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Cabello-Aguilar S, Vendrell JA, Solassol J. A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology. Curr Issues Mol Biol 2023; 45:9737-9752. [PMID: 38132454 PMCID: PMC10741970 DOI: 10.3390/cimb45120608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.
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Affiliation(s)
- Simon Cabello-Aguilar
- Montpellier BioInformatics for Clinical Diagnosis (MOBIDIC), Molecular Medicine and Genomics Platform (PMMG), CHU Montpellier, 34295 Montpellier, France
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Julie A. Vendrell
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
| | - Jérôme Solassol
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France; (J.A.V.); (J.S.)
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2
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Khan AQ, Nikolov N, Matskin M, Prodan R, Roman D, Sahin B, Bussler C, Soylu A. Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines. SENSORS (BASEL, SWITZERLAND) 2023; 23:564. [PMID: 36679360 PMCID: PMC9863399 DOI: 10.3390/s23020564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.
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Affiliation(s)
- Akif Quddus Khan
- Department of Computer Science, Norwegian University of Science and Technology—NTNU, 2815 Gjøvik, Norway
| | | | - Mihhail Matskin
- Department of Computer Science, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden
| | - Radu Prodan
- Department of Information Technology, University of Klagenfurt, 9020 Klagenfurt, Austria
| | | | - Bekir Sahin
- Logistics Management, National University of Science and Technology, 111 Sohar, Oman
| | | | - Ahmet Soylu
- Department of Computer Science, OsloMet—Oslo Metropolitan University, 0167 Oslo, Norway
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3
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Garzón W, Benavides L, Gaignard A, Redon R, Südholt M. A taxonomy of tools and approaches for distributed genomic analyses. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022] Open
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4
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Krämer M, Würz HM, Altenhofen C. Executing cyclic scientific workflows in the cloud. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2021. [DOI: 10.1186/s13677-021-00229-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractWe present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.
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5
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El-Khishin DA, Ageez A, Saad ME, Ibrahim A, Shokrof M, Hassan LR, Abouelhoda MI. Sequencing and assembly of the Egyptian buffalo genome. PLoS One 2020; 15:e0237087. [PMID: 32813723 PMCID: PMC7437910 DOI: 10.1371/journal.pone.0237087] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 07/20/2020] [Indexed: 01/09/2023] Open
Abstract
Water buffalo (Bubalus bubalis) is an important source of meat and milk in countries with relatively warm weather. Compared to the cattle genome, a little has been done to reveal its genome structure and genomic traits. This is due to the complications stemming from the large genome size, the complexity of the genome, and the high repetitive content. In this paper, we introduce a high-quality draft assembly of the Egyptian water buffalo genome. The Egyptian breed is used as a dual purpose animal (milk/meat). It is distinguished by its adaptability to the local environment, quality of feed changes, as well as its high resistance to diseases. The genome assembly of the Egyptian water buffalo has been achieved using a reference-based assembly workflow. Our workflow significantly reduced the computational complexity of the assembly process, and improved the assembly quality by integrating different public resources. We also compared our assembly to the currently available draft assemblies of water buffalo breeds. A total of 21,128 genes were identified in the produced assembly. A list of milk virgin-related genes; milk pregnancy-related genes; milk lactation-related genes; milk involution-related genes; and milk mastitis-related genes were identified in the assembly. Our results will significantly contribute to a better understanding of the genetics of the Egyptian water buffalo which will eventually support the ongoing breeding efforts and facilitate the future discovery of genes responsible for complex processes of dairy, meat production and disease resistance among other significant traits.
