Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development

By Linda Szabo, Robert Morey, N. Palpant, Peter L Wang, Nastaran Afari, Chuan Jiang, M. Parast, Charles E. Murry, L. Laurent, Julia Salzman. Genome Biology, 2015

557 citations recorded by the source corpus.

The pervasive expression of circular RNA is a recently discovered feature of gene expression in highly diverged eukaryotes, but the functions of most circular RNAs are still unknown. Computational methods to discover and quantify circular RNA are essential. Moreover, discovering biological contexts where circular RNAs are regulated will shed light on potential functional roles they may play. We present a new algorithm that increases the sensitivity and specificity of circular RNA detection by discovering and quantifying circular and linear RNA splicing events at both annotated and un-annotated exon boundaries, including intergenic regions of the genome, with high statistical confidence. Unlike approaches that rely on read count and exon homology to determine confidence in prediction of circular RNA expression, our algorithm uses a statistical approach. Using our algorithm, we unveiled striking induction of general and tissue-specific circular RNAs, including in the heart and lung, during human fetal development. We discover regions of the human fetal brain, such as the frontal cortex, with marked enrichment for genes where circular RNA isoforms are dominant. The vast majority of circular RNA production occurs at major spliceosome splice sites; however, we find the first examples of developmentally induced circular RNAs processed by the minor spliceosome, and an enriched propensity of minor spliceosome donors to splice into circular RNA at un-annotated, rather than annotated, exons. Together, these results suggest a potentially significant role for circular RNA in human development.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ef0f15e9608dbc620f7b1ebb868de8bb09f1fd53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Detecting circular RNAs: bioinformatic and experimental challenges (2016, methodology): “The benefit of statistical approaches for circRNA detection was highlighted in a recent paper 5 in which several apparently highly expressed circRNA junctions that contained exons from different but homologous genes, discarded post-facto as probable RT or alignment arte-facts by other algorithms,…”
Circular RNA Expression: Its Potential Regulation and Function (2016, background): “Recently, the question regarding the generality of developmental regulation has been addressed by several groups in multiple organisms: circRNA expression from hundreds of genes was found to be induced during early development, including in humans, flies, and pigs [9,55,58].”
The bioinformatics toolbox for circRNA discovery and analysis (2020, methodology): “KNIFE, segemehl, CIRI, PTESFinder and CIRCexplorer achieved the best sensitivity [87].”
CircNet: a database of circular RNAs derived from transcriptome sequencing data (2015, background): “CircRNAs functional correlation with brain function, synaptic plasticity (26,27) and fetal development (28) has recently been reported.”
Specific identification and quantification of circular RNAs from sequencing data (2016, methodology): “DCC predicts fewer circRNA candidates than KNIFE .”
Circular RNA-protein interactions: functions, mechanisms, and identification (2020, background): “Numerous circRNAs exhibit specificity to certain tissues and developmental periods [5, 31, 32].”
Comparison of circular RNA prediction tools (2015, methodology): “In the above comparison, KNIFE (19) and segemehl (20), two other recently developed pipelines for circRNA prediction, were not included as we failed to implement and successfully run these algorithms on the sequencing datasets used here.”
Circular RNAs: analysis, expression and potential functions (2016, methodology): “The developmental induction of NCX1 circRNA expression was also recapitulated in vitro with experiments differentiating human embryonic stem cells to cardiomyocytes over the course of 2 weeks (Szabo et al., 2015).”
Circular RNA and its mechanisms in disease: From the bench to the clinic. (2018, background): “circRNAs also accumulate in some physiological processes, such as neuronal differentiation, fetal development and synaptic development (RybakWolf et al., 2015; Szabo et al., 2015; Westholm et al., 2014; You et al., 2015), and this accumulation of circRNAs indicates that they might function in these ”