SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms. Muyle, A., Käfer, J., Zemp, N., Mousset, S., Picard, F., & Marais, G., A. Genome Biology and Evolution, 8(8):2530-2543, 2016.
SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms [pdf]Paper  SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms [link]Website  abstract   bibtex   
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 non-recombining and repeat-dense Y, W, U and V) are notoriously difficult to sequence. Strategies have been developed to obtain partially assembled sex chromosome sequences. 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 Silene data show that our method identifies 20 to 35% more sex-linked genes than existing pipelines, while making reliable inferences for downstream analyses. ~12 individuals are needed for optimal results based on simulations. For species with an 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 organisms, including non-model ones for which nothing is known a priori, that can be bred in the lab, are suitable for our method. SEX-DETector and its implementation in a Galaxy workflow are made freely available.
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 title = {SEX-DETector: A Probabilistic Approach to Study Sex Chromosomes in Non-Model Organisms},
 type = {article},
 year = {2016},
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 keywords = {galaxy workflow,rna-seq,sex-linked genes,uv,xy,zw},
 pages = {2530-2543},
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 abstract = {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 non-recombining and repeat-dense Y, W, U and V) are notoriously difficult to sequence. Strategies have been developed to obtain partially assembled sex chromosome sequences. 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 Silene data show that our method identifies 20 to 35% more sex-linked genes than existing pipelines, while making reliable inferences for downstream analyses. ~12 individuals are needed for optimal results based on simulations. For species with an 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 organisms, including non-model ones for which nothing is known a priori, that can be bred in the lab, are suitable for our method. SEX-DETector and its implementation in a Galaxy workflow are made freely available.},
 bibtype = {article},
 author = {Muyle, Aline and Käfer, Jos and Zemp, Niklaus and Mousset, Sylvain and Picard, Franck and Marais, Gabriel AB},
 journal = {Genome Biology and Evolution},
 number = {8}
}
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