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Affiliation(s)
- Dina A. El-Khishin
- Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt
- * E-mail:
| | - Amr Ageez
- Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt
- Faculty of Biotechnology, MSA University, October City, Egypt
| | - Mohamed E. Saad
- Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt
- Department of Biology, Taibah University, Almadinah Almonawarah, KSA
| | - Amr Ibrahim
- Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt
| | - Moustafa Shokrof
- Centre for Informatics Sciences, Nile University Giza, October city, Egypt
- Department of Computer Science, University of California at Davis, Davis, CA, United States of America
| | - Laila R. Hassan
- Animal Production Research Institute, Agricultural Research Center (ARC), Ministry of Agriculture and Land Reclamation, Giza, Egypt
| | - Mohamed I. Abouelhoda
- Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt
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6
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Ahmed AE, Heldenbrand J, Asmann Y, Fadlelmola FM, Katz DS, Kendig K, Kendzior MC, Li T, Ren Y, Rodriguez E, Weber MR, Wozniak JM, Zermeno J, Mainzer LS. Managing genomic variant calling workflows with Swift/T. PLoS One 2019; 14:e0211608. [PMID: 31287816 PMCID: PMC6615596 DOI: 10.1371/journal.pone.0211608] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 06/08/2019] [Indexed: 12/30/2022] Open
Abstract
Bioinformatics research is frequently performed using complex workflows with multiple steps, fans, merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Scientific workflow management systems could help with that. Many are now being proposed, but is there yet the “best” workflow management system for bioinformatics? Such a system would need to satisfy numerous, sometimes conflicting requirements: from ease of use, to seamless deployment at peta- and exa-scale, and portability to the cloud. We evaluated Swift/T as a candidate for such role by implementing a primary genomic variant calling workflow in the Swift/T language, focusing on workflow management, performance and scalability issues that arise from production-grade big data genomic analyses. In the process we introduced novel features into the language, which are now part of its open repository. Additionally, we formalized a set of design criteria for quality, robust, maintainable workflows that must function at-scale in a production setting, such as a large genomic sequencing facility or a major hospital system. The use of Swift/T conveys two key advantages. (1) It operates transparently in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.), thus a single workflow is trivially portable across numerous clusters. (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, which makes it easy to maintain and to request resources optimal for each stage of the pipeline. While Swift/T’s data-level parallelism eliminates the need to code parallel analysis of multiple samples, it does make debugging more difficult, as is common for implicitly parallel code. Nonetheless, the language gives users a powerful and portable way to scale up analyses in many computing architectures. The code for our implementation of a variant calling workflow using Swift/T can be found on GitHub at https://github.com/ncsa/Swift-T-Variant-Calling, with full documentation provided at http://swift-t-variant-calling.readthedocs.io/en/latest/.
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Affiliation(s)
- Azza E. Ahmed
- Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Jacob Heldenbrand
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Yan Asmann
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida, United States of America
| | - Faisal M. Fadlelmola
- Centre for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Daniel S. Katz
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Katherine Kendig
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Matthew C. Kendzior
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Tiffany Li
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Yingxue Ren
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida, United States of America
| | - Elliott Rodriguez
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Matthew R. Weber
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Justin M. Wozniak
- Argonne National Laboratory, Argonne, Illinois, United States of America
| | - Jennie Zermeno
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - Liudmila S. Mainzer
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
- * E-mail:
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7
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Karim MR, Michel A, Zappa A, Baranov P, Sahay R, Rebholz-Schuhmann D. Improving data workflow systems with cloud services and use of open data for bioinformatics research. Brief Bioinform 2019; 19:1035-1050. [PMID: 28419324 PMCID: PMC6169675 DOI: 10.1093/bib/bbx039] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Indexed: 11/22/2022] Open
Abstract
Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.
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Affiliation(s)
- Md Rezaul Karim
- Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
| | - Audrey Michel
- School of Biochemistry and Cell Biology, University College Cork, Ireland
| | - Achille Zappa
- Insight Centre for Data Analytics, National University of Ireland Galway, Dangan, Galway, Ireland
| | - Pavel Baranov
- School of Biochemistry and Cell Biology, University College Cork, Ireland
| | - Ratnesh Sahay
- Semantics in eHealth and Life Sciences (SeLS), Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
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8
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Bramer LM, Stratton KG, White AM, Bleeker AH, Kobold MA, Waters KM, Metz TO, Rodland KD, Webb-Robertson BJM. P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data. J Proteome Res 2019; 18:1426-1432. [PMID: 30667224 DOI: 10.1021/acs.jproteome.8b00840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.
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Affiliation(s)
- Lisa M Bramer
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Kelly G Stratton
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Amanda M White
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Ameila H Bleeker
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Markus A Kobold
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Katrina M Waters
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Thomas O Metz
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
| | - Karin D Rodland
- Biological Sciences Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States.,Department of Cell, Developmental, and Cancer Biology , Oregon Health & Science University , Portland , Oregon 97221 , United States
| | - Bobbie-Jo M Webb-Robertson
- Computing & Analytics Division , Pacific Northwest National Laboratory , 902 Battelle Boulevard , Richland , Washington 99352 , United States
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9
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Baichoo S, Souilmi Y, Panji S, Botha G, Meintjes A, Hazelhurst S, Bendou H, Beste ED, Mpangase PT, Souiai O, Alghali M, Yi L, O'Connor BD, Crusoe M, Armstrong D, Aron S, Joubert F, Ahmed AE, Mbiyavanga M, Heusden PV, Magosi LE, Zermeno J, Mainzer LS, Fadlelmola FM, Jongeneel CV, Mulder N. Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics. BMC Bioinformatics 2018; 19:457. [PMID: 30486782 PMCID: PMC6264621 DOI: 10.1186/s12859-018-2446-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 10/23/2018] [Indexed: 12/30/2022] Open
Abstract
Background The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. Results H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. Conclusion The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.
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Affiliation(s)
- Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius.
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, South Australia, Australia
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Scott Hazelhurst
- School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Eugene de Beste
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Phelelani T Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis, University Tunis El manar, Tunis, Tunisia.,Institut Superieur des Technologies Medicales de Tunis, University Tunis El manar, Tunis, Tunisia
| | - Mustafa Alghali
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical & Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Brian D O'Connor
- Genomics Institute, University of California, Santa Cruz, California, USA
| | - Michael Crusoe
- Common Workflow Language project, Software Freedom Conservancy, New York City, NY, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - Azza E Ahmed
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical & Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Mamana Mbiyavanga
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Peter van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Lerato E Magosi
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jennie Zermeno
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Faisal M Fadlelmola
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - C Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
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10
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Ko G, Kim PG, Yoon J, Han G, Park SJ, Song W, Lee B. Closha: bioinformatics workflow system for the analysis of massive sequencing data. BMC Bioinformatics 2018; 19:43. [PMID: 29504905 PMCID: PMC5836837 DOI: 10.1186/s12859-018-2019-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND While next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis. RESULTS To address this challenge, we developed a cloud-based workflow management system, Closha, to provide fast and cost-effective analysis of massive genomic data. We implemented complex workflows making optimal use of high-performance computing clusters. Closha allows users to create multi-step analyses using drag and drop functionality and to modify the parameters of pipeline tools. Users can also import the Galaxy pipelines into Closha. Closha is a hybrid system that enables users to use both analysis programs providing traditional tools and MapReduce-based big data analysis programs simultaneously in a single pipeline. Thus, the execution of analytics algorithms can be parallelized, speeding up the whole process. We also developed a high-speed data transmission solution, KoDS, to transmit a large amount of data at a fast rate. KoDS has a file transfer speed of up to 10 times that of normal FTP and HTTP. The computer hardware for Closha is 660 CPU cores and 800 TB of disk storage, enabling 500 jobs to run at the same time. CONCLUSIONS Closha is a scalable, cost-effective, and publicly available web service for large-scale genomic data analysis. Closha supports the reliable and highly scalable execution of sequencing analysis workflows in a fully automated manner. Closha provides a user-friendly interface to all genomic scientists to try to derive accurate results from NGS platform data. The Closha cloud server is freely available for use from http://closha.kobic.re.kr/ .
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Affiliation(s)
| | | | - Jongcheol Yoon
- Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea
| | - Gukhee Han
- Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea
| | - Seong-Jin Park
- Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea
| | - Wangho Song
- Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea
| | - Byungwook Lee
- Korean BioInformation Center (KOBIC), KRIBB, 125 Gwahangno, Yuseong-gu, Daejeon, 34141, South Korea.
