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

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.

SUBMITTER: Kumar G 

PROVIDER: S-EPMC7020508 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature