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.
Kumar, Gaurav; Ertel, Adam; Feldman, George; Kupper, Joan; and Fortina, Paolo, "iSeqQC: a tool for expression-based quality control in RNA sequencing." (2020). Department of Cancer Biology Faculty Papers. Paper 164.
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This work is licensed under a Creative Commons Attribution 4.0 License.
This article is the author’s final published version in BMC Bioinformatics, Volume 21, Issue 1, February 2020, Article number 56.
The published version is available at https://doi.org/10.1186/s12859-020-3399-8. Copyright © Kumar et al.
Publication made possible in part by support from the Thomas Jefferson University + Philadelphia University Open Access Fund