Identify, quantify and annotate SNPs without any reference genome, using RNA-seq data only

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.

Researchers from Universit´e de Lyon have now proposed a method that identifies, quantifies and annotates SNPs without any reference genome, using RNA-seq data only. Using human RNA-seq data, the researchers first compared the performance of our method with GATK, a well established method that requires a reference genome and showed that both methods predict SNPs with similar accuracy. They then validated experimentally the predictions of their 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 proteins. The researchers further enable to test for the association of the identified SNPs with a phenotype of interest.


With fasta/fastq input from an RNA-seq experiment, SNPs are found by Kis-Splice without using a reference. As KisSplice provides only a local context around the SNPs, a reference can be built with Trinity, and SNPs can be positioned on whole transcripts. Some SNPs that do not map on the transcripts of Trinity, called orphan SNPs, are harder to study but can still be of interest. We propose a statistical method, called kissDE to find condition-specific SNPs (even if they are not positioned) out of all SNPs found. Finally, we can also predict a functional impact for the positioned SNPs, and intersect these results with condition-specific SNPs using our package KisSplice2-RefTranscriptome (K2rt).

Maestre HL et al. (2015) De novo identification, differential analysis and functional annotation of SNPs from RNA-seq data in non-model species. bioRXiv [Epub ahead of print]. [abstract]

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