BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Abstract

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected “ signal” in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

Quality control of large genome datasets

“…As a partial way to mitigate this deficiency, we recommend performing global dataset comparisons using genome fingerprints, and other general-purpose 32 or domain-specific metrics. Such relative benchmarking, in which each individual genome can serve as its own reference, can supplement absolute benchmarking relative to truth sets.…”

Quality control of large genome datasets using genome fingerprints

“…We observe that such verification may be insufficient for global evaluation of large genome datasets including samples from diverse population backgrounds, which may be differentially affected by reference and software changes. As a partial way to mitigate this deficiency, we recommend performing global dataset comparisons using genome fingerprints and other general-purpose [9] or domain-specific metrics. Such 'relative benchmarking', in which each individual genome can serve as its own reference, can supplement 'absolute benchmarking' relative to truth sets.…”

“…As a partial way to mitigate this deficiency, we recommend performing global dataset comparisons using genome fingerprints, and other general-purpose 27 or domain-specific metrics. Such ‘relative benchmarking’, in which each individual genome can serve as its own reference, can supplement ‘absolute benchmarking’ relative to truth sets.…”

Reproducible big data science: A case study in continuous FAIRness

“…Small data analyses can be implemented effectively via R or Python scripts, that can be 179 executed on a workstation or a cloud-hosted virtual machine and then shared as 180 documents or via notebook environments such as Jupyter [23]. Big data analyses can be 181 more challenging to implement and share, due to the need to orchestrate the execution 182 of multiple application programs on many processors in order to process large quantities 183 of data in a timely manner, whether for quality control [24], computation of derived 184 quantities, or other purposes. 185…”