RNASeq_similarity_matrix: visually identify sample mix-ups in RNASeq data using a ‘genomic’ sequence similarity matrix

• Published in: Bioinformatics 2020-03, Vol.36 (6), p.1940-1941
• Main Authors: Kist, Nicolaas C, Power, Robert A, Skelton, Andrew, Seegobin, Seth D, Verbelen, Moira, Bonde, Bushan, Malki, Karim
• Format: Article
• Language: English
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btz821

Abstract Summary Mistakes in linking a patient’s biological samples with their phenotype data can confound RNA-Seq studies. The current method for avoiding such sample mix-ups is to test for inconsistencies between biological data and known phenotype data such as sex. However, in DNA studies a common QC step is to check for unexpected relatedness between samples. Here, we extend this method to RNA-Seq, which allows the detection of duplicated samples without relying on identifying inconsistencies with phenotype data. Results We present RNASeq_similarity_matrix: an automated tool to generate a sequence similarity matrix from RNA-Seq data, which can be used to visually identify sample mix-ups. This is particularly useful when a study contains multiple samples from the same individual, but can also detect contamination in studies with only one sample per individual. Availability and implementation RNASeq_similarity_matrix has been made available as a documented GPL licensed Docker image on www.github.com/nicokist/RNASeq_similarity_matrix.