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11
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Elastic Scheduling of Scientific Workflows under Deadline Constraints in Cloud Computing Environments. FUTURE INTERNET 2018. [DOI: 10.3390/fi10010005] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
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12
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biochem4j: Integrated and extensible biochemical knowledge through graph databases. PLoS One 2017; 12:e0179130. [PMID: 28708831 PMCID: PMC5510799 DOI: 10.1371/journal.pone.0179130] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 05/24/2017] [Indexed: 01/17/2023] Open
Abstract
Biologists and biochemists have at their disposal a number of excellent, publicly available data resources such as UniProt, KEGG, and NCBI Taxonomy, which catalogue biological entities. Despite the usefulness of these resources, they remain fundamentally unconnected. While links may appear between entries across these databases, users are typically only able to follow such links by manual browsing or through specialised workflows. Although many of the resources provide web-service interfaces for computational access, performing federated queries across databases remains a non-trivial but essential activity in interdisciplinary systems and synthetic biology programmes. What is needed are integrated repositories to catalogue both biological entities and-crucially-the relationships between them. Such a resource should be extensible, such that newly discovered relationships-for example, those between novel, synthetic enzymes and non-natural products-can be added over time. With the introduction of graph databases, the barrier to the rapid generation, extension and querying of such a resource has been lowered considerably. With a particular focus on metabolic engineering as an illustrative application domain, biochem4j, freely available at http://biochem4j.org, is introduced to provide an integrated, queryable database that warehouses chemical, reaction, enzyme and taxonomic data from a range of reliable resources. The biochem4j framework establishes a starting point for the flexible integration and exploitation of an ever-wider range of biological data sources, from public databases to laboratory-specific experimental datasets, for the benefit of systems biologists, biosystems engineers and the wider community of molecular biologists and biological chemists.
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13
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Costa RL, Gadelha L, Ribeiro-Alves M, Porto F. GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis. PeerJ 2017; 5:e3509. [PMID: 28695067 PMCID: PMC5501156 DOI: 10.7717/peerj.3509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 06/06/2017] [Indexed: 12/28/2022] Open
Abstract
There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.
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Affiliation(s)
- Raquel L. Costa
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
- National Institute of Cancer (INCA), Rio de Janeiro, RJ, Brazil
| | - Luiz Gadelha
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Laboratory of Clinical Research in DST- AIDS, National Institute of Infectology Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fábio Porto
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
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14
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Te Pas MFW, Madsen O, Calus MPL, Smits MA. The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype. Int J Mol Sci 2017; 18:E472. [PMID: 28241430 PMCID: PMC5344004 DOI: 10.3390/ijms18020472] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 02/02/2017] [Accepted: 02/13/2017] [Indexed: 02/06/2023] Open
Abstract
With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.
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Affiliation(s)
- Marinus F W Te Pas
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700AH Wageningen, The Netherlands.
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University, 6700AH Wageningen, The Netherlands.
| | - Mario P L Calus
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700AH Wageningen, The Netherlands.
| | - Mari A Smits
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700AH Wageningen, The Netherlands.
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15
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Saeb AT, Abouelhoda M, Selvaraju M, Althawadi SI, Mutabagani M, Adil M, Al Hokail A, Tayeb HT. The Use of Next-Generation Sequencing in the Identification of a Fastidious Pathogen: A Lesson From a Clinical Setup. Evol Bioinform Online 2017; 12:1176934316686072. [PMID: 28469373 PMCID: PMC5395265 DOI: 10.1177/1176934316686072] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 10/20/2016] [Indexed: 01/25/2023] Open
Abstract
Clostridium haemolyticum is the causal agent of bacillary hemoglobinuria in cattle, goat, sheep, and ruminants. In this study, we report the first recorded human-infecting C. haemolyticum strain collected from an 18-year-old woman diagnosed with acute lymphoblastic leukemia. After failure of traditional techniques, only next-generation sequencing (NGS) technology in combination with bioinformatics, phylogenetic, and pathogenomics analyses revealed that our King Faisal Specialist Hospital and Research Center (KFSHRC) bacterial isolate belongs to C. haemolyticum species. KFSHRC isolate is composed of 1 chromosome and 4 plasmids. The total genome size is estimated to be 2.7 Mbp with a low GC content of 28.02%. Comparative pathogenomics analysis showed that C. haemolyticum KFSHRC isolate is a potential virulent pathogenic bacterium as it possesses the virulence factors necessary to establish an infection, acquire essential nutrients, resist antimicrobial agents, and tolerate hostile conditions both in the human host and in its surrounding environment. These factors are included in the main chromosome in addition to novel recombination of the plasmids, and they could be the reason for the incidence of that human infection. This work demonstrated the importance of using NGS in medical microbiology for pathogen identification. It also demonstrates the importance of sequencing more microbial samples and sharing this information in public databases to facilitate the identification of pathogenic microbes with better accuracy.
