ABSTRACT: De novo assembly of a non-model species transcriptome using Illumina data: assembly performance dependent on method used upon the snail transcriptome (Radix balthica, Pulmonata, Basommatophora).
Project description:Expression of the EMT-inducing transcription factor Snail is enhanced in different human cancers. To investigate the in vivo role of Snail during progression of epithelial cancer, we used a mouse model with skin-specific overexpression of Snail. Snail transgenic mice spontaneously developed distinct histological subtypes of skin cancer, such as basal cell carcinoma, squamous cell carcinoma and sebaceous gland carcinoma. Development of sebaceous gland carcinomas strongly correlated with the direct and complete repression of Blimp-1, a central regulator of sebocyte homeostasis. Snail expression in keratinocyte stem cells significantly promotes their proliferation associated with an activated FoxM1 gene expression signature, resulting in a larger pool of Mts24-marked progenitor cells. Furthermore, primary keratinocytes expressing Snail showed increased survival and strong resistance to genotoxic stress. Snail expression in a skin-specific p53-null background resulted in accelerated formation of spontaneous tumours and enhanced metastasis. Our data demonstrate that in vivo expression of Snail results in de novo epithelial carcinogenesis by allowing enhanced survival, expansion of the cancer stem cell pool with accumulated DNA damage, a block in terminal differentiation and increased proliferation rates of tumour-initiating cells.
Project description:Dependent on concise, pre-defined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large scale proteomics datasets, and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) which leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.
