Imperial College London
Alternate

Dr Antonio J Berlanga-Taylor

Faculty of Medicine, School of Public Health

Honorary Senior Research Fellow
 
 
 
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Contact

 

a.berlanga

 
 
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Location

 

47 Praed StreetSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cribbs:2019:10.1101/581009,
author = {Cribbs, A and Luna-Valero, S and George, C and Sudbery, IM and Berlanga-Taylor, AJ and Sansom, SN and Smith, T and Ilott, NE and Johnson, J and Scaber, J and Brown, K and Sims, D and Heger, A},
doi = {10.1101/581009},
title = {CGAT-core: a python framework for building scalable, reproducible computational biology workflows},
url = {http://dx.doi.org/10.1101/581009},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:p>In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudo-alignment.</jats:p>
AU  - Cribbs,A
AU  - Luna-Valero,S
AU  - George,C
AU  - Sudbery,IM
AU  - Berlanga-Taylor,AJ
AU  - Sansom,SN
AU  - Smith,T
AU  - Ilott,NE
AU  - Johnson,J
AU  - Scaber,J
AU  - Brown,K
AU  - Sims,D
AU  - Heger,A
DO  - 10.1101/581009
PY  - 2019///
TI  - CGAT-core: a python framework for building scalable, reproducible computational biology workflows
UR  - http://dx.doi.org/10.1101/581009
ER  -
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