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

Abstract

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.

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

“…We extend the proposal of encapsulator and ReproZip by indicating the need to publish the aspects related to the experiment so that it can be uniquely identified, accessed, and retrieved later. This work extends our previous work on provenance management [11]. The implementation of our framework provides provenance data, VM specifications, experiment script, and package versions used as a result.…”

Capturing provenance information for biomedical data and workflows: A scoping review

“…They also provide detailed characteristics about the respective provenance approaches. 2009-2011 6 [10], [29], [30], [31], [32], [33] 2012-2014 13 [1], [2], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44] 2015-2017 9 [4], [45], [46], [47], [48], [49], [50], [51], [52] 2018-2020 21 [9], [11], [53], [54], [55] [70], [71] Target domain (Bio-) medical or healthcare domain 36 [1], [2], [4], [9], [10], [11], [28], [29], [31], [34], [36], [37], [38], [39], [40], [42], [43], [44], [46], [47], [48], [49], [51]…”

“…2. Scienti c work ows and work ow executions: mainly Open Provenance Model [72] (OPM)-oriented work ows on different semantic levels, like in the BioWorkbench [55], OpenPREDICT [56] or in OWL projects. Provenance data was stored in relational databases, like in OPMProv [35] or in graph databases [45].…”

Peer Review #1 of "BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments (v0.2)"

“…BioWorkbench 1 (Mondelli, 2018a) aims to support users in the specification process of an experiment, the experiment execution in HPC resources, the management of consumed and produced data, and the analysis of its execution results. BioWorkbench uses a SWfMS for definition and execution of bioinformatics workflows, and a web application for provenance data analytics.…”