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To provide a genomic narrative that can be trusted, microbiology
laboratories need quality control (QC) metrics to accompany their
genomic pipelines. QC metrics enable:
•  Implementing standards in routine lab sample processing
•  Performance comparison of pipeline optimizations or alternatives
•  Retrospective tracing of problems that arise
QC metrics are not easy to implement – they may need to be adjusted for
organism type, sample quality, sequencing technology and preparation,
and the mix of software components that are brought together in a
pipeline. Another challenge is to transform QC reporting from a manual
review of a pipeline’s disparate and often opaque application log files,
into an automated system of reporting and decision making that can be
adjusted by researchers and system administrators who are not expert
programmers.
We have developed a general purpose text-mining and reporting
application called Report Calc for Quality Control (RCQC) that works
directly within command-line scripts, or as a tool in Galaxy (an interactive
bioinformatics platform and workflow engine). An RCQC interpreter
follows instructions in a RCQC script to extract QC variables from various
application log and report files. It can implement rules that trigger
warning or failure statuses in an active pipeline. Various opportunities
arise for metrics along the stages of a genomic pipeline; our initial focus
is on basic assembly metrics as illustrated on this poster.
Abstract
RCQC Recipes
QC Ontology
Using the JSON-LD format’s metadata feature, RCQC can link particular
QC report terms to their standardized ontology counterparts. Creating a
controlled vocabulary for QC enables reports from disparate genomic
pipelines to be compared, which should eventually lead to a set of
pipeline metrics for accrediting commercial, government and open source
software. Within the context of the OBOFoundry of ontologies we are
introducing an ontology called GenEpiO (currently available at
https://github.com/Public-Health-Bioinformatics/irida_ontology) which
holds QC terms like "genome size ratio", “contig count”, etc. Using the
Protégé ontology editor it is easy to see the definitions for these terms.
Acknowledgements
IRIDA project funding is provided by Genome Canada, Genome BC, and
the Genomics R&D Initiative (GRDI) with additional support from Simon
Fraser University and Cystic Fibrosis Canada. We thank additional
project advisors for constructive comments.
We have started a library of simple "recipe" scripts that extract quality
control (QC) data from various reports like FastQC, QUAST, CheckM and
SPAdes into the popular and software-friendly JSON format (an auto-
generated HTML version of the same content is also available). One can
override sections of an RCQC recipe with settings that test variations in a
pipeline job. An example RCQC text-mining script and output HTML and
JSON report is shown below along with typical report files from other
pipeline tools.
1Department of Pathology, University of British Columbia; 2National Microbiology Laboratory, Public Health Agency of Canada; 3Department of Pathology,
University of British Columbia & BC Public Health Microbiology and Reference Laboratory
Damion M. Dooley1; Aaron J. Petkau2; Franklin Bristow2;
Gary Van Domselaar2; William W.L. Hsiao3
A Scripting Language For Standardized Evaluation Of Quality
Metrics In Galaxy And Command-line Driven Workflows
This work stemmed from the plan to enhance QC reporting on the web-
based Integrated Rapid Infectious Disease Analysis (www.IRIDA.ca)
project which manages sequence libraries and pipelines for food-born
pathogen assembly, annotation, SNP detection, and phylogenetic
analysis. RCQC has been developed to work as a command-line python
app, but in addition, since IRIDA uses Galaxy to execute its pipeline, we
have a Galaxy RCQC tool for “pro” users to develop recipes. We will be
offering a basic version of this tool that allows users without programming
skills to adjust key QC parameters only.
Recipes can include conditionals that trigger a halt to a pipeline by
sending the appropriate signal (exit code). More than one RCQC recipe
can be run in a pipeline, and their report output can be daisy chained in
order to contribute to a single collective report. QC metric conditionals
shown below can either signal a possible error situation (the “fail(qc…)”
call), or even call a halt to futile pipeline work (via “fail(job …)”).
adjusting parameters and formulae for pipeline operation – one that did
not require recompilation after each user-driven change. As a result, the
RCQC system provides a more transparent rule set that reduces the skill
needed to make process adjustments. Standard assembly pipeline QC
metrics are introduced which provide a blueprint for the way QC
components could be shared amongst NGS sequencing pipelines.
Further information, including source code, is available at
https://github.com/Public-Health-Bioinformatics/rcqc.
Implementation
Protege ontology editor view of GenEpiO assembly quality control terms
JSON-LDHTML
FLASHFastQC
CheckM
RCQC recipe for text-mining flash.log
In developing a scripting language to
do this work, we did not want to
reinvent the wheel (in fact RCQC offers
up for reuse all of python’s built-in
math and operator functions). We did
however need a flexible mechanism for
FLASH

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Report Calc for Quality Control

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