Biology subjects

Tahira, A. C.

Publications and source records attributed to Tahira, A. C..

3 recordsLinked to original sources

bioRxiv2023

The CUT&RUN Greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is a recent development for epigenome mapping, but its unique methodology can hamper proper quantitative analyses. As traditional normalization approaches have been shown to be inaccurate, we sought to determine endogenous normalization factors based on regions of constant nonspecific signal. This constancy was determined by applying Shannons information entropy, and the set of normalizer regions, which we named the "greenlist," was extensively validated using publicly available datasets. We demonstrate here that the greenlist normalization outperforms the current top standards, and remains consistent across different experimental set-ups, cell lines, and antibodies; the approach can even be applied to other organisms or to CUT&Tag. Requiring no additional experimental steps and no added cost, this approach can be universally applied to CUT&RUN experiments to greatly minimize the interference of technical variation over the biological epigenome changes of interest.

bioinformatics↗

bioRxiv2023

The human developing cerebral cortex is characterized by an increased de novo expression of lncRNAs in excitatory neurons

BackgroundOutstanding human cognitive abilities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs (lncRNAs) have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many lncRNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the lncRNA repertoire expressed in the developing cerebral cortex of humans and explored their role in the evolution of this brain region. ResultsUsing systems biology approaches and comparative transcriptomics, we comprehensively annotated the cortical transcriptomes of humans, macaques, mice, and chickens and classified human cortical lncRNAs into evolutionary groups as a function of their predicted minimal ages. LncRNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older lncRNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory neurons. In comparison, younger lncRNAs showed preferential expression in cortical excitatory neurons, belonged to human-specific gene coexpression modules, and were dysregulated in autism spectrum disorder. ConclusionsThese results suggest a shift in the roles of cortical lncRNAs over evolution, highlighting the antique lncRNAs as a source of molecular evolution of conserved developmental programs; conversely, the de novo expression of primate and human-specific lncRNAs are sources of molecular evolution and dysfunction of cortical excitatory neurons.

evolutionary biology↗

bioRxiv2019

A Deep Learning approach predicts the impact of point mutations in intronic flanking regions on micro-exon splicing definition

While mammalian exons are on average 140-nt-long, thousands of human genes harbor micro-exons ([≤] 39 nt). Large numbers of micro-exons have their splicing altered in diseases such as autism and cancer, and yet there is no systematic assessment of the impact of point mutations in intronic flanking-sequences on the splicing of a neighboring micro-exon. Here, we constructed a model using the Convolutional Neural Network (CNN) to predict the impact of point mutations in intronic-flanking-sequences on the splicing of a neighboring micro-exon. The prediction model was based on both the sequence contents and conservation among species of the two 100-nt intronic regions (5 and 3) that flank all human micro-exons and a set with the same number of randomly selected long exons. After training our CNN model, the micro-exon splicing event prediction accuracy, using an independent validation dataset, was 0.71 with an area under the ROC curve of 0.76, showing that our model had identified sequence patterns that have been conserved in evolution in the introns that flank micro-exons. Next, we introduced in silico point mutations at each of the 200 nucleotides in the introns that flank a micro-exon and used the trained CNN algorithm to predict splicing for every mutated intronic sequence version. This analysis identified thousands of point mutations in the flanking introns that significantly decreased the power of the CNN model to correctly predict a neighboring micro-exon splicing event, thus pointing to predictive bases in intronic regions important for micro-exon splicing signaling. We found these predictive bases to locate within conserved RNA-binding-motifs for RNA-binding-proteins (RBPs) known to relate to micro-exon splicing. Experimental data of minigene splicing reporter changes upon intron-base point-mutation confirmed the effect predicted by the CNN model for some of the micro-exon splicing events. The model can be used for validating novel micro-exons de novo assembled from RNA-seq data, and for an unbiased screening of introns, identifying genomic bases that have high micro-exon-splicing predictive power, possibly revealing critical point mutations that would be related in a yet unknown manner to a given disease.

bioinformatics↗