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Affiliation(s)
- Amr Tm Saeb
- Genetics and Biotechnology Department, Strategic Center for Diabetes Research, College of Medicine, King Saud University, Riyadh, Saudi Arabia.,Saudi Human Genome Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Mohamed Abouelhoda
- Saudi Human Genome Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia.,Genetics Department, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Manojkumar Selvaraju
- Saudi Human Genome Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia.,Integrated Gulf Biosystems, Riyadh, Saudi Arabia
| | - Sahar I Althawadi
- Department of Pathology & Laboratory Medicine, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Maysoon Mutabagani
- Department of Pathology & Laboratory Medicine, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | | | - Abdullah Al Hokail
- Department of Medicine, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia.,Alfaisal University, Riyadh, Saudi Arabia
| | - Hamsa T Tayeb
- Saudi Human Genome Program, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia.,Genetics Department, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
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16
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MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants. BMC Bioinformatics 2017; 18:49. [PMID: 28107819 PMCID: PMC5248509 DOI: 10.1186/s12859-016-1454-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 12/24/2016] [Indexed: 12/28/2022] Open
Abstract
Background Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. Results In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. Conclusions MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.
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17
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Multi-level meta-workflows: new concept for regularly occurring tasks in quantum chemistry. J Cheminform 2016; 8:58. [PMID: 27818709 PMCID: PMC5073744 DOI: 10.1186/s13321-016-0169-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 10/04/2016] [Indexed: 11/10/2022] Open
Abstract
Background
In Quantum Chemistry, many tasks are reoccurring frequently, e.g. geometry optimizations, benchmarking series etc. Here, workflows can help to reduce the time of manual job definition and output extraction. These workflows are executed on computing infrastructures and may require large computing and data resources. Scientific workflows hide these infrastructures and the resources needed to run them. It requires significant efforts and specific expertise to design, implement and test these workflows. Significance Many of these workflows are complex and monolithic entities that can be used for particular scientific experiments. Hence, their modification is not straightforward and it makes almost impossible to share them. To address these issues we propose developing atomic workflows and embedding them in meta-workflows. Atomic workflows deliver a well-defined research domain specific function. Publishing workflows in repositories enables workflow sharing inside and/or among scientific communities. We formally specify atomic and meta-workflows in order to define data structures to be used in repositories for uploading and sharing them. Additionally, we present a formal description focused at orchestration of atomic workflows into meta-workflows. Conclusions We investigated the operations that represent basic functionalities in Quantum Chemistry, developed the relevant atomic workflows and combined them into meta-workflows. Having these workflows we defined the structure of the Quantum Chemistry workflow library and uploaded these workflows in the SHIWA Workflow Repository.Meta-workflows and embedded workflows in the template representation ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0169-8) contains supplementary material, which is available to authorized users.
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18
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Pinthong W, Muangruen P, Suriyaphol P, Mairiang D. A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model. PeerJ 2016; 4:e2248. [PMID: 27547555 PMCID: PMC4974928 DOI: 10.7717/peerj.2248] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 06/22/2016] [Indexed: 11/20/2022] Open
Abstract
Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.
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Affiliation(s)
- Watthanai Pinthong
- Department of Anatomy, Faculty of Medicine Siriraj Hospital, Mahidol University , Bangkok , Thailand
| | - Panya Muangruen
- Siriraj Information Technology Department, Faculty of Medicine Siriraj Hospital, Mahidol University , Bangkok, Thailand
| | - Prapat Suriyaphol
- Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University , Bangkok , Thailand
| | - Dumrong Mairiang
- Medical Biotechnology Research Laboratory, The National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand; Division of Dengue Hemorrhagic Fever Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
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19
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Satagopam V, Gu W, Eifes S, Gawron P, Ostaszewski M, Gebel S, Barbosa-Silva A, Balling R, Schneider R. Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases. BIG DATA 2016; 4:97-108. [PMID: 27441714 PMCID: PMC4932659 DOI: 10.1089/big.2015.0057] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.
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Affiliation(s)
- Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Serge Eifes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
- Information Technology for Translational Medicine (ITTM) S.A., Esch-Belval, Luxembourg
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Adriano Barbosa-Silva
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
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20
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de la Garza L, Veit J, Szolek A, Röttig M, Aiche S, Gesing S, Reinert K, Kohlbacher O. From the desktop to the grid: scalable bioinformatics via workflow conversion. BMC Bioinformatics 2016; 17:127. [PMID: 26968893 PMCID: PMC4788856 DOI: 10.1186/s12859-016-0978-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 03/03/2016] [Indexed: 01/04/2023] Open
Abstract
Background Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free —an aspect that could potentially drive away members of the scientific community. Results We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. Conclusions Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.
