MOTIVATION: Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. RESULTS: We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. AVAILABILITY AND IMPLEMENTATION: R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Genome browsers are widely used for locating interesting genomic regions, but their interactive use is obviously limited to inspecting short genomic portions. An ideal interaction is to provide patterns of regions on the browser, and then extract other genomic regions over the whole genome where such patterns occur, ranked by similarity. RESULTS: We developed SimSearch, an optimized pattern-search method and an open source plugin for the Integrated Genome Browser (IGB), to find genomic region sets that are similar to a given region pattern. It provides efficient visual genome-wide analytics computation in large datasets; the plugin supports intuitive user interactions for selecting an interesting pattern on IGB tracks and visualizing the computed occurrences of similar patterns along the entire genome. SimSearch also includes functions for the annotation and enrichment of results, and is enhanced with a Quickload repository including numerous epigenomic feature datasets from ENCODE and Roadmap Epigenomics. The paper also includes some use cases to show multiple genome-wide analyses of biological interest, which can be easily performed by taking advantage of the presented approach. CONCLUSIONS: The novel SimSearch method provides innovative support for effective genome-wide pattern search and visualization; its relevance and practical usefulness is demonstrated through a number of significant use cases of biological interest. The SimSearch IGB plugin, documentation, and code are freely available at https://deib-geco.github.io/simsearch-app/ and https://github.com/DEIB-GECO/simsearch-app/ .

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

BACKGROUND: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. RESULTS: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. CONCLUSIONS: PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download.

BACKGROUND: Biologists generally interrogate genomics data using web-based genome browsers that have limited analytical potential. New generation genome browsers such as the Integrated Genome Browser (IGB) have largely overcome this limitation and permit customized analyses to be implemented using plugins. We illustrate the use of a plugin for IGB that exploits advanced visualization techniques to integrate the analysis of genomics data with network and structural approaches. RESULTS: We show how visualization technologies that combine both genomics and network biology can facilitate the selection of the key amino acid contacts from protein-protein and protein-drug interactions. Starting from the MDM2-P53 interaction, which is a high-value target for cancer therapy, and Nutlin, the parent small molecule of an MDM2 antagonist that is currently in clinical trials, we show that this method can be generalized to analyze how drugs and mutations can interfere with both protein-protein and drug-protein networks. We illustrate this point by two additional use-cases exploring the molecular basis of tamoxifen side effects and of drug resistance in chronic myeloid leukemia patients. CONCLUSIONS: Combined network and structure biology approaches provide key insights into both the genetic and the edgetic roles of variants in diseases. 3D interactomes facilitate the identification of disease-relevant interactions that can then be specifically targeted by drugs. Recent advances in molecular interaction and structure visualization tools have greatly simplified the mapping of mutated residues to molecular interaction interfaces. Such approaches can now also be integrated with genome visualization tools to enable comparative analyses of interaction contacts.

BACKGROUND: The increasing availability of resequencing data has led to a better understanding of the most important genes in cancer development. Nevertheless, the mutational landscape of many tumor types is heterogeneous and encompasses a long tail of potential driver genes that are systematically excluded by currently available methods due to the low frequency of their mutations. We developed LowMACA (Low frequency Mutations Analysis via Consensus Alignment), a method that combines the mutations of various proteins sharing the same functional domains to identify conserved residues that harbor clustered mutations in multiple sequence alignments. LowMACA is designed to visualize and statistically assess potential driver genes through the identification of their mutational hotspots. RESULTS: We analyzed the Ras superfamily exploiting the known driver mutations of the trio K-N-HRAS, identifying new putative driver mutations and genes belonging to less known members of the Rho, Rab and Rheb subfamilies. Furthermore, we applied the same concept to a list of known and candidate driver genes, and observed that low confidence genes show similar patterns of mutation compared to high confidence genes of the same protein family. CONCLUSIONS: LowMACA is a software for the identification of gain-of-function mutations in putative oncogenic families, increasing the amount of information on functional domains and their possible role in cancer. In this context LowMACA emphasizes the role of genes mutated at low frequency otherwise undetectable by classical single gene analysis. LowMACA is an R package available at http://www.bioconductor.org/packages/release/bioc/html/LowMACA.html. It is also available as a GUI standalone downloadable at: https://cgsb.genomics.iit.it/wiki/projects/LowMACA.

