A GPU-based high performance computing infrastructure for specialized NGS analyses

Motivation Recent advances in genome sequencing and biological data analysis technologies used in bioinformatics have led to a fast and continuous increase in biological data. The difficulty of managing the huge amounts of data currently available to researchers and the need to have results within a reasonable time have led to the use of distributed and parallel computing infrastructures for their analysis. Recently, bioinformatics is exploring new approaches based on the use of hardware accelerators as GPUs. From an architectural perspective, GPUs are very different from traditional CPUs. Indeed, the latter are devices composed of few cores with lots of cache memory able to handle a few software threads at a time. Conversely, the former are devices equipped with hundreds of cores able to handle thousands of threads simultaneously, so that a very high level of parallelism can be reached. Use of GPUs over the last years has resulted in significant increases in the performance of certain applications. Despite GPUs are increasingly used in bioinformatics most laboratories do not have access to a GPU cluster or server. In this context, it is very important to provide useful services to use these tools. Methods A web-based platform has been implemented with the aim to enable researchers to perform their analysis through dedicated GPU-based computing resources. To this end, a GPU cluster equipped with 16 NVIDIA Tesla k20c cards has been configured. The infrastructure has been built upon the Galaxy technology [1]. Galaxy is an open web-based scientific workflow system for data intensive biomedical research accessible to researchers that do not have programming experience. Let us recall that Galaxy provides a public server, but it does not provide support to GPU-computing. By default, Galaxy is designed to run jobs on local systems. However, it can also be configured to run jobs on a cluster. The front-end Galaxy application runs on a single server, but tools are run on cluster nodes instead. To this end, Galaxy supports different distributed resource managers with the aim to enable different clusters. For the specific case, in our opinion SLURM [2] represents the most suitable workload manager to manage and control jobs. SLURM is a highly configurable workload and resource manager and it is currently used on six of the ten most powerful computers in the world including the Piz Daint, utilizing over 5000 NVIDIA Tesla K20 GPUs. Results GPU-based tools [3] devised by our group for quality control of NGS data have been used to test the infrastructure. Initially, this activity required to make changes to the tools with the aim to optimize the parallelization on the cluster according to the adopted workload manager. Successively, the tools have been converted into web-based services accessible through the Galaxy portal. Abstract truncated at 3,000 characters - the full version is available in the pdf file.

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A GPU-based high performance computing infrastructure for specialized NGS analyses

10.7287/peerj.preprints.2175v1 ◽

2016 ◽

Author(s):

Andrea Manconi ◽

Marco Moscatelli ◽

Matteo Gnocchi ◽

Giuliano Armano ◽

Luciano Milanesi

Keyword(s):

Gpu Computing ◽

Scientific Workflow ◽

Biological Data ◽

Single Server ◽

Web Based ◽

Continuous Increase ◽

Gpu Cluster ◽

Workflow System ◽

The Galaxy ◽

Workload Manager

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Tripal and Galaxy: supporting reproducible scientific workflows for community biological databases

Database ◽

10.1093/database/baaa032 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Shawna Spoor ◽

Connor Wytko ◽

Brian Soto ◽

Ming Chen ◽

Abdullah Almsaeed ◽

...

Keyword(s):

Workflow Management ◽

Application Programming Interface ◽

Scientific Workflow ◽

Biological Databases ◽

Web Based ◽

Job Management ◽

The Galaxy ◽

Application Programming ◽

Computational Resources ◽

Programming Interface

Abstract Online biological databases housing genomics, genetic and breeding data can be constructed using the Tripal toolkit. Tripal is an open-source, internationally developed framework that implements FAIR data principles and is meant to ease the burden of constructing such websites for research communities. Use of a common, open framework improves the sustainability and manageability of such as site. Site developers can create extensions for their site and in turn share those extensions with others. One challenge that community databases often face is the need to provide tools for their users that analyze increasingly larger datasets using multiple software tools strung together in a scientific workflow on complicated computational resources. The Tripal Galaxy module, a ‘plug-in’ for Tripal, meets this need through integration of Tripal with the Galaxy Project workflow management system. Site developers can create workflows appropriate to the needs of their community using Galaxy and then share those for execution on their Tripal sites via automatically constructed, but configurable, web forms or using an application programming interface to power web-based analytical applications. The Tripal Galaxy module helps reduce duplication of effort by allowing site developers to spend time constructing workflows and building their applications rather than rebuilding infrastructure for job management of multi-step applications.

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From peer-reviewed to peer-reproduced: a role for data standards, models and computational workflows in scholarly publishing

10.1101/011973 ◽

2014 ◽

Author(s):

Alejandra Gonzalez-Beltran ◽

Peter Li ◽

Jun Zhao ◽

Maria Susana Avila-Garcia ◽

Marco Roos ◽

...

