Massimo Delledonne

Bellerophontes: an RNA-Seq data analysis framework for chimeric transcripts discovery based on accurate fusion model

MOTIVATION Next-generation sequencing technology allows the detection of genomic structural variations, novel genes and transcript isoforms from the analysis of high-throughput data. In this work, we propose a new framework for the detection of fusion transcripts through short paired-end reads which integrates splicing-driven alignment and abundance estimation analysis, producing a more accurate set of reads supporting the junction discovery and taking into account also not annotated transcripts. Bellerophontes performs a selection of putative junctions on the basis of a match to an accurate gene fusion model. RESULTS We report the fusion genes discovered by the proposed framework on experimentally validated biological samples of chronic myelogenous leukemia (CML) and on public NCBI datasets, for which Bellerophontes is able to detect the exact junction sequence. With respect to state-of-art approaches, Bellerophontes detects the same experimentally validated fusions, however, it is more selective on the total number of detected fusions and provides a more accurate set of spanning reads supporting the junctions. We finally report the fusions involving non-annotated transcripts found in CML samples. AVAILABILITY AND IMPLEMENTATION Bellerophontes JAVA/Perl/Bash software implementation is free and available at http://eda.polito.it/bellerophontes/.

Nitric Oxide-Mediated Signaling Functions During the Plant Hypersensitive Response

Growing evidence suggests that nitric oxide (NO) is a central molecule in several physiological functions, ranging from plant development to defence responses. Plants use NO as a signaling molecule in pathways comparable to those of mammals, suggesting the existence of many commonalities between the action of NO in plants and animals.

Nitric Oxide Involvement in Incompatible Plant-Pathogen Interactions

Most plants resist potential parasite attack using a variety of biochemical responses that often lead to a localized cell death termed the hypersensitive response, and include production of antimicrobial compounds, lignin formation, oxidative and nitrosative burst, and increased expression of genes related to pathogenesis. In this framework, nitric oxide (NO) functions together with reactive oxygen species in triggering hypersensitive cell death, and works independently of such intermediates in the induction of defense-related genes. In this chapter, we will examine the synthesis of NO and its signaling functions in the hypersensitive response and in the establishment of systemic acquired resistance.

RNA-Seq Evaluating Several Custom Microarrays Background Correction and Gene Expression Data Normalization Systems

Microarray gene expression technologies represents a widely used tool in transcriptomics and genomics studies worldwide. Even if this technology exhibits a low dynamic range as well as a feeble sensitivity and specificity (limited performances) with respect to RNA sequencing (RNA-seq) Original Research Article Dago et al.; BJI, 19(4): 1-14, 2017; Article no.BJI.36345 2 methodology in whole transcriptomic and/or genomic studies; it is noteworthy to underline the stability of the former (microarrays) because of their well-established biostatistics and bioinformatics analysis schemes. Several studies shown that inadequate data pre-processing as regards microarray gene expression data analysis; i.e. inadequate gene expression data normalization (DN) and scarce noise background subtraction (BS), might compromise microarray aptitude in calling correctly significantly differentially expressed genes (DEGs). Here, we were interested in assessing the performance of 20 different microarrays background correction and gene expression data normalisation arrangements from R software “linear models for microarray and RNA-seq data analysis” package, by comparing the number of differentially expressed genes detected by our previous developed custom microarray designs and RNA-seq platform. The present study basing exclusively on several clustering and principal component analysis (PCA) as well as descriptive and inferential statistic surveys, developed in the R programing environment, suggested a predominance of microarray data normalisation systems with respect to noise background correction procedure. Although, all processed background subtraction and gene expression data normalization arrangement (BS+DN) claimed to improve the agreement (sensitivity) between microarrays and RNA-seq in calling DEGs; quantile normalisation procedure applied to our processed custom microarray designs has been recorded as exhibiting the best sensitivity (p-value<0.05), since discriminates the highest number of DEGs in agreement with RNA-seq as opposed to the others analysed microarray gene expression data normalisation systems. In conclusion our findings confirmed the pre-eminence of data pre-processing procedure in microarray gene expression profiling analysis according a priority to data normalisation procedure and suggested the stability of quantile normalisation system with respect to the others processed normalisation arrangements in the present executed gene expression comparative study.