UNLABELLED: Prioritization of candidate genes emanating from large-scale screens requires integrated analyses at the genomics, molecular, network and structural biology levels. We have extended the Integrated Genome Browser (IGB) to facilitate these tasks. The graphical user interface greatly simplifies building disease networks and zooming in at atomic resolution to identify variations in molecular complexes that may affect molecular interactions in the context of genomic data. All results are summarized in genome tracks and can be visualized and analyzed at the transcript level. AVAILABILITY AND IMPLEMENTATION: The MI Bundle is a plugin for the IGB. The plugin, help, video and tutorial are available at http://cru.genomics.iit.it/igbmibundle/ and https://github.com/CRUiit/igb-mi-bundle/wiki. The source code is released under the Apache License, Version 2. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Life-science laboratories make increasing use of Next Generation Sequencing (NGS) for studying bio-macromolecules and their interactions. Array-based methods for measuring gene expression or protein-DNA interactions are being replaced by RNA-Seq and ChIP-Seq. Sequencing is generally performed by specialized facilities that have to keep track of sequencing requests, trace samples, ensure quality and make data available according to predefined privileges. An integrated tool helps to troubleshoot problems, to maintain a high quality standard, to reduce time and costs. Commercial and non-commercial tools called LIMS (Laboratory Information Management Systems) are available for this purpose. However, they often come at prohibitive cost and/or lack the flexibility and scalability needed to adjust seamlessly to the frequently changing protocols employed. In order to manage the flow of sequencing data produced at the Genomic Unit of the Italian Institute of Technology (IIT), we developed SMITH (Sequencing Machine Information Tracking and Handling). METHODS: SMITH is a web application with a MySQL server at the backend. Wet-lab scientists of the Centre for Genomic Science and database experts from the Politecnico of Milan in the context of a Genomic Data Model Project developed SMITH. The data base schema stores all the information of an NGS experiment, including the descriptions of all protocols and algorithms used in the process. Notably, an attribute-value table allows associating an unconstrained textual description to each sample and all the data produced afterwards. This method permits the creation of metadata that can be used to search the database for specific files as well as for statistical analyses. RESULTS: SMITH runs automatically and limits direct human interaction mainly to administrative tasks. SMITH data-delivery procedures were standardized making it easier for biologists and analysts to navigate the data. Automation also helps saving time. The workflows are available through an API provided by the workflow management system. The parameters and input data are passed to the workflow engine that performs de-multiplexing, quality control, alignments, etc. CONCLUSIONS: SMITH standardizes, automates, and speeds up sequencing workflows. Annotation of data with key-value pairs facilitates meta-analysis.

BACKGROUND: Modern genomic technologies produce large amounts of data that can be mapped to specific regions in the genome. Among the first steps in interpreting the results is annotation of genomic regions with known features such as genes, promoters, CpG islands etc. Several tools have been published to perform this task. However, using these tools often requires a significant amount of bioinformatics skills and/or downloading and installing dedicated software. RESULTS: Here we present AnnotateGenomicRegions, a web application that accepts genomic regions as input and outputs a selection of overlapping and/or neighboring genome annotations. Supported organisms include human (hg18, hg19), mouse (mm8, mm9, mm10), zebrafish (danRer7), and Saccharomyces cerevisiae (sacCer2, sacCer3). AnnotateGenomicRegions is accessible online on a public server or can be installed locally. Some frequently used annotations and genomes are embedded in the application while custom annotations may be added by the user. CONCLUSIONS: The increasing spread of genomic technologies generates the need for a simple-to-use annotation tool for genomic regions that can be used by biologists and bioinformaticians alike. AnnotateGenomicRegions meets this demand. AnnotateGenomicRegions is an open-source web application that can be installed on any personal computer or institute server. AnnotateGenomicRegions is available at: http://cru.genomics.iit.it/AnnotateGenomicRegions.

BACKGROUND: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome. RESULTS: In order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation. CONCLUSION: The resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database.