KisSplice – SNP calling from RNA-seq data without a reference genome

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.

Here, Université de Lyon researchers 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, they clarify the precision and recall of their method and discuss them with respect to other methods which use a reference genome or an assembled transcriptome. The researchers then validate 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 the protein sequence. They further enable to test for the association of the identified SNPs with a phenotype of interest.

Results of KisSplice2RefTranscriptome


The green, red and blue areas correspond respectively to non-coding, synonymous and non-synonymous SNPs. The dashed area corresponds to errors of our predictions of the impact on the protein sequence. The outer area corresponds to SNPs that are not in dbSNP or for which the prediction cannot be evaluated due to a lack of annotation in dbSNP.

Availability – All the methods presented in this paper are implemented in software that are freely available at

Lopez-Maestre H, Brinza L, Marchet C, Kielbassa J, Bastien S, Boutigny M, Monnin D, Filali AE, Carareto CM, Vieira C, Picard F, Kremer N, Vavre F, Sagot MF, Lacroix V. (2016) SNP calling from RNA-seq data without a reference genome: identification, quantification, differential analysis and impact on the protein sequence. Nucleic Acids Res 44(19):e148. [article]

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