RT Journal Article SR Electronic T1 iSeqQC: A Tool for Expression-Based Quality Control in RNA Sequencing JF bioRxiv FD Cold Spring Harbor Laboratory SP 768101 DO 10.1101/768101 A1 Gaurav Kumar A1 Adam Ertel A1 George Feldman A1 Joan Kupper A1 Paolo Fortina YR 2019 UL http://biorxiv.org/content/early/2019/09/18/768101.abstract AB Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise the data. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers or batch effects. Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced by batch effects due to 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 either through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.