predicted several candidate genes associated with disease resistance responses in other host-pathogen systems. Two potential candidate genes, ppa011763m and ppa026453m, may be the genes primarily responsible for M. fructicola recognition in peach, activating both PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI) responses. Our results provide a foundation for further genetic dissection, marker assisted breeding for brown rot resistance, and development of peach cultivars resistant to brown rot

Transcriptome Profiling of Citrus Fruit Response to Huanglongbing Disease

Huanglongbing (HLB) or “citrus greening” is the most destructive citrus disease worldwide. In this work, we studied host responses of citrus to infection with Candidatus Liberibacter asiaticus (CaLas) using next-generation sequencing technologies. A deep mRNA profile was obtained from peel of healthy and HLB-affected fruit. It was followed by pathway and protein-protein network analysis and quantitative real time PCR analysis of highly regulated genes. We identified differentially regulated pathways and constructed networks that provide a deep insight into the metabolism of affected fruit. Data mining revealed that HLB enhanced transcription of genes involved in the light reactions of photosynthesis and in ATP synthesis. Activation of protein degradation and misfolding processes were observed at the transcriptomic level. Transcripts for heat shock proteins were down-regulated at all disease stages, resulting in further protein misfolding. HLB strongly affected pathways involved in source-sink communication, including sucrose and starch metabolism and hormone synthesis and signaling. Transcription of several genes involved in the synthesis and signal transduction of cytokinins and gibberellins was repressed while that of genes involved in ethylene pathways was induced. CaLas infection triggered a response via both the salicylic acid and jasmonic acid pathways and increased the transcript abundance of several members of the WRKY family of transcription factors. Findings focused on the fruit provide valuable insight to understanding the mechanisms of the HLB-induced fruit disorder and eventually developing methods based on small molecule applications to mitigate its devastating effects on fruit production

Progresses and Challenges in Link Prediction

Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and metrics of link prediction, this Perspective will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning and others, mainly extracted from thousands of related publications in the last decade. Finally, this Perspective will outline some long-standing challenges for future studies.Comment: 45 pages, 1 tabl

ABA-deficiency and molecular mechanisms involved in the dehydration response and ripening of citrus fruit

The aim of this work has been to unravel the influence of the phytohormone ABA in the molecular mechanisms underlying the postharvest dehydration response and the development and ripening of citrus fruit, taking advantage of the spontaneous fruit-specific ABA-deficient 'Pinalate' mutant, which is more prone to dehydration and to develop non-chilling peel pitting (NCPP) than its wild-type 'Navelate' orange. A comparative transcriptomic analysis highlighted the ability of parental fruit to induce early molecular responses aimed to reduce water loss and its detrimental effects. ABA application to mutant fruit modulated relevant transcriptomic changes but did not substantially modify either dehydration rate or NCPP incidence, which suggested that 'Pinalate' mutant could be insensitive to ABA. Therefore, the ABA perception system components in Citrus were identified and their regulation under developmental and stressful conditions was investigated. Minor differences between varieties were found in the CsPYR/PYL/RCAR ABA receptors and the CsSnRK2s downstream protein kinases transcript levels, whereas CsPP2CAs negative regulators accumulation was lower in the mutant fruit. ABA receptors and CsSnRK2s gene expression patterns depended on the tissue, the stress severity and the source of the ABA signal from a developmental or stressful stimulus, whilst CsPP2CAs displayed a consistent pattern. Overall, this work suggest that the ABA-deficient 'Pinalate' fruit may sense ABA although the hormone signal could be impaired because reduced CsPP2CAs levels causing altered water stress response and higher NCPP susceptibility.Romero Gascón, F. (2012). ABA-deficiency and molecular mechanisms involved in the dehydration response and ripening of citrus fruit [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18105Palanci

A temporal precedence based clustering method for gene expression microarray data

Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. Results: A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system. Conclusions: Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits