Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web

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Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web

Lampa, Samuel, 1983- (författare): Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Stockholm University, Stockholm, Sweden,Pharmaceutical Bioinformatics

Spjuth, Ola, Docent, 1977- (preses): Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab

Grafström, Roland, Professor (preses): Institute of Environmental Medicine, Karolinska Institutet

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Hinsen, Konrad, Researcher (opponent): Centre de Biophysique Moléculaire (CNRS), Orléans, France

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ISBN 9789151304274
Uppsala : Acta Universitatis Upsaliensis, 2018
Engelska 68 s.

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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)

Abstract Ämnesord

Stäng

The pharmaceutical industry is facing a research and development productivity crisis. At the same time we have access to more biological data than ever from recent advancements in high-throughput experimental methods. One suggested explanation for this apparent paradox has been that a crisis in reproducibility has affected also the reliability of datasets providing the basis for drug development. Advanced computing infrastructures can to some extent aid in this situation but also come with their own challenges, including increased technical debt and opaqueness from the many layers of technology required to perform computations and manage data. In this thesis, a number of approaches and methods for dealing with data and computations in early drug discovery in a reproducible way are developed. This has been done while striving for a high level of simplicity in their implementations, to improve understandability of the research done using them. Based on identified problems with existing tools, two workflow tools have been developed with the aim to make writing complex workflows particularly in predictive modelling more agile and flexible. One of the tools is based on the Luigi workflow framework, while the other is written from scratch in the Go language. We have applied these tools on predictive modelling problems in early drug discovery to create reproducible workflows for building predictive models, including for prediction of off-target binding in drug discovery. We have also developed a set of practical tools for working with linked data in a collaborative way, and publishing large-scale datasets in a semantic, machine-readable format on the web. These tools were applied on demonstrator use cases, and used for publishing large-scale chemical data. It is our hope that the developed tools and approaches will contribute towards practical, reproducible and understandable handling of data and computations in early drug discovery.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmakologi och toxikologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmacology and Toxicology (hsv//eng)
NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

Nyckelord

Reproducibility
Scientific Workflow Management Systems
Workflows
Pipelines
Flow-based programming
Predictive modelling
Semantic Web
Linked Data
Semantic MediaWiki
MediaWiki
RDF
SPARQL
Golang
Reproducerbarhet
Arbetsflödeshanteringssystem
Flödesbaserad programmering
Prediktiv modellering
Semantiska webben
Länkade data
Go
Bioinformatics
Bioinformatik
Pharmacology
Farmakologi

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