Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.

MOTIVATION: Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. RESULTS: We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION: The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Workflow managers for scientific analysis provide a high-level programming platform facilitating standardization, automation, collaboration and access to sophisticated computing resources. The Galaxy workflow manager provides a prime example of this type of platform. As compositions of simpler tools, workflows effectively comprise specialized computer programs implementing often very complex analysis procedures. To date, no simple way to automatically test Galaxy workflows and ensure their correctness has appeared in the literature. RESULTS: With wft4galaxy we offer a tool to bring automated testing to Galaxy workflows, making it feasible to bring continuous integration to their development and ensuring that defects are detected promptly. wft4galaxy can be easily installed as a regular Python program or launched directly as a Docker container-the latter reducing installation effort to a minimum. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/phnmnl/wft4galaxy under the Academic Free License v3.0. CONTACT: [email protected].

The adoption of Big Data technologies can potentially boost the scalability of data-driven biology and health workflows by orders of magnitude. Consider, for instance, that technologies in the Hadoop ecosystem have been successfully used in data-driven industry to scale their processes to levels much larger than any biological-or health-driven work attempted thus far. In this work we demonstrate the scalability of a sequence alignment pipeline based on technologies from the Hadoop ecosystem - namely, Apache Flink and Hadoop MapReduce, both running on the distributed Apache YARN platform. Unlike previous work, our pipeline starts processing directly from the raw BCL data produced by Illumina sequencers. A Flink-based distributed algorithm reconstructs reads from the Illumina BCL data, and then demultiplexes them - analogously to the bcl2fastq2 program provided by Illumina. Subsequently, the BWA-MEM-based distributed aligner from the Seal project is used to perform read mapping on the YARN platform. While the standard programs by Illumina and BWA-MEM are limited to shared-memory parallelism (multi-threading), our solution is completely distributed and can scale across a large number of computing nodes. Results show excellent pipeline scalability, linear in the number of nodes. In addition, this approach automatically benefits from the robustness to hardware failure and transient cluster problems provided by the YARN platform, as well as the scalability of the Hadoop Distributed File System. Moreover, this YARN-based approach complements the up-and-coming version 4 of the GATK toolkit, which is based on Spark and therefore can run on YARN. Together, they can be used to form a scalable complete YARN-based variant calling pipeline for Illumina data, which will be further improved with the arrival of distributed in-memory filesystem technology such as Apache Arrow, thus removing the need to write intermediate data to disk.

SUMMARY: BioBlend.objects is a new component of the BioBlend package, adding an object-oriented interface for the Galaxy REST-based application programming interface. It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets and workflows. AVAILABILITY AND IMPLEMENTATION: BioBlend.objects is available online at https://github.com/afgane/bioblend. The new object-oriented API is implemented by the galaxy/objects subpackage.

SUMMARY: Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPig's scalability over many computing nodes and illustrate its use with example scripts. AVAILABILITY AND IMPLEMENTATION: Available under the open source MIT license at http://sourceforge.net/projects/seqpig/

SUMMARY: SEAL is a scalable tool for short read pair mapping and duplicate removal. It computes mappings that are consistent with those produced by BWA and removes duplicates according to the same criteria employed by Picard MarkDuplicates. On a 16-node Hadoop cluster, it is capable of processing about 13 GB per hour in map+rmdup mode, while reaching a throughput of 19 GB per hour in mapping-only mode. AVAILABILITY: SEAL is available online at http://biodoop-seal.sourceforge.net/.