Author

Ana Carolina Tahira

Instituto ButantanORCID 0000-0002-7036-7305Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:gene regulatory network inference· 2 Organism:human· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Sergio Verjovski‐AlmeidaiD· 2 shared Alyssa Rossi BorgesiD· 1 shared David Pires· 1 shared Fabio Nunes de MelloiD· 1 shared Júlia Pinheiro Chagas da CunhaiD· 1 shared Juliana Nunes RosóniD· 1 shared Leticia de Sousa LopesiD· 1 shared Lucas F. daSilvaiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersBioinformatics

Sergio Verjovski‐AlmeidaORCID

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3 papers

Recent·Most cited

Molecular BiologybioRxivPreprintApr 16, 2024

Comprehensive Analysis of Nascent Transcriptome Reveals Diverse Transcriptional Profiles Across the Trypanosoma cruzi Genome Underlining the Regulatory Role of Genome Organization, Chromatin Status, and Cis-Acting Elements

Pedro Leonardo Carvalho de Lima, Leticia de Sousa Lopes, Juliana Nunes Rosón…·Júlia Pinheiro Chagas da Cunha

The paper analyzes nascent transcriptomes of Trypanosoma cruzi to show asymmetric transcriptional abundance and links it to genome organization, chromatin status, and cis-acting elements.

8 citations

whole-genome sequencingstatistical model

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BioinformaticsBriefings in BioinformaticsPeer-reviewedDec 27, 2023

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

Fabio Nunes de Mello, Ana Carolina Tahira, Maria Gabriela Berzoti-Coelho, Sergio Verjovski‐Almeida

The paper introduces a normalization method for CUT&RUN data using constant nonspecific signal regions, showing it outperforms existing standards across multiple conditions.

17 citations

humanChIP-seq

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BioinformaticsbioRxivPreprintDec 7, 2019

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

Lucas F. daSilva, Ana Carolina Tahira, Vinícius Mesel, Sergio Verjovski‐Almeida

This paper develops a CNN model to predict how point mutations in intronic flanking regions affect micro-exon splicing, identifying conserved RNA-binding motifs as key predictors.

DOI 10.1101/868786

humanDNA methylation

𝕏 PostBluesky