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Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.
Keywords
Drug Repositioning, Drug Discovery, Multi-omics, Pathways, Software, Bioinformatics
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
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Editorial decision: Accept
On 26 May, 2020
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On 22 May, 2020
Submission checks complete
On 21 May, 2020
Editor invited
On 21 May, 2020
No comments provided
Editorial decision: Minor revision
On 20 May, 2020
Reviewers invited
Invitations sent on 15 Apr, 2020
Editor assigned
On 06 Apr, 2020
Submission checks complete
On 05 Apr, 2020
Editor invited
On 05 Apr, 2020
No comments provided
Editorial decision: Major revision
On 03 Apr, 2020
Review #1 received
Received 29 Mar, 2020
Reviewer #1 agreed
On 25 Mar, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Editor assigned
On 19 Feb, 2020
Submission checks complete
On 18 Feb, 2020
Editor invited
On 18 Feb, 2020
Version 3
Posted 31 Jan, 2020
No comments provided
Editorial decision: Major revision
On 12 Feb, 2020
Reviewer #1 agreed
On 31 Jan, 2020
Review #1 received
Received 31 Jan, 2020
Editor assigned
On 31 Jan, 2020
Reviewers invited
Invitations sent on 31 Jan, 2020
Submission checks complete
On 30 Jan, 2020
Editor invited
On 30 Jan, 2020
No comments provided
Review #2 received
Received 27 Jan, 2020
Editorial decision: Major revision
On 27 Jan, 2020
Review #1 received
Received 27 Dec, 2019
Reviewer #2 agreed
On 05 Dec, 2019
Reviewers invited
Invitations sent on 20 Nov, 2019
Reviewer #1 agreed
On 20 Nov, 2019
Editor assigned
On 15 Nov, 2019
Submission checks complete
On 14 Nov, 2019
Editor invited
On 14 Nov, 2019
No comments provided
Editorial decision: Major revision
On 21 Oct, 2019
Review #2 received
Received 18 Oct, 2019
Review #1 received
Received 20 Sep, 2019
Reviewer #2 agreed
On 22 Aug, 2019
Reviewer #3 agreed
On 22 Aug, 2019
Reviewer #1 agreed
On 21 Aug, 2019
Reviewers invited
Invitations sent on 09 Aug, 2019
Editor assigned
On 07 Aug, 2019
Submission checks complete
On 06 Aug, 2019
Editor invited
On 06 Aug, 2019
First submitted
On 30 Jul, 2019
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Bioinformatics
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See the published version of this preprint at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
No community comments so far
Editorial decision: Accept
On 26 May, 2020
Editor assigned
On 22 May, 2020
Submission checks complete
On 21 May, 2020
Editor invited
On 21 May, 2020
No comments provided
Editorial decision: Minor revision
On 20 May, 2020
Reviewers invited
Invitations sent on 15 Apr, 2020
Editor assigned
On 06 Apr, 2020
Submission checks complete
On 05 Apr, 2020
Editor invited
On 05 Apr, 2020
No comments provided
Editorial decision: Major revision
On 03 Apr, 2020
Review #1 received
Received 29 Mar, 2020
Reviewer #1 agreed
On 25 Mar, 2020
Reviewers invited
Invitations sent on 24 Mar, 2020
Editor assigned
On 19 Feb, 2020
Submission checks complete
On 18 Feb, 2020
Editor invited
On 18 Feb, 2020
Version 3
Posted 31 Jan, 2020
No comments provided
Editorial decision: Major revision
On 12 Feb, 2020
Reviewer #1 agreed
On 31 Jan, 2020
Review #1 received
Received 31 Jan, 2020
Editor assigned
On 31 Jan, 2020
Reviewers invited
Invitations sent on 31 Jan, 2020
Submission checks complete
On 30 Jan, 2020
Editor invited
On 30 Jan, 2020
No comments provided
Review #2 received
Received 27 Jan, 2020
Editorial decision: Major revision
On 27 Jan, 2020
Review #1 received
Received 27 Dec, 2019
Reviewer #2 agreed
On 05 Dec, 2019
Reviewers invited
Invitations sent on 20 Nov, 2019
Reviewer #1 agreed
On 20 Nov, 2019
Editor assigned
On 15 Nov, 2019
Submission checks complete
On 14 Nov, 2019
Editor invited
On 14 Nov, 2019
No comments provided
Editorial decision: Major revision
On 21 Oct, 2019
Review #2 received
Received 18 Oct, 2019
Review #1 received
Received 20 Sep, 2019
Reviewer #2 agreed
On 22 Aug, 2019
Reviewer #3 agreed
On 22 Aug, 2019
Reviewer #1 agreed
On 21 Aug, 2019
Reviewers invited
Invitations sent on 09 Aug, 2019
Editor assigned
On 07 Aug, 2019
Submission checks complete
On 06 Aug, 2019
Editor invited
On 06 Aug, 2019
First submitted
On 30 Jul, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
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