SEX-DETector: a probabilistic approach to uncover sex chromosomes in non-model organisms

We propose a probabilistic framework to infer autosomal and sex-linked genes from RNA-seq data of a cross for any sex chromosome type (XY, ZW, UV). Sex chromosomes (especially the nonrecombining and repeat-dense Y, W, U and V) are notoriously difficult to sequence. Strategies have been developed to obtain partially assembled sex chromosome sequences. However, most of them remain difficult to apply to numerous non-model organisms, either because they require a reference genome, or because they are designed for evolutionarily old systems. Sequencing a cross (parents and progeny) by RNA-seq to study the segregation of alleles and infer sex-linked genes is a cost-efficient strategy, which also provides expression level estimates. However, the lack of a proper statistical framework has limited a broader application of this approach. Tests on empirical data show that our method identifies many more sex-linked genes than existing pipelines, while making reliable inferences for downstream analyses. Simulations suggest few individuals are needed for optimal results. For species with unknown sex-determination system, the method can assess the presence and type (XY versus ZW) of sex chromosomes through a model comparison strategy. The method is particularly well optimised for sex chomosomes of young or intermediate age, which are expected in thousands of yet unstudied lineages. Any organism, including non-model ones for which nothing is known a priori, that can be bred in the lab, is suitable for our method. SEX-DETector is made freely available to the community through a Galaxy workflow. key words: XY, ZW, UV, sex-linked genes, RNA-seq, Galaxy workflow. 2 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/023358 doi: bioRxiv preprint first posted online Jul. 27, 2015;