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Affiliation(s)
- Luis de la Garza
- Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany.
| | - Johannes Veit
- Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany
| | - Andras Szolek
- Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany
| | - Marc Röttig
- Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany
| | - Stephan Aiche
- Algorithmic Bioinformatics, Computer Science Institute, Freie Universität Berlin, Takustr. 9, Berlin, 14195, Germany
| | - Sandra Gesing
- College of Engineering, University of Notre Dame, 257 Fitzpatrick Hall, Notre Dame, 46556, IN, United States
| | - Knut Reinert
- Algorithmic Bioinformatics, Computer Science Institute, Freie Universität Berlin, Takustr. 9, Berlin, 14195, Germany
| | - Oliver Kohlbacher
- Center for Bioinformatics and Dept. of Computer Science, University of Tübingen, Sand 14, Tübingen, 72070, Germany
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21
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Ocaña K, de Oliveira D. Parallel computing in genomic research: advances and applications. Adv Appl Bioinform Chem 2015; 8:23-35. [PMID: 26604801 PMCID: PMC4655901 DOI: 10.2147/aabc.s64482] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities.
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Affiliation(s)
- Kary Ocaña
- National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, Brazil
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22
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Monat C, Tranchant-Dubreuil C, Kougbeadjo A, Farcy C, Ortega-Abboud E, Amanzougarene S, Ravel S, Agbessi M, Orjuela-Bouniol J, Summo M, Sabot F. TOGGLE: toolbox for generic NGS analyses. BMC Bioinformatics 2015; 16:374. [PMID: 26552596 PMCID: PMC4640241 DOI: 10.1186/s12859-015-0795-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 10/24/2015] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines. RESULTS TOGGLE (TOolbox for Generic nGs anaLysEs) is a suite of tools able to design pipelines that manage large sets of NGS softwares and utilities. Moreover, TOGGLE offers an easy way to manipulate the various options of the different softwares through the pipelines in using a single basic configuration file, which can be changed for each assay without having to change the code itself. We also describe one implementation of TOGGLE in a complete analysis pipeline designed for SNP discovery for large sets of genomic data, ready to use in different environments (from a single machine to HPC clusters). CONCLUSION TOGGLE speeds up the creation of robust pipelines with reliable log tracking and data flow, for a large range of analyses. Moreover, it enables Biologists to concentrate on the biological relevance of results, and change the experimental conditions easily. The whole code and test data are available at https://github.com/SouthGreenPlatform/TOGGLE .
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Affiliation(s)
- Cécile Monat
- UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France.
| | | | - Ayité Kougbeadjo
- UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France.
| | - Cédric Farcy
- UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France.
| | - Enrique Ortega-Abboud
- UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France.
| | | | - Sébastien Ravel
- UMR-BGPI CIRAD TA A-54/K, Campus International de Baillarguet, Montpellier Cedex 5, F-34398, France.
| | - Mawussé Agbessi
- UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France.
| | | | - Maryline Summo
- UMR AGAP CIRAD/INRA/SupAgro, TA A-108/03 - Avenue Agropolis, Montpellier Cedex 5, F-34398, France.
| | - François Sabot
- UMR DIADE IRD/UM, 911 Avenue Agropolis, Montpellier Cedex 5, F-34934, France.
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Souilmi Y, Lancaster AK, Jung JY, Rizzo E, Hawkins JB, Powles R, Amzazi S, Ghazal H, Tonellato PJ, Wall DP. Scalable and cost-effective NGS genotyping in the cloud. BMC Med Genomics 2015; 8:64. [PMID: 26470712 PMCID: PMC4608296 DOI: 10.1186/s12920-015-0134-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 09/11/2015] [Indexed: 12/20/2022] Open
Abstract
Background While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10’s of dollars. Results We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Conclusions Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0134-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yassine Souilmi
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA. .,Department of Biology, Mohamed Vth University, 4 Ibn Battouta Avenue, B.P: 1014RP, Rabat, Morocco.
| | - Alex K Lancaster
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA.
| | - Jae-Yoon Jung
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA.
| | - Ettore Rizzo
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 1, Pavia, 27100, Italy.
| | - Jared B Hawkins
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA.
| | - Ryan Powles
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA.
| | - Saaïd Amzazi
- Department of Biology, Mohamed Vth University, 4 Ibn Battouta Avenue, B.P: 1014RP, Rabat, Morocco.
| | - Hassan Ghazal
- Department of Biology, Mohamed First University, Oujda, Nador, Morocco.
| | - Peter J Tonellato
- Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Boston, MA, 02115, USA. .,Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA.
| | - Dennis P Wall
- Department of Pediatrics and Psychiatry (by courtesy), Division of Systems Medicine & Program in Biomedical Informatics, Stanford University, Stanford, CA, 94305, USA.
