AUTHOR=Deshpande Dhrithi , Chhugani Karishma , Chang Yutong , Karlsberg Aaron , Loeffler Caitlin , Zhang Jinyang , Muszyńska Agata , Munteanu Viorel , Yang Harry , Rotman Jeremy , Tao Laura , Balliu Brunilda , Tseng Elizabeth , Eskin Eleazar , Zhao Fangqing , Mohammadi Pejman , P. Łabaj Paweł , Mangul Serghei 

TITLE=RNA-seq data science: From raw data to effective interpretation

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.997383

DOI=10.3389/fgene.2023.997383

ISSN=1664-8021

ABSTRACT=RNA-sequencing (RNA-seq) has become an exemplar technology in modern biology and clinical applications over the past decade. It has gained immense popularity in recent years driven by continuous efforts of the bioinformatics community to develop accurate and scalable computational tools. RNA-seq is a method of analyzing the RNA content of a sample using the modern sequencing platforms. It generates enormous amounts of transcriptomic data in the form of nucleotide sequences, known as reads. RNA-seq analysis enables the probing of genes and corresponding transcripts which is essential for answering important biological questions, such as detecting novel exons or the whole, transcripts, assessing gene and their alternative transcripts expressions, and studying alternative splicing structure. However, obtaining meaningful biological signals from raw data using computational methods is challenging and one of the reasons is due to the limitations of modern sequencing technologies. The need to leverage these technological challenges have pushed the rapid development of many novel computational tools which have evolved and diversified in accordance with technological advancements, leading to the current myriad population of RNA-seq tools.