A software tool 'CroCo' detects pervasive cross-species contamination in next generation sequencing data

• Published in: BMC biology 2018-03, Vol.16 (1), p.28-28, Article 28
• Main Authors: Simion, Paul, Belkhir, Khalid, François, Clémentine, Veyssier, Julien, Rink, Jochen C, Manuel, Michaël, Philippe, Hervé, Telford, Maximilian J
• Format: Article
• Language: English
• Subjects: Analysis; Animals; Applications software; Computational Biology - methods; Computational Biology - standards; Contamination; Ctenophora; Databases, Genetic - standards; DNA sequencing; Gene Expression Profiling - methods; Gene Expression Profiling - standards; Genetic aspects; High-Throughput Nucleotide Sequencing - methods; High-Throughput Nucleotide Sequencing - standards; Hydrozoa; Life Sciences; NGS; Nucleotide sequencing; Phylogenomics; Phylogeny; Quantitative Methods; RNA, Messenger - analysis; RNA, Messenger - genetics; Software; Software - standards; Species Specificity
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-018-0486-7

Multiple RNA samples are frequently processed together and often mixed before multiplex sequencing in the same sequencing run. While different samples can be separated post sequencing using sample barcodes, the possibility of cross contamination between biological samples from different species that have been processed or sequenced in parallel has the potential to be extremely deleterious for downstream analyses. We present CroCo, a software package for identifying and removing such cross contaminants from assembled transcriptomes. Using multiple, recently published sequence datasets, we show that cross contamination is consistently present at varying levels in real data. Using real and simulated data, we demonstrate that CroCo detects contaminants efficiently and correctly. Using a real example from a molecular phylogenetic dataset, we show that contaminants, if not eliminated, can have a decisive, deleterious impact on downstream comparative analyses. Cross contamination is pervasive in new and published datasets and, if undetected, can have serious deleterious effects on downstream analyses. CroCo is a database-independent, multi-platform tool, designed for ease of use, that efficiently and accurately detects and removes cross contamination in assembled transcriptomes to avoid these problems. We suggest that the use of CroCo should become a standard cleaning step when processing multiple samples for transcriptome sequencing.