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24
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SeqMule: automated pipeline for analysis of human exome/genome sequencing data. Sci Rep 2015; 5:14283. [PMID: 26381817 PMCID: PMC4585643 DOI: 10.1038/srep14283] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 08/21/2015] [Indexed: 11/16/2022] Open
Abstract
Next-generation sequencing (NGS) technology has greatly helped us identify disease-contributory variants for Mendelian diseases. However, users are often faced with issues such as software compatibility, complicated configuration, and no access to high-performance computing facility. Discrepancies exist among aligners and variant callers. We developed a computational pipeline, SeqMule, to perform automated variant calling from NGS data on human genomes and exomes. SeqMule integrates computational-cluster-free parallelization capability built on top of the variant callers, and facilitates normalization/intersection of variant calls to generate consensus set with high confidence. SeqMule integrates 5 alignment tools, 5 variant calling algorithms and accepts various combinations all by one-line command, therefore allowing highly flexible yet fully automated variant calling. In a modern machine (2 Intel Xeon X5650 CPUs, 48 GB memory), when fast turn-around is needed, SeqMule generates annotated VCF files in a day from a 30X whole-genome sequencing data set; when more accurate calling is needed, SeqMule generates consensus call set that improves over single callers, as measured by both Mendelian error rate and consistency. SeqMule supports Sun Grid Engine for parallel processing, offers turn-key solution for deployment on Amazon Web Services, allows quality check, Mendelian error check, consistency evaluation, HTML-based reports. SeqMule is available at http://seqmule.openbioinformatics.org.
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25
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Upton A, Trelles O, Cornejo-García JA, Perkins JR. Review: High-performance computing to detect epistasis in genome scale data sets. Brief Bioinform 2015; 17:368-79. [PMID: 26272945 DOI: 10.1093/bib/bbv058] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Indexed: 11/14/2022] Open
Abstract
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.
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Moghadam BT, Alvarsson J, Holm M, Eklund M, Carlsson L, Spjuth O. Scaling predictive modeling in drug development with cloud computing. J Chem Inf Model 2015; 55:19-25. [PMID: 25493610 DOI: 10.1021/ci500580y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.
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Affiliation(s)
- Behrooz Torabi Moghadam
- Department of Pharmaceutical Biosciences, ‡Department of Information Technology, and §Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University , SE-751 24 Uppsala, Sweden
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Aranguren ME, González AR, Wilkinson MD. Executing SADI services in Galaxy. J Biomed Semantics 2014; 5:42. [PMID: 25309716 PMCID: PMC4193140 DOI: 10.1186/2041-1480-5-42] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 09/19/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years Galaxy has become a popular workflow management system in bioinformatics, due to its ease of installation, use and extension. The availability of Semantic Web-oriented tools in Galaxy, however, is limited. This is also the case for Semantic Web Services such as those provided by the SADI project, i.e. services that consume and produce RDF. Here we present SADI-Galaxy, a tool generator that deploys selected SADI Services as typical Galaxy tools. RESULTS SADI-Galaxy is a Galaxy tool generator: through SADI-Galaxy, any SADI-compliant service becomes a Galaxy tool that can participate in other out-standing features of Galaxy such as data storage, history, workflow creation, and publication. Galaxy can also be used to execute and combine SADI services as it does with other Galaxy tools. Finally, we have semi-automated the packing and unpacking of data into RDF such that other Galaxy tools can easily be combined with SADI services, plugging the rich SADI Semantic Web Service environment into the popular Galaxy ecosystem. CONCLUSIONS SADI-Galaxy bridges the gap between Galaxy, an easy to use but "static" workflow system with a wide user-base, and SADI, a sophisticated, semantic, discovery-based framework for Web Services, thus benefiting both user communities.
