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.
Recommended CitationKumar, 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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