Evidence map›Paper›PMID 29246107›Full record

ArticleBMC bioinformatics2017

Canary: an atomic pipeline for clinical amplicon assays.

Kenneth D Doig, Jason Ellul, Andrew Fellowes, Ella R Thompson, Georgina Ryland, Piers Blombery, Anthony T Papenfuss, Stephen B Fox

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kenneth D DoigResearch Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. Ken.Doig@petermac.org.
Jason EllulResearch Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Andrew FellowesDepartment of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Ella R ThompsonDepartment of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Georgina RylandDepartment of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Piers BlomberyDepartment of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Anthony T PapenfussResearch Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.
Stephen B FoxDepartment of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.

Funding

Therapeutics Innovation Australia and a National Health and Medical Research Council (NHMRC) Program Grant 1054618
6 · The paper itself

Abstract

backgroundHigh throughput sequencing requires bioinformatics pipelines to process large volumes of data into meaningful variants that can be translated into a clinical report. These pipelines often suffer from a number of shortcomings: they lack robustness and have many components written in multiple languages, each with a variety of resource requirements. Pipeline components must be linked together with a workflow system to achieve the processing of FASTQ files through to a VCF file of variants. Crafting these pipelines requires considerable bioinformatics and IT skills beyond the reach of many clinical laboratories.

resultsHere we present Canary, a single program that can be run on a laptop, which takes FASTQ files from amplicon assays through to an annotated VCF file ready for clinical analysis. Canary can be installed and run with a single command using Docker containerization or run as a single JAR file on a wide range of platforms. Although it is a single utility, Canary performs all the functions present in more complex and unwieldy pipelines. All variants identified by Canary are 3' shifted and represented in their most parsimonious form to provide a consistent nomenclature, irrespective of sequencing variation. Further, proximate in-phase variants are represented as a single HGVS 'delins' variant. This allows for correct nomenclature and consequences to be ascribed to complex multi-nucleotide polymorphisms (MNPs), which are otherwise difficult to represent and interpret. Variants can also be annotated with hundreds of attributes sourced from MyVariant.info to give up to date details on pathogenicity, population statistics and in-silico predictors.

conclusionsCanary has been used at the Peter MacCallum Cancer Centre in Melbourne for the last 2 years for the processing of clinical sequencing data. By encapsulating clinical features in a single, easily installed executable, Canary makes sequencing more accessible to all pathology laboratories. Canary is available for download as source or a Docker image at https://github.com/PapenfussLab/Canary under a GPL-3.0 License.

Indexed as

SoftwareComputational BiologyDatabases, GeneticGenetic VariationHigh-Throughput Nucleotide SequencingHumansAmpliconCanaryClinical diagnosticsPathOSPipelinesTargeted sequencingVariant calling

Identifiers

PMID29246107
PMCPMC5732437

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.