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Affiliation(s)
- Mikel Egaña Aranguren
- Biological Informatics, Centre for Plant Biotechnology and Genomics (CBGP), Technical University of Madrid (UPM), Campus of Montegancedo, 28223 Pozuelo de Alarcón, Spain ; Genomic Resources, Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of Basque Country (UPV/EHU), Sarriena auzoa z/g, 48940 Leioa - Bilbo, Spain
| | - Alejandro Rodríguez González
- Biological Informatics, Centre for Plant Biotechnology and Genomics (CBGP), Technical University of Madrid (UPM), Campus of Montegancedo, 28223 Pozuelo de Alarcón, Spain
| | - Mark D Wilkinson
- Biological Informatics, Centre for Plant Biotechnology and Genomics (CBGP), Technical University of Madrid (UPM), Campus of Montegancedo, 28223 Pozuelo de Alarcón, Spain
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Shanahan HP, Owen AM, Harrison AP. Bioinformatics on the cloud computing platform Azure. PLoS One 2014; 9:e102642. [PMID: 25050811 PMCID: PMC4106841 DOI: 10.1371/journal.pone.0102642] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Accepted: 06/20/2014] [Indexed: 12/27/2022] Open
Abstract
We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development.
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Affiliation(s)
- Hugh P. Shanahan
- Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
- * E-mail:
| | - Anne M. Owen
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom
| | - Andrew P. Harrison
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom
- Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, United Kingdom
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Oakley TH, Alexandrou MA, Ngo R, Pankey MS, Churchill CKC, Chen W, Lopker KB. Osiris: accessible and reproducible phylogenetic and phylogenomic analyses within the Galaxy workflow management system. BMC Bioinformatics 2014; 15:230. [PMID: 24990571 PMCID: PMC4227113 DOI: 10.1186/1471-2105-15-230] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2013] [Accepted: 04/29/2014] [Indexed: 01/09/2023] Open
Abstract
Background Phylogenetic tools and ‘tree-thinking’ approaches increasingly permeate all biological research. At the same time, phylogenetic data sets are expanding at breakneck pace, facilitated by increasingly economical sequencing technologies. Therefore, there is an urgent need for accessible, modular, and sharable tools for phylogenetic analysis. Results We developed a suite of wrappers for new and existing phylogenetics tools for the Galaxy workflow management system that we call Osiris. Osiris and Galaxy provide a sharable, standardized, modular user interface, and the ability to easily create complex workflows using a graphical interface. Osiris enables all aspects of phylogenetic analysis within Galaxy, including de novo assembly of high throughput sequencing reads, ortholog identification, multiple sequence alignment, concatenation, phylogenetic tree estimation, and post-tree comparative analysis. The open source files are available on in the Bitbucket public repository and many of the tools are demonstrated on a public web server (http://galaxy-dev.cnsi.ucsb.edu/osiris/). Conclusions Osiris can serve as a foundation for other phylogenomic and phylogenetic tool development within the Galaxy platform.
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Affiliation(s)
- Todd H Oakley
- Ecology, Evolution, and Marine Biology, University of California-Santa Barbara, Santa Barbara, CA 93106, USA.
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31
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Tretyakov K, Goldberg T, Jin VX, Horton P. Summary of talks and papers at ISCB-Asia/SCCG 2012. BMC Genomics 2013. [PMCID: PMC3639071 DOI: 10.1186/1471-2164-14-s2-i1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
The second ISCB-Asia conference of the International Society for Computational Biology took place December 17-19, 2012, in Shenzhen, China. The conference was co-hosted by BGI as the first Shenzhen Conference on Computational Genomics (SCCG).
45 talks were presented at ISCB-Asia/SCCG 2012. The topics covered included software tools, reproducible computing, next-generation sequencing data analysis, transcription and mRNA regulation, protein structure and function, cancer genomics and personalized medicine. Nine of the proceedings track talks are included as full papers in this supplement.
In this report we first give a short overview of the conference by listing some statistics and visualizing the talk abstracts as word clouds. Then we group the talks by topic and briefly summarize each one, providing references to related publications whenever possible. Finally, we close with a few comments on the success of this conference.
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El-Kalioby M, Abouelhoda M, Krüger J, Giegerich R, Sczyrba A, Wall DP, Tonellato P. Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package. BMC Bioinformatics 2012; 13 Suppl 17:S22. [PMID: 23281941 PMCID: PMC3521398 DOI: 10.1186/1471-2105-13-s17-s22] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Background Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. Results In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Conclusions Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.
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