iSeqQC: a tool for expression-based quality control in RNA sequencing

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: ) or web-interface (). A local shiny installation can also be obtained from github (). Conclusion iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.