Journal:Use of application containers and workflows for genomic data analysis

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Full article title Use of application containers and workflows for genomic data analysis
Journal Journal of Pathology Informatics
Author(s) Schulz, Wade L.; Durant, Thomas; Siddon, Alexa J.; Torres, Richard
Author affiliation(s) Yale University School of Medicine, VA Connecticut Healthcare System
Primary contact Email: Log in to source site to view
Year published 2016
Volume and issue 7
Page(s) 53
DOI 10.4103/2153-3539.197197
ISSN 2153-3539
Distribution license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
Website http://www.jpathinformatics.org
Download http://www.jpathinformatics.org/temp/JPatholInform7153-6111104_165831.pdf (PDF)
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Abstract

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.

Keywords: Big data, bioinformatics workflow, containerization, genomics

Introduction

The amount of data available for research is growing at an exponential rate. The recent push for open data has also rapidly increased the availability of biomedical datasets for secondary analysis. Examples include the Yale Open Data Access project[1], a repository of clinical trial data, and The Cancer Genome Atlas (TCGA)[2], a project that makes genomic data accessible to researchers after initial findings are released. While these data sets promote ongoing research, the ability to efficiently store, move, and analyze such large repositories is often a bottleneck to analysis.[3]

In addition to the massive growth in volume and availability, novel analyses — including advanced statistical methods and machine learning — often require significant resources for efficient processing. One example of this in biomedical research is the analysis of next generation sequencing (NGS) data. NGS is also known as massively parallel or high-throughput sequencing, as it simultaneously sequences many fragments of DNA, thereby producing enormous amounts of information. These datasets often require several preprocessing steps followed by detailed analysis. In addition to being resource intensive, the reproducibility of computational experiments using these data is often limited due to the complexity of system and software configuration.[4] Some application frameworks have made advances to improve the reproducibility of individual applications and analysis pipelines[5][6], but significant work remains to increase this reliability, particularly for experiments performed in resource-limited environments or on computational clusters.

The deployment of complex computational systems is not unique to bioinformatics. As such, there has been significant progress in building virtualization layers for operating systems and more recently, software applications.[7][8] A current example of this includes the Docker platform (Docker, San Francisco, CA, U.S.A.), which allows for the creation and configuration of software containers for deployment on a range of systems.[9][10] While the use of these technologies has limitations, it also has the potential improve the usability of many software applications in computational biology. As such, several studies and initiatives have begun to focus on the use of Docker in bioinformatics and computer science research.[11][12][13] In this paper, we demonstrate the potential benefits of containerized applications and application workflows for computational genomics research.

Technical background

To augment an ongoing study related to tumor heterogeneity, we obtained access to acute myeloid leukemia (AML) NGS data from TCGA.[14] Aligned NGS data from TCGA are available through the Cancer Genomics Hub (cgHub). The data set of interest consisted of approximately 12 terabytes (TBs) of whole genome sequencing (WGS) data and another 12 TB of whole exome sequencing data. Our analysis required the identification of somatic variants followed by a prediction of tumor heterogeneity using publicly available software tools. Unfortunately, many bioinformatics tools have specific software dependencies and natively run on only a subset of operating systems.[15] In addition, many applications are unable to run their computations in parallel, thus limiting analysis throughput. While increasing the number of servers or individual server resources can improve analysis speed, overall processing may still be less efficient due to these limitations.

As previously noted, the Docker platform allows for virtualized application deployments within a lightweight, Linux-based wrapper called a container.[9][11][15] This approach is similar to operating system virtualization but at the application level. Containerization enables developers to create virtual environments that have only the minimum necessary libraries, which users can quickly deploy on their own infrastructure in a secure, reproducible fashion. In addition, the isolation offered by this approach means that a more robust, parallelized workflow can be created for some applications that do not natively support multi-threading or parallel processing. While this type of workflow implementation is not beneficial for all use cases, scenarios where compute capacity on a single node exceeds what a single application can utilize are likely to benefit from such an approach.

To efficiently predict tumor heterogeneity from TCGA data, we implemented two key technologies: a Python workflow using the Luigi library (Spotify, Stockholm, Sweden)[16] and Docker containers for each key application in our analysis pipeline. While neither of these technologies is unique to genomic analysis or bioinformatics, Luigi is a general workflow orchestration library, and Docker allows for application visualization, as previously noted. For this use case, we were restricted to a single computational node with eight cores and 128 gigabyte (GB) of memory for analysis. As shown in [Figure 1], aligned WGS data were obtained for paired germline and tumor specimens from cgHub (now the genomic data commons) using the cgDownload utility.[17] We identified somatic variants with the SomaticSniper application[18] and generated clonal predictions with the SciClone library.[19] Sequential data acquisition and analysis took approximately four hours to complete per paired sequence but had significant variability between specimens, since file size and the number of genetic mutations varied significantly.

