RESUMEN
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
Asunto(s)
Biología Computacional/normas , Bases de Datos Genéticas/normas , Secuenciación de Nucleótidos de Alto Rendimiento/normas , Filogenia , ARN Mensajero/genética , Programas Informáticos/normas , Animales , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Perfilación de la Expresión Génica/normas , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Hidrozoos , ARN Mensajero/análisis , Especificidad de la EspecieRESUMEN
MOTIVATION: DIYABC is a software package for a comprehensive analysis of population history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 implements a number of new features and analytical methods. It allows (i) the analysis of single nucleotide polymorphism data at large number of loci, apart from microsatellite and DNA sequence data, (ii) efficient Bayesian model choice using linear discriminant analysis on summary statistics and (iii) the serial launching of multiple post-processing analyses. DIYABC v2.0 also includes a user-friendly graphical interface with various new options. It can be run on three operating systems: GNU/Linux, Microsoft Windows and Apple Os X. AVAILABILITY: Freely available with a detailed notice document and example projects to academic users at http://www1.montpellier.inra.fr/CBGP/diyabc CONTACT: estoup@supagro.inra.fr Supplementary information: Supplementary data are available at Bioinformatics online.