iSeqQC: a tool for expression-based quality control in RNA sequencing
Published on 2020-02-14T08:11:06Z (GMT) by
Abstract Background Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers. Results Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC). Conclusion iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.
Cite this collection
Kumar, Gaurav; Ertel, Adam; Feldman, George; Kupper, Joan; Fortina, Paolo (2020): iSeqQC: a tool for expression-based quality control in RNA sequencing. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4856688.v1