References

  1. ↑ Krumholz, H.M.; Waldstreicher, J. (2016). "The Yale Open Data Access (YODA) Project--A mechanism for data sharing". New England Journal of Medicine 375 (5): 403–405. doi:10.1056/NEJMp1607342. PMID 27518657. 
  2. ↑ Collins, F.S.; Barker, A.D. (2007). "Mapping the cancer genome: Pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies". Scientific American 296 (3): 50–7. PMID 17348159. 
  3. ↑ Fan, J.; Han, F.; Liu, H. (2014). "Challenges of big data analysis". National Science Review 1 (2): 293–314. doi:10.1093/nsr/nwt032. PMC PMC4236847. PMID 25419469. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236847. 
  4. ↑ Nekrutenko, A.; Taylor, J. (2012). "Next-generation sequencing data interpretation: Enhancing reproducibility and accessibility". Nature Reviews Genetics 13 (9): 667–72. doi:10.1038/nrg3305. PMID 22898652. 
  5. ↑ Blankenberg, Daniel; Von Kuster, Gregory; Coraor, Nathaniel; Ananda, Guruprasad; Lazarus, Ross; Mangan, Mary; Nekrutenko, Anton; Taylor, James (2010). "Galaxy: a web-based genome analysis tool for experimentalists". Current Protocols in Molecular Biology 19 (Unit 19.10.1–21). doi:10.1002/0471142727.mb1910s89. PMC PMC4264107. PMID 20069535. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4264107. 
  6. ↑ Hatakeyama, M.; Opitz, L.; Russo, G.; Qi, W.; Schlapbach, R.; Rehrauer, H. (2016). "SUSHI: An exquisite recipe for fully documented, reproducible and reusable NGS data analysis". BMC Bioinformatics 17: 228. doi:10.1186/s12859-016-1104-8. 
  7. ↑ Dudley, J.T.; Butte, A.J. (2010). "In silico research in the era of cloud computing". Nature Biotechnology 28 (11): 1181–5. doi:10.1038/nbt1110-1181. PMC PMC3755123. PMID 21057489. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755123. 
  8. ↑ Howe, B. (2012). "Virtual appliances, cloud computing, and reproducible research". Computing in Science & Engineering 14 (4): 36–41. doi:10.1109/MCSE.2012.62. 
  9. ↑ 9.0 9.1 "Docker". Docker, Inc. https://www.docker.com/. Retrieved 21 November 2016. 
  10. ↑ Anderson, C. (2015). "Docker". IEEE Software 32 (3): 102–105. doi:10.1109/MS.2015.62. 
  11. ↑ 11.0 11.1 Boettiger, C. (2015). "An introduction to Docker for reproducible research". SIGOPS Operating Systems Review 49 (1): 71–79. doi:10.1145/2723872.2723882. 
  12. ↑ Hung, L.H.; Kristiyanto, D.; Lee, S.B.; Yeung, K.Y. (2016). "GUIdock: Using Docker containers with a common graphics user interface to address the reproducibility of research". PLoS One 11 (4): e0152686. doi:10.1371/journal.pone.0152686. PMC PMC4821530. PMID 27045593. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821530. 
  13. ↑ Moreews, F.; Sallou, O.; Ménager, H. et al. (2015). "BioShaDock: A community driven bioinformatics shared Docker-based tools registry". F1000Research 4: 1443. doi:10.12688/f1000research.7536.1. PMC PMC4743153. PMID 26913191. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4743153. 
  14. ↑ Cancer Genome Atlas Research Network (2013). "Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia". New England Journal of Medicine 368 (22): 2059-74. doi:10.1056/NEJMoa1301689. PMC PMC3767041. PMID 23634996. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767041. 
  15. ↑ 15.0 15.1 Piccolo, S.R.; Frampton, M.B. (2016). "Tools and techniques for computational reproducibility". GigaScience 5 (1): 30. doi:10.1186/s13742-016-0135-4. PMC PMC4940747. PMID 27401684. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4940747. 
  16. ↑ "Spotify/Luigi". GitHub, Inc. https://github.com/spotify/luigi. Retrieved 21 November 2016. 
  17. ↑ "Genomic Data Commons". National Cancer Institute. https://gdc.cancer.gov/. Retrieved 21 November 2016. 
  18. ↑ Larson, D.E.; Harris, C.C.; Chen, K. et al. (2012). "SomaticSniper: Identification of somatic point mutations in whole genome sequencing data". Bioinformatics 28 (3): 311-7. doi:10.1093/bioinformatics/btr665. PMC PMC3268238. PMID 22155872. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3268238. 
  19. ↑ Miller, C.A.; White, B.S.; Dees, N.D. et al. (2014). "SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution". PLoS Computational Biology 10 (8): e1003665. doi:10.1371/journal.pcbi.1003665. PMC PMC4125065. PMID 25102416. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125065. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.

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