A survey of tools for variant analysis of next-generation genome sequencing data.

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Mutations in ROGDI Cause Kohlschütter-Tönz Syndrome.

Kohlschütter-Tönz syndrome (KTS) is an autosomal-recessive disease characterized by the combination of epilepsy, psychomotor regression, and amelogenesis imperfecta. The molecular basis has not yet been elucidated. Here, we report that KTS is caused by mutations in ROGDI. Using a combination of autozygosity mapping and exome sequencing, we identified a homozygous frameshift deletion, c.229_230del (p.Leu77Alafs(∗)64), in ROGDI in two affected individuals from a consanguineous family. Molecular studies in two additional KTS-affected individuals from two unrelated Austrian and Swiss families revealed homozygosity for nonsense mutation c.286C>T (p.Gln96(∗)) and compound heterozygosity for the splice-site mutations c.531+5G>C and c.532-2A>T in ROGDI, respectively. The latter mutation was also found to be heterozygous in the mother of the Swiss affected individual in whom KTS was reported for the first time in 1974. ROGDI is highly expressed throughout the brain and other organs, but its function is largely unknown. Possible interactions with DISC1, a protein involved in diverse cytoskeletal functions, have been suggested. Our finding that ROGDI mutations cause KTS indicates that the protein product of this gene plays an important role in neuronal development as well as amelogenesis.

Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages.

BACKGROUND: Cancer immunotherapy has recently entered a remarkable renaissance phase with the approval of several agents for treatment. Cancer treatment platforms have demonstrated profound tumor regressions including complete cure in patients with metastatic cancer. Moreover, technological advances in next-generation sequencing (NGS) as well as the development of devices for scanning whole-slide bioimages from tissue sections and image analysis software for quantitation of tumor-infiltrating lymphocytes (TILs) allow, for the first time, the development of personalized cancer immunotherapies that target patient specific mutations. However, there is currently no bioinformatics solution that supports the integration of these heterogeneous datasets. RESULTS: We have developed a bioinformatics platform - Personalized Oncology Suite (POS) - that integrates clinical data, NGS data and whole-slide bioimages from tissue sections. POS is a web-based platform that is scalable, flexible and expandable. The underlying database is based on a data warehouse schema, which is used to integrate information from different sources. POS stores clinical data, genomic data (SNPs and INDELs identified from NGS analysis), and scanned whole-slide images. It features a genome browser as well as access to several instances of the bioimage management application Bisque. POS provides different visualization techniques and offers sophisticated upload and download possibilities. The modular architecture of POS allows the community to easily modify and extend the application. CONCLUSIONS: The web-based integration of clinical, NGS, and imaging data represents a valuable resource for clinical researchers and future application in medical oncology. POS can be used not only in the context of cancer immunology but also in other studies in which NGS data and images of tissue sections are generated. The application is open-source and can be downloaded at http://www.icbi.at/POS.

QuACN: an R package for analyzing complex biological networks quantitatively.

MOTIVATION: Network-based representations of biological data have become an important way to analyze high-throughput data. To interpret the large amount of data that is produced by different high-throughput technologies, networks offer multifaceted aspects to analyze the data. As networks represent biological relationships within their structure, it turned out to be fruitful to analyze their topology. Therefore, we developed a freely available, open source R-package called Quantitative Analysis of Complex Networks (QuACN) to meet this challenge. QuACN contains different, information-theoretic and non-information-theoretic, topological network descriptors to analyze, classify and compare biological networks. AVAILABILITY: QuACN is freely available under LGPL via CRAN (http://cran.r-project.org/web/packages/QuACN/).

MEMOSys 2.0: an update of the bioinformatics database for genome-scale models and genomic data.

The MEtabolic MOdel research and development System (MEMOSys) is a versatile database for the management, storage and development of genome-scale models (GEMs). Since its initial release, the database has undergone major improvements, and the new version introduces several new features. First, the novel concept of derived models allows users to create model hierarchies that automatically propagate modifications along their order. Second, all stored components can now be easily enhanced with additional annotations that can be directly extracted from a supplied Systems Biology Markup Language (SBML) file. Third, the web application has been substantially revised and now features new query mechanisms, an easy search system for reactions and new link-out services to publicly available databases. Fourth, the updated database now contains 20 publicly available models, which can be easily exported into standardized formats for further analysis. Fifth, MEMOSys 2.0 is now also available as a fully configured virtual image and can be found online at http://www.icbi.at/memosys and http://memoys.i-med.ac.at. Database URL: http://memosys.i-med.ac.at.

SeqBench: integrated solution for the management and analysis of exome sequencing data.

BACKGROUND: The rapid development of next generation sequencing technologies, including the recently introduced benchtop sequencers, made sequencing affordable for smaller research institutions. A widely applied method to identify causing mutations of diseases is exome sequencing, which proved to be cost-effective and time-saving. FINDINGS: SeqBench, a web-based application, combines management and analysis of exome sequencing data into one solution. It provides a user friendly data acquisition module to facilitate comprehensive and intuitive data handling. SeqBench provides direct access to the analysis pipeline SIMPLEX, which can be configured to run locally, on a cluster, or in the cloud. Identified genomic variants are presented along with several functional annotations and can be interpreted in a family context. CONCLUSIONS: The web-based application SeqBench supports the management and analysis of exome sequencing data, is open-source and available at http://www.icbi.at/SeqBench.

[COMMODE] a large-scale database of molecular descriptors using compounds from PubChem.

BACKGROUND: Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging. RESULTS: This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at.

SIMPLEX: cloud-enabled pipeline for the comprehensive analysis of exome sequencing data.

In recent studies, exome sequencing has proven to be a successful screening tool for the identification of candidate genes causing rare genetic diseases. Although underlying targeted sequencing methods are well established, necessary data handling and focused, structured analysis still remain demanding tasks. Here, we present a cloud-enabled autonomous analysis pipeline, which comprises the complete exome analysis workflow. The pipeline combines several in-house developed and published applications to perform the following steps: (a) initial quality control, (b) intelligent data filtering and pre-processing, (c) sequence alignment to a reference genome, (d) SNP and DIP detection, (e) functional annotation of variants using different approaches, and (f) detailed report generation during various stages of the workflow. The pipeline connects the selected analysis steps, exposes all available parameters for customized usage, performs required data handling, and distributes computationally expensive tasks either on a dedicated high-performance computing infrastructure or on the Amazon cloud environment (EC2). The presented application has already been used in several research projects including studies to elucidate the role of rare genetic diseases. The pipeline is continuously tested and is publicly available under the GPL as a VirtualBox or Cloud image at http://simplex.i-med.ac.at; additional supplementary data is provided at http://www.icbi.at/exome.

A cellular automaton framework for infectious disease spread simulation.

In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated.The proposed framework is implemented using the programming language Java. The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease. Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated. It showed that geographical barriers may help to slow down the spread of an infectious disease, however, when an aggressive and deadly communicable disease spreads, only quarantine and controlled medical treatment are able to stop the outbreak, if at all.