MOTIVATION: Microscopy images of stained cells and tissues play a central role in most biomedical experiments and routine histopathology. Storing colour histological images digitally opens the possibility to process numerically colour distribution and intensity to extract quantitative data. Among those numerical procedures are colour deconvolution, which enable decomposing an RGB image into channels representing the optical absorbance and transmittance of the dyes when their RGB representation is known. Consequently, a range of new applications become possible for morphological and histochemical segmentation, automated marker localization and image enhancement. AVAILABILITY AND IMPLEMENTATION: Colour deconvolution is presented here in two open-source forms: a MATLAB program/function and an ImageJ plugin written in Java. Both versions run in Windows, Macintosh and UNIX-based systems under the respective platforms. Source code and further documentation are available at: https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In dynamic-traffic Wavelength Division Multiplexing (WDM) networks, it is desirable to guarantee each optical circuit a minimum Optical Signal-to-Noise Ratio (OSNR) throughout the circuit's entire lifetime. The objective of this paper is to increase such guaranteed OSNR, which in turn enables bandwidth-efficient modulation formats to be applied to the circuit signals. The guaranteed OSNR is improved by applying OSNR-driven Spectrum Assignment (OSA) constraints to each fiber link in the network, based on its forecast traffic volume (offered load). Applying OSA constraints to fiber links has two conflicting outcomes. On the one hand, circuit requests may experience higher blocking probabilities due to the lack of usable spectrum. On the other hand, the adverse effects of medium nonlinearity may be contained, thus yielding improved circuit OSNR. Two OSA constraints are described and their favorable impact on circuit OSNR (up to 2.75 dB gain) is estimated by using a Gaussian Noise (GN) model and an event-driven simulation of two WDM networks supporting dynamic circuits.

Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools – Genome Analysis Toolkit (GATK) and MuTect – to create the GATK-LOD N method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline. The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LOD N increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LOD N filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LOD N increased both specificity and sensitivity of GATK results. We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LOD N method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes.

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/.

BACKGROUND: Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies. METHODS: Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs). RESULTS: The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%. CONCLUSIONS: Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures.

In GMPLS-based transparent optical networks, the switching and routing functionalities are performed in optical domain. However in these networks, physical layer impairments (PLIs) incurred by non-ideal transmission media accumulate along an optical path, and the overall effect determines the feasib

This paper explores the solution space for extending the signaling protocol of a GMPLS control plane for an impairment-aware path setup in transparent optical networks. Four combinations of routing and optical feasibility checking architectures based on modifications of RSVP-TE are proposed and studied. Simulation results show that a combination of hop- by-hop routing and feasibility check can be considered as a good compromise both in term of blocking probability and limited impact on the control plane. The slightly higher lightpath set-up time compared to other architectures can be tolerated especially considering the impact on the RSVP protocol current behavior as well as its independence from parameters strictly related to the network properties and topology.