BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments

PeerJ

article

Maria Luiza Mondelli¹  Thiago Magalhães¹  Guilherme Loss¹  Michael Wilde²  Ian Foster²  Marta Mattoso³  Daniel Katz⁴  Helio Barbosa¹  Ana Tereza R. de Vasconcelos¹  Kary Ocaña¹  Luiz M.R. Gadelha Jr¹ 

[1] National Laboratory for Scientific Computing;Computation Institute, Argonne National Laboratory/University of Chicago;Computer and Systems Engineering Program, COPPE, Federal University of Rio de Janeiro;National Center for Supercomputing Applications, University of Illinois;Federal University of Juiz de Fora

关键词: Bioinformatics;    Scientific workflows;    Provenance;    Profiling;    Data analytics;   

DOI  :  10.7717/peerj.5551

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process.

【 授权许可】

CC BY