Summary of the steps executed by the Kuura pipeline.

Summary of the steps executed by the Kuura pipeline.</p

Complete validation results.

In the revision process, the pipeline was validated on gold standard data sets HG003, HG004, HG006 and HG007, data sets generated with the same sequencing protocol in the same study as data sets HG001, HG002 and HG005. The table shows the number of variants identified by each variant caller, their precision and recall values. *The table contains only SNP information. (XLSX)</p

Overall architecture of the Kuura pipeline.

The source codes for the pipeline are version controlled using git and maintained in github, the run environment is stored as a docker image. Upon initiating the analysis pipeline, nextflow deploys the docker container on top of HPC cluster and runs the analysis within the docker container and upon completion of the process the output is directly written to the specified storage volume.</p

Validation results using each variant caller.

The table shows the number of variants identified by each variant caller, their precision and recall values. *The table contains only SNP information.</p

Summary of the tools and their respective <i>docker</i> containers used in each stage.

Summary of the tools and their respective docker containers used in each stage.</p

Detailed installation and usage instructions.

The advent of high-throughput sequencing technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided the research community with an abundance of data but has also presented the challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is in using the optimal resources in terms of available tools. To address this research gap, we propose “Kuura—An automated workflow for analyzing WES and WGS data”, which is optimized for both whole exome and whole genome sequencing data. This workflow is based on the nextflow pipeline scripting language and uses docker to manage and deploy the workflow. The workflow consists of four analysis stages—quality control, mapping to reference genome & quality score recalibration, variant calling & variant recalibration and variant consensus & annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2), generating a list of high-confidence variants in a consensus call file. The workflow is flexible as it integrates the fragmented tools and can be easily extended by adding or updating tools or amending the parameters list. The use of a single parameters file enhances reproducibility of the results. The ease of deployment and usage of the workflow further increases computational reproducibility providing researchers with a standardized tool for the variant calling step in different projects. The source code, instructions for installation and use of the tool are publicly available at our github repository https://github.com/dhanaprakashj/kuura_pipeline.</div

Screenshot showing a successfully executed pipeline and the information presented while the pipeline is running.

Screenshot showing a successfully executed pipeline and the information presented while the pipeline is running.</p

Information on the gold standard datasets used for validating the pipeline.

Information on the gold standard datasets used for validating the pipeline.</p

<i>IL11</i> 3′ UTR luciferase assay.

<p>Luciferase reporter constructs, each containing one fragment of the <i>IL11</i> 3′ UTR, were co-transfected with a Renilla luciferase construct and the miRNA precursors into MDA-MB-231(SA) cells (n = 5), and luciferase activity was measured 28 hours later. * p<0.05, ** p<0.01, *** p<0.001, as compared to the negative control Pre-miR.</p

Genes over 1.5-fold downregulated in response to miR-204 and/or -379 versus negative control Pre-miR.

<p>RNA samples for genome-wide gene expression analysis were collected 24 hours after MDA-MB-231(SA) cells had been transfected with miRNA precursors. The predicted targets of miR-204 are typed in red and the predicted targets of miR-379 in blue. Expression fold changes, gene names, and more detailed information about the target predictions are listed in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037361#pone.0037361.s004" target="_blank">Tables S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037361#pone.0037361.s005" target="_blank">S4</a>.</p