Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms.
Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms. Four samples were sequenced, each one coming from frozen brain tissue (frontal cortex) of a deceased female human patient with no remarkable pathology.
Project description:A mutualistic relationship between reef-building corals and endosymbiotic algae (Symbiodinium spp.) forms the basis for the existence of coral reefs. Genotyping tools for Symbiodinium spp. have added a new level of complexity to studies concerning cnidarian growth, nutrient acquisition, and stress. For example, the response of the coral holobiont to thermal stress is connected to the host-Symbiodinium genotypic combination, as different partnerships can have different bleaching susceptibilities. If, and to what extent, differences in algal symbiont clade contents can exert effects on the coral host transcriptome is currently unknown. In this study, we monitored algal physiological parameters and profiled the coral host transcriptional responses in acclimated, thermally stressed, and recovered coral fragments using a custom cDNA gene expression microarray. Combining these analyses with results from algal and host genotyping revealed a striking symbiont effect on both the acclimated coral host transcriptome and the magnitude of the thermal stress response. This is the first study that links coral host transcriptomic patterns to the clade content of their algal symbiont community. Our data provide a critical step to elucidating the molecular basis of the apparent variability seen among different coral-algal partnerships.
Project description:Coral reefs are based on the symbiotic relationship between corals and photosynthetic dinoflagellates of the genus Symbiodinium. We followed gene expression of coral larvae of Acropora palmata and Montastraea faveolata after exposure to Symbiodinium strains that differed in their ability to establish symbioses. We show that the coral host transcriptome remains almost unchanged during infection by competent symbionts, but is massively altered by symbionts that fail to establish symbioses. Our data suggest that successful coral-algal symbioses depend mainly on the symbionts' ability to enter the host in a stealth manner rather than a more active response from the coral host.