Amadis Pages | bioRxiv Science

bioRxiv2016

The discovery potential of RNA processing profiles

Small non-coding RNAs are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, the first unsupervised computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare small non-coding RNAs (sncRNAs) by harnessing the power of read profiles. We applied SeRPeNT to: a) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, b) show pervasive differential processing between cell compartments and c) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of small non-coding RNAs at unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.

Genomics↗

bioRxiv2016

The prognostic potential of alternative transcript isoforms across human tumors

BackgroundPhenotypic changes during cancer progression are associated to alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival.\n\nMethodsUsing information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new predictive signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor status and melanoma tumors with proliferative and invasive phenotypes.\n\nResultsTranscript isoform signatures accurately separate early from late stage and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures, and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to estrogen receptor status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup.\n\nConclusionsOur analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis, and may potentially contribute towards current strategies of precision cancer medicine.

Cancer Biology↗