Keyword(s):

De Novo ◽

Scientific Information ◽

Scientific Paper ◽

Scientific Workflow ◽

Workflow System ◽

Design Variables ◽

The Galaxy ◽

Scientific Papers ◽

Conceptual Data ◽

Published Paper

Motivation: Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the proce- dures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanop- ublications (NP) and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler. Results: Executable workflows were developed using Galaxy which reproduced results that were con- sistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata. Availability: SOAPdenovo2 scripts, data and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy. cbiit.cuhk.edu.hk; and the representations using the ISA, NP and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. Contact: philippe.rocca- [email protected] and [email protected]

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Process-oriented ecological modeling approach and scientific workflow system

Biodiversity Science ◽

10.3724/sp.j.1003.2014.13267 ◽

2014 ◽

Vol 22 (3) ◽

pp. 277

Author(s):

Qiao Huijie ◽

Lin Congtian ◽

Wang Jiangning ◽

Ji Liqiang

Keyword(s):

Ecological Modeling ◽

Scientific Workflow ◽

Modeling Approach ◽

Workflow System ◽

Process Oriented

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Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy

GigaScience ◽

10.1093/gigascience/giaa049 ◽

2020 ◽

Vol 9 (5) ◽

Author(s):

Katarzyna Murat ◽

Björn Grüning ◽

Paulina Wiktoria Poterlowicz ◽

Gillian Westgate ◽

Desmond J Tobin ◽

...

Keyword(s):

Dna Methylation ◽

Association Studies ◽

Methylation Analysis ◽

Web Based ◽

Array Platform ◽

Complex Evaluation ◽

The Galaxy ◽

Population Epigenetics ◽

Analyse Data ◽

Dna Methylation Analysis

Abstract Background Infinium Human Methylation BeadChip is an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome based on Illumina’s bead technology and is one of the most common techniques used in epigenome-wide association studies. Finding associations between epigenetic variation and phenotype is a significant challenge in biomedical research. The newest version, HumanMethylationEPIC, quantifies the DNA methylation level of 850,000 CpG sites, while the previous versions, HumanMethylation450 and HumanMethylation27, measured >450,000 and 27,000 loci, respectively. Although a number of bioinformatics tools have been developed to analyse this assay, they require some programming skills and experience in order to be usable. Results We have developed a pipeline for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Human Methylation BeadChip. Our tool is integrated into Galaxy (http://galaxyproject.org), a web-based platform. This allows users to analyse data from the Infinium Human Methylation BeadChip in the easiest possible way. Conclusions The pipeline provides a group of integrated analytical methods wrapped into an easy-to-use interface. Our tool is available from the Galaxy ToolShed, GitHub repository, and also as a Docker image. The aim of this project is to make Infinium Human Methylation BeadChip analysis more flexible and accessible to everyone.

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USMI Galaxy Demonstrator (UGD): a collection of tools to integrate microorganisms information

10.7287/peerj.preprints.2766v1 ◽

2017 ◽

Author(s):

Daniele Pierpaolo Colobraro ◽

Paolo Romano

Keyword(s):

Service Providers ◽

Basic Information ◽

Unique Identifier ◽

Web Based ◽

Representational State Transfer ◽

Biological Resource Centres ◽

The Galaxy ◽

Science Domain ◽

On Line ◽

Strain Number

Due to the fragmentation of microbial information and the several branch of human activities encompassed by microorganism applications, a comprehensive approach for merging information on microbes is needed. Although on line service providers collect several data on microorganisms and provide services for microbial Biological Resource Centres (mBRCs), such services are still limited both in contents and aims. The USMI Galaxy Demonstrator (UGD), an implementation of the Galaxy framework exploiting the XML-based Microbiological Common Language (MCL), is meant to support researchers to make an integrated access to enriched information from microbial catalogues, as well as to help mBRC curators in validating and enriching the contents of their catalogues. Researchers and mBRC curators may exploit the UGD to avoid manual, potentially long, searches on the web and to identify and select microorganisms of interest. UGD tools are written in Python, version 2.7. They allow to enrich the basic information provided by catalogues with related taxonomy, literature, sequence and chemical compound data retrieved from some of the main databases on the basis of the strain number, i.e. the unique identifier for a given culture, and the species names. The data is retrieved by querying database Web Services using either the Simple Object Access Protocol (SOAP) or the Representational State Transfer (REST) access protocols. The MCL format provides a versatile way to archive and exchange data among mBRCs. Galaxy is a well-known, open, web-based platform which offers many tools to retrieve, manage and analyze different kind of information arising from any life science domain. By exploiting Galaxy flexibility,UGD implements some tools and workflows that can be used to find and integrate several information on microorganisms. UGD tools integrate basic information which may support mBRC staff in the insertion of all fundamental strain information in a proper format allowing integration and interoperability with external databases. They also extend the output by adding information on source materials, including species and strain numbers, and retrieve associated microorganisms which use a compound or an enzyme in whatever metabolic pathway by returning the accession number, synonyms, links to external databases, taxon name, and strain number of the requested molecule.

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Workspace – a Scientific Workflow System with commercial impact

El Sawah, S. (ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation. ◽

10.36334/modsim.2019.d2.oakes ◽

2019 ◽

Keyword(s):

Scientific Workflow ◽

Workflow System

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Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

PeerJ Computer Science ◽

10.7717/peerj-cs.90 ◽

2016 ◽

Vol 2 ◽

pp. e90 ◽

Cited By ~ 24

Author(s):

Ranko Gacesa ◽

David J. Barlow ◽

Paul F. Long

Keyword(s):

Machine Learning ◽

Sequence Data ◽

Biological Data ◽

Biological Databases ◽

Web Based ◽

Physiological Functions ◽

Link Type ◽

Venom Toxins ◽

Venomous Animals ◽

Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

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