Abstract
Reproducing results is a major issue in cancer biology, whose “work bench” is dynamic and complex, with frequently updated algorithms and software. The better to manage our work in this environment we have developed SyQADA, a System for Quality-Assured Data Analysis – a workflow automation system designed to simplify common sequential analysis processes on the same or different data. SyQADA manages many of the details of procedural bookkeeping involved in bioinformatics workflows: What samples are we using? Where are the raw data? Were all the samples processed? Did every job complete satisfactorily? Is there as much output as expected? Where are the input files for the next step? How long does a typical job take to run? Which program versions did we use? Can we easily compare these results with the output of a different version of a program, or with different input data?
Using SyQADA, we have found ourselves better able to reproduce results while at the same time reducing the human effort required to manage our upstream data analyses. Here, we briefly describe how our lung cancer studies have benefitted from the use of SyQADA.
To understand the effect of different variant callers for Ion Torrent deep sequencing data in a lung cancer genomics study, we created a work protocol that allowed us to compare the different sets of variants called on 34 distinct somatic DNA samples from 4 patients. This complex processing framework involved running multiple variant callers, annotating variants, filtering germline variants using quality control metrics, and collating results across samples and callers. With SyQADA, we were able to re-run individual processes changing parameters with trivial changes to our configuration, yielding improved output. We then applied that unmodified protocol to the 500 samples from 48 individuals in our study, and rapidly produced data from which we could perform biological analysis. We then applied the protocol to a study of pre-malignant lesions in 25 lung cancer patients. In both studies, our workflow allowed us to generate comparable results in a matter of hours rather than days.
SyQADA has been used by individuals with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse analytical workflows. Projects to which SyQADA has been applied include allelic imbalance studies of TCGA samples for cancers of the breast, pancreas, lung, and colon, processing roughly 6000 samples through a dozen steps.
A zipfile containing the SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab will be available.
Citation Format: Jerry Fowler, F. Anthony San Lucas, Smruthy Sivakumar, Aditya Deshpande, Humam Kadara, Paul A. Scheet. Optimizing the replication of cancer genomics workflows: case studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2594. doi:10.1158/1538-7445.AM2017-2594
- ©2017 American Association for Cancer Research.
Volume 77, Issue 13 Supplement
