Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.

Summary: Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. Availability: NetworkAnalyzer can be downloaded via the Cytoscape web

The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de). Supported assays include whole genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays, and any other protocol that produces high-resolution DNA methylation data. Important applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

PROTOCOL 670 | VOL.7 NO.4 | 2012 | NATURE PROTOCOLS INTRODUCTION Current high-throughput techniques such as yeast two-hybrid screens for protein interaction partners produce great volumes of experimental data that can be integrated and explored to gain insight into biological processes performed by interacting molecules 1-6. Furthermore, structural biologists study the interactions of residues in protein structures to understand complex protein structure-function relationships 7-11. Commonly, large-scale interaction data are represented as networks and initially analyzed by graph-theoretic methods to characterize the topological network structure and its global and local interaction properties 12-18. A number of software tools are available for the visual exploration and computational analysis of networks 19-21. General software libraries for network analysis are the Java framework JUNG 22 , the C + + library LEDA 23 , the Python package NetworkX 24 and R packages such as igraph 25 , statnet 26 , sna 27 , tnet 28 and QuACN 29. However, they cannot be applied by users without programming expertise. In contrast, sophisticated free software platforms such as Pajek 30 , VisANT 31 , ONDEX 32 and BIANA 33 provide graphical user interfaces for the analysis of biological networks. In addition, the free and stand-alone Cytoscape platform has gained considerable interest in recent years because of its open-source code development and its rapidly growing community of users and developers 34,35. In particular, its functionality is easily extendable by additional plug-ins that support specific network analysis tasks. For example, software protocols are already available for cluster analysis with the TransClust and ClusterExplorer plug-ins 36 , as well as for the integration of physical and genetic interactions into module maps with the PanGIA plug-in 37. Here we demonstrate how to apply two of our Cytoscape plug-ins, NetworkAnalyzer 38 and RINalyzer 39 , for the standard and advanced analysis of network topologies. NetworkAnalyzer performs a comprehensive analysis of network topologies without requiring advanced knowledge in graph theory or programming expertise 38. In particular, it supports the characterization of molecular networks in terms of scale-free and small-world properties, modularity and hierarchical structure 5,12,13,40 , the identification of important network nodes and edges based on topological parameters 11,41-43 , and the comparison of networks with regard to their topology 44-47. Since its initial release in 2007, NetworkAnalyzer has been extended by additional features and topological parameters and is widely used in academia and industry as indicated by thousands of software downloads. Recently, this plug-in became an integral part of each standard installation of Cytoscape, and its source code was published under the GNU Lesser General Public License. Basically, NetworkAnalyzer efficiently computes a number of topological parameters, including node degree, clustering and topological coefficient, charact...

Charting differences between tumors and normal tissue is a mainstay of cancer research. However, clonal tumor expansion from complex normal tissue architectures potentially obscures cancer-specific events, including divergent epigenetic patterns. Using whole-genome bisulfite sequencing of normal B cell subsets, we observed broad epigenetic programming of selective transcription factor binding sites coincident with the degree of B cell maturation. By comparing normal B cells to malignant B cells from 268 patients with chronic lymphocytic leukemia (CLL), we showed that tumors derive largely from a continuum of maturation states reflected in normal developmental stages. Epigenetic maturation in CLL was associated with an indolent gene expression pattern and increasingly favorable clinical outcomes. We further uncovered that most previously reported tumor-specific methylation events are normally present in non-malignant B cells. Instead, we identified a potential pathogenic role for transcription factor dysregulation in CLL, where excess programming by EGR and NFAT with reduced EBF and AP-1 programming imbalances the normal B cell epigenetic program.

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Neuroblastoma is a malignancy of the developing sympathetic nervous system that is often lethal when relapse occurs. We here used whole-exome sequencing, mRNA expression profiling, array CGH and DNA methylation analysis to characterize 16 paired samples at diagnosis and relapse from individuals with neuroblastoma. The mutational burden significantly increased in relapsing tumors, accompanied by altered mutational signatures and reduced subclonal heterogeneity. Global allele frequencies at relapse indicated clonal mutation selection during disease progression. Promoter methylation patterns were consistent over disease course and were patient specific. Recurrent alterations at relapse included mutations in the putative CHD5 neuroblastoma tumor suppressor, chromosome 9p losses, DOCK8 mutations, inactivating mutations in PTPN14 and a relapse-specific activity pattern for the PTPN14 target YAP. Recurrent new mutations in HRAS, KRAS and genes mediating cell-cell interaction in 13 of 16 relapse tumors indicate disturbances in signaling pathways mediating mesenchymal transition. Our data shed light on genetic alteration frequency, identity and evolution in neuroblastoma.