Towards mouse genetic-specific RNA-sequencing read mapping.

Genetic variations affect behavior and cause disease but understanding how these variants drive complex traits is still an open question. A common approach is to link the genetic variants to intermediate molecular phenotypes such as the transcriptome using RNA-sequencing (RNA-seq). Paradoxically, these variants between the samples are usually ignored at the beginning of RNA-seq analyses of many model organisms. This can skew the transcriptome estimates that are used later for downstream analyses, such as expression quantitative trait locus (eQTL) detection. Here, we assessed the impact of reference-based analysis on the transcriptome and eQTLs in a widely-used mouse genetic population: the BXD panel of recombinant inbred lines. We highlight existing reference bias in the transcriptome data analysis and propose practical solutions which combine available genetic variants, genotypes, and genome reference sequence. The use of custom BXD line references improved downstream analysis compared to classical genome reference. These insights would likely benefit genetic studies with a transcriptomic component and demonstrate that genome references need to be reassessed and improved. Author summary: To understand how genetic variations affect behavior and cause disease it is common to quantify expression of transcripts by sequencing. Transcripts are extracted, fragmented, and the sequence of the fragments read. An important step for their quantification is to virtually assign the different fragments to the transcript they originate from using a reference genome. Reference genomes are costly to build, so usually only one high-quality reference per animal model species is available. When comparing genetically different individuals, using a single reference may introduce a bias because it might be more similar to some individuals than to others. Paradoxically, the variations at the core of genetic studies are thus ignored at the start of the analysis. We built customized references with known genetic variants for each of the mouse lines we had and quantified the impact of the reference at different levels of the bioinformatic analysis. We found that using customized references reduced the bias compared to using a single reference. Our study uses publicly available data and tools, so others can easily implement this improvement in their analyses. [ABSTRACT FROM AUTHOR]

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