Author

Doaa Hassan

National Telecommunications InstituteORCID 0000-0002-7358-0830Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Organism:human· 3 Assay:long-read sequencing· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1

Frequent Co-Authors

Sarath Chandra JangaiD· 3 shared Quoseena MiriD· 2 shared Swapna Vidhur DaulatabadiD· 2 shared Aditya Ariyur· 1 shared Arthur G. HuntiD· 1 shared ASHWINI SHIVAKUMAR· 1 shared Carli Camporeale· 1 shared Cassandra Catacalos-GoadiD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersGenomics

Sarath Chandra JangaORCID

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3 papers

Recent·Most cited

Nucleotide-resolution Mapping of RNA N6-Methyladenosine (m6A) modifications and comprehensive analysis of global polyadenylation events in mRNA 3' end processing in malaria pathogen Plasmodium falciparum

Cassandra Catacalos-Goad, Manohar Chakrabarti, Doaa Hassan, Carli Camporeale, Sahiti Somalraju, Matthew Tegowski, Kaitlin Klotz, ASHWINI SHIVAKUMAR, Lichun Zhou, Ruchi Singh, Robert W. Reid, Daniel A Janies, Shaoyu Li, Kate D. Meyer, Sarath Chandra Janga, , Kausik Chakrabarti

The paper maps m6A modifications and polyadenylation events in Plasmodium falciparum mRNAs, identifying stage-specific m6A sites and their potential role in 3' end processing.

RNA Biology · Oct 28, 2026Peer-reviewedGenomics

Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data

Doaa Hassan, Daniel Acevedo, Swapna Vidhur Daulatabad, Quoseena Mir, Sarath Chandra Janga

Penguin is a tool that uses machine learning to predict pseudouridine sites in direct RNA nanopore sequencing data, achieving high accuracy compared to existing methods.

Methods · Jul 1, 2022Peer-reviewedGenomics

Nm-Nano: A Machine Learning Framework for Transcriptome-Wide Single Molecule Mapping of 2{acute}-O-Methylation (Nm) Sites in Nanopore Direct RNA Sequencing Datasets

Doaa Hassan, Aditya Ariyur, Swapna Vidhur Daulatabad, Quoseena Mir, Sarath Chandra Janga

Nm-Nano predicts Nm sites in Nanopore RNA sequencing data with high accuracy, identifying frequently modified genes in human cell lines and their biological roles.

bioRxiv · Jan 3, 2022 · PreprintBioinformatics