The Grid 2: Blueprint for a New Computing Infrastructure

Understanding cloud-native applications after 10 years of cloud computing - A systematic mapping study

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

Review: High-performance computing to detect epistasis in genome scale data sets

Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

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Harnessing Big Data for Systems Pharmacology.

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional pilot project to create an atlas of genetic mutations responsible for cancer. One of the aims of this project is to develop an infrastructure for making the cancer related data publicly accessible, to enable cancer researchers anywhere around the world to make and validate important discoveries. However, data in the cancer genome atlas are organized as text archives in a set of directories. Devising bioinformatics applications to analyse such data is still challenging, as it requires downloading very large archives and parsing the relevant text files in order to collect the critical co-variates necessary for analysis. Furthermore, the various types of experimental results are not connected biologically, i.e. in order to truly exploit the data in the genome-wide context in which the TCGA project was devised, the data needs to be converted into a structured representation and made publicly available for remote querying and virtual integration. In this work, we address these issues by RDFizing data from TCGA and linking its elements to the Linked Open Data (LOD) Cloud. The outcome is the largest LOD data source (to the best of our knowledge) comprising of over 30 billion triples. This data source can be exploited through publicly available SPARQL endpoints, thus providing an easy-to-use, time-efficient, and scalable solution to accessing the Cancer Genome Atlas. We also describe showcases which are enabled by the new linked data representation of the Cancer Genome Atlas presented in this paper.

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Linked cancer genome atlas database

Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows

Background
Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community.

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Breaking the computational barriers of pairwise genome comparison.

The petabyte scale of the Big Data generation in bioinformatics requires the introduction of advanced computational techniques to enable efficient knowledge discovery from data. Many data analysis tools in bioinformatics have been developed but few have been adapted to take advantage of high performance computing (HPC) resources. For some of these tools, an attractive option is to employ a map/reduce strategy. On the other hand, Cloud Computing could be an important platform to run such tools in parallel because it provides on-demand, elastic computational resources. This paper presents a software suite for Microsoft Azure which supports legacy software (without modifications of the algorithm). We demonstrate the feasibility of the approach by benchmarking a typical bioinformatics tool, namely dotplot.

Enabling Large-Scale Bioinformatics Data Analysis with Cloud Computing

The field of metagenomics, defined as the direct genetic analysis of uncultured samples of genomes contained within an environmental sample, is gaining increasing popularity. The aim of studies of metagenomics is to determine the species present in an environmental community and identify changes in the abundance of species under different conditions. Current metagenomic analysis software faces bottlenecks due to the high computational load required to analyze complex samples. A computational open-source workflow has been developed for the detailed analysis of metagenomes. This workflow provides new tools and datafile specifications that facilitate the identification of differences in abundance of reads assigned to taxa (mapping), enables the detection of reads of low-abundance bacteria (producing evidence of their presence), provides new concepts for filtering spurious matches, etc. Innovative visualization ideas for improved display of metagenomic diversity are also proposed to better understand how reads are mapped to taxa. Illustrative examples are provided based on the study of two collections of metagenomes from faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity and their mothers. The proposed workflow provides an open environment that offers the opportunity to perform the mapping process using different reference databases. Additionally, this workflow shows the specifications of the mapping process and datafile formats to facilitate the development of new plugins for further post-processing. This open and extensible platform has been designed with the aim of enabling in-depth analysis of metagenomic samples and better understanding of the underlying biological processes.

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Computational workflow for the fine-grained analysis of metagenomic samples

The cost of obtaining genome-scale biomedical data continues to drop rapidly, with many hospitals and universities being able to produce large amounts of data. Managing and analysing such ever-growing datasets is becoming a crucial issue. Cloud computing presents a good solution to this problem due to its flexibility in obtaining computational resources. However, it is essential to allow end-users with no experience to take advantage of the cloud computing model of elastic resource provisioning. This paper presents a workflow that allows the end user to perform the core steps of a genome wide association analysis, consisting of 1) uploading raw data files to the cloud, 2) genotype calling, i.e. converting the raw data into information on genome variation 3) quality assessment of the genotype data and subsequent filtering based on user-provided parameters and 4) downloading the filtered genotype data in a standard file format. A number of steps in this process are computationally intensive; moreover, the computational resources involved vary greatly depending on the size of the study, from a few samples to a few thousand. Therefore cloud computing provides an ideal solution to this problem. The paper describes in detail how the pipeline was implemented, focussing on the cloud infrastructure and the different tools and software involved for software management and GUI construction. The key contributions of this paper are: 1a) iIt presents a real world application of cloud-computing to address a critical problem in biomedicine, . 2b) iIt provides a thorough description of how such a pipeline was implemented, in terms of data management and user-interface, such that the end-user does not need to focus on the computational aspect but can instead concentrate on data analysis and biological interpretation of results, and. c3) wWe show how cloud-computing can be used in a more effective way through the parallelisation of the appropriate parts of the pipeline.

Oscar Torreno

Papers

Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows

Breaking the computational barriers of pairwise genome comparison.

Enabling Large-Scale Bioinformatics Data Analysis with Cloud Computing

Computational workflow for the fine-grained analysis of metagenomic samples

A Cloud-based GWAS Analysis Pipeline for Clinical Researchers