SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence.

SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot be applied to non-model species. They are also mostly tailored for whole genome (re-)sequencing experiments, whereas in many cases, transcriptome sequencing can be used as a cheaper alternative which already enables to identify SNPs located in transcribed regions. In this paper, we propose a method that identifies, quantifies and annotates SNPs without any reference genome, using RNA-seq data only. Individuals can be pooled prior to sequencing, if not enough material is available from one individual. Using pooled human RNA-seq data, we clarify the precision and recall of our method and discuss them with respect to other methods which use a reference genome or an assembled transcriptome. We then validate experimentally the predictions of our method using RNA-seq data from two non-model species. The method can be used for any species to annotate SNPs and predict their impact on the protein sequence. We further enable to test for the association of the identified SNPs with a phenotype of interest.

The gene expression analysis of blood reveals S100A11 and AQP9 as potential biomarkers of infective endocarditis.

BACKGROUND: The diagnostic and prognostic assessments of infective endocarditis (IE) are challenging. To investigate the host response during IE and to identify potential biomarkers, we determined the circulating gene expression profile using whole genome microarray analysis. METHODS AND RESULTS: A transcriptomic case-control study was performed on blood samples from patients with native valve IE (n = 39), excluded IE after an initial suspicion (n = 10) at patient's admission, and age-matched healthy controls (n = 10). Whole genome microarray analysis showed that patients with IE exhibited a specific transcriptional program with a predominance of gene categories associated with cell activation as well as innate immune and inflammatory responses. Quantitative real-time RT-PCR performed on a selection of highly modulated genes showed that the expression of the gene encoding S100 calcium binding protein A11 (S100A11) was significantly increased in patients with IE in comparison with controls (P<0.001) and patients with excluded IE (P<0.05). Interestingly, the upregulated expression of the S100A11 gene was more pronounced in staphylococcal IE than in streptococcal IE (P<0.01). These results were confirmed by serum concentrations of the S100A11 protein. Finally, we showed that in patients with IE, the upregulation of the aquaporin-9 gene (AQP9) was significantly associated with the occurrence of acute heart failure (P = 0.02). CONCLUSIONS: Using transcriptional signatures of blood samples, we identified S100A11 as a potential diagnostic marker of IE, and AQP9 as a potential prognostic factor.