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Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

Kroll KW, Mokaram NE, Pelletier AR, Frankhouser DE, Westphal MS, Stump PA, Stump CL, Bundschuh R, Blachly JS, Yan P - Cancer Inform (2014)

Bottom Line: Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool.Second is the QC database, which displays the resulting metrics in a user-friendly web interface.The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Division of Hematology, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.

ABSTRACT
QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.

No MeSH data available.


Sample report. This is a detailed view for a specific sample in the database. QC metrics are grouped into logical segments that can be individually shown or hidden by clicking the “Show/Hide” buttons. Qualitative FastQC metrics are color-coded according to whether they pass, fail, or receive a warning. Quantitative metrics are reported numerically or as percentages, depending on the type of metric. This view also has a drop-down menu to show or hide all plots generated by FastQC and RSeQC (not shown). Aggregate reports supply similar information, but for multiple samples. In an aggregate report, qualitative metrics from FastQC are represented as the number of selected samples that passed, failed, or generated warnings. Quantitative metrics are represented using minimum, maximum, and average values for the selected samples, as well as box plots summarizing the data across the selected samples.
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f4-cin-suppl.3-2014-007: Sample report. This is a detailed view for a specific sample in the database. QC metrics are grouped into logical segments that can be individually shown or hidden by clicking the “Show/Hide” buttons. Qualitative FastQC metrics are color-coded according to whether they pass, fail, or receive a warning. Quantitative metrics are reported numerically or as percentages, depending on the type of metric. This view also has a drop-down menu to show or hide all plots generated by FastQC and RSeQC (not shown). Aggregate reports supply similar information, but for multiple samples. In an aggregate report, qualitative metrics from FastQC are represented as the number of selected samples that passed, failed, or generated warnings. Quantitative metrics are represented using minimum, maximum, and average values for the selected samples, as well as box plots summarizing the data across the selected samples.

Mentions: Users may investigate an individual sample in detail by clicking on the sample name in the table view. This view will navigate to a new page with all the QC output information associated with the selected sample, as shown in Figure 4. Data are grouped into blocks of similar metrics that can be displayed together. There is also a block for all the plots produced by FastQC and RSeQC, and each one can be individually enlarged. Users can download the entire QC information for a single sample with the “Download Report” button on this page. The download file will contain a two-row table featuring all the columns in the database.


Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

Kroll KW, Mokaram NE, Pelletier AR, Frankhouser DE, Westphal MS, Stump PA, Stump CL, Bundschuh R, Blachly JS, Yan P - Cancer Inform (2014)

Sample report. This is a detailed view for a specific sample in the database. QC metrics are grouped into logical segments that can be individually shown or hidden by clicking the “Show/Hide” buttons. Qualitative FastQC metrics are color-coded according to whether they pass, fail, or receive a warning. Quantitative metrics are reported numerically or as percentages, depending on the type of metric. This view also has a drop-down menu to show or hide all plots generated by FastQC and RSeQC (not shown). Aggregate reports supply similar information, but for multiple samples. In an aggregate report, qualitative metrics from FastQC are represented as the number of selected samples that passed, failed, or generated warnings. Quantitative metrics are represented using minimum, maximum, and average values for the selected samples, as well as box plots summarizing the data across the selected samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214596&req=5

f4-cin-suppl.3-2014-007: Sample report. This is a detailed view for a specific sample in the database. QC metrics are grouped into logical segments that can be individually shown or hidden by clicking the “Show/Hide” buttons. Qualitative FastQC metrics are color-coded according to whether they pass, fail, or receive a warning. Quantitative metrics are reported numerically or as percentages, depending on the type of metric. This view also has a drop-down menu to show or hide all plots generated by FastQC and RSeQC (not shown). Aggregate reports supply similar information, but for multiple samples. In an aggregate report, qualitative metrics from FastQC are represented as the number of selected samples that passed, failed, or generated warnings. Quantitative metrics are represented using minimum, maximum, and average values for the selected samples, as well as box plots summarizing the data across the selected samples.
Mentions: Users may investigate an individual sample in detail by clicking on the sample name in the table view. This view will navigate to a new page with all the QC output information associated with the selected sample, as shown in Figure 4. Data are grouped into blocks of similar metrics that can be displayed together. There is also a block for all the plots produced by FastQC and RSeQC, and each one can be individually enlarged. Users can download the entire QC information for a single sample with the “Download Report” button on this page. The download file will contain a two-row table featuring all the columns in the database.

Bottom Line: Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool.Second is the QC database, which displays the resulting metrics in a user-friendly web interface.The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Division of Hematology, Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.

ABSTRACT
QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.

No MeSH data available.