Use of application containers and workflows for genomic data analysis
Background: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibili...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Wolters Kluwer Medknow Publications
2016-01-01
|
Series: | Journal of Pathology Informatics |
Subjects: | |
Online Access: | http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=53;epage=53;aulast=Schulz |
id |
doaj-71c5a92c8c4740d6a8fd9fcc1012306d |
---|---|
record_format |
Article |
spelling |
doaj-71c5a92c8c4740d6a8fd9fcc1012306d2020-11-24T21:19:20ZengWolters Kluwer Medknow PublicationsJournal of Pathology Informatics2153-35392153-35392016-01-0171535310.4103/2153-3539.197197Use of application containers and workflows for genomic data analysisWade L SchulzThomas DurantAlexa J SiddonRichard TorresBackground: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Methods: Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. Results: While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. Conclusions: With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=53;epage=53;aulast=SchulzBig databioinformatics workflowcontainerizationgenomics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wade L Schulz Thomas Durant Alexa J Siddon Richard Torres |
spellingShingle |
Wade L Schulz Thomas Durant Alexa J Siddon Richard Torres Use of application containers and workflows for genomic data analysis Journal of Pathology Informatics Big data bioinformatics workflow containerization genomics |
author_facet |
Wade L Schulz Thomas Durant Alexa J Siddon Richard Torres |
author_sort |
Wade L Schulz |
title |
Use of application containers and workflows for genomic data analysis |
title_short |
Use of application containers and workflows for genomic data analysis |
title_full |
Use of application containers and workflows for genomic data analysis |
title_fullStr |
Use of application containers and workflows for genomic data analysis |
title_full_unstemmed |
Use of application containers and workflows for genomic data analysis |
title_sort |
use of application containers and workflows for genomic data analysis |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Pathology Informatics |
issn |
2153-3539 2153-3539 |
publishDate |
2016-01-01 |
description |
Background: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Methods: Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. Results: While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. Conclusions: With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia. |
topic |
Big data bioinformatics workflow containerization genomics |
url |
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=53;epage=53;aulast=Schulz |
work_keys_str_mv |
AT wadelschulz useofapplicationcontainersandworkflowsforgenomicdataanalysis AT thomasdurant useofapplicationcontainersandworkflowsforgenomicdataanalysis AT alexajsiddon useofapplicationcontainersandworkflowsforgenomicdataanalysis AT richardtorres useofapplicationcontainersandworkflowsforgenomicdataanalysis |
_version_ |
1726005920747487232 |