UNLABELLED: A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: We present iFUSE (integrated fusion gene explorer), an online visualization tool that provides a fast and informative view of structural variation data and prioritizes those breaks likely representing fusion genes. This application uses calculated break points to determine fusion genes based on the latest annotation for genomic sequence information, and where relevant the structural variation (SV) events are annotated with predicted RNA and protein sequences. iFUSE takes as input a Complete Genomics (CG) junction file, a FusionMap fusion detection report file or a file already analysed and annotated by the iFUSE application on a previous occasion. RESULTS: We demonstrate the use of iFUSE with case studies from tumour-normal SV detection derived from Complete Genomics whole-genome sequencing results. AVAILABILITY: iFUSE is available as a web service at http://ifuse.erasmusmc.nl.

SUMMARY: The discovery of functionally related groups in a set of significantly abundant proteins from a mass spectrometry experiment is an important step in a proteomics analysis pipeline. Here we describe NetWeAvers (Network Weighted Averages) for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers is an R package that provides a novel method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks. AVAILABILITY AND IMPLEMENTATION: The NetWeAvers package is written in R, is open source and is freely available on CRAN and from netweavers.erasmusmc.nl under the GPL-v2 license. CONTACT: [email protected]