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

Abstract

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

“…Bulk RNA-seq has been widely applied for capturing aggregate snapshots of gene expression within tissues [1,2]. The advent of single-cell level technologies has brought in a new era, revealing cellular heterogeneity at a higher resolution that was not observable by bulk approaches [3][4][5].…”

“…Previous studies have shown differences in both gene expression and cell composition between singlecell, single-nucleus, and bulk RNA-seq data [4,[6][7][8][9][10]. In some cases, cell dissociation and processing in singlecell RNA-seq could introduce complete or partial cell loss in cell types that are adherent, sensitive, too large, or have low capture efficiency [6][7][8].…”

Human Amniotic MSC Response in LPS-Stimulated Ascites from Patients with Cirrhosis: FOXO1 Gene and Th17 Activation in Enhanced Antibacterial Activation

“…Moreover, we employed omics techniques to investigate the underlying mechanism by which hA-MSCs modulate the immune system. Omics sciences are a rapidly evolving class of disciplines that utilize high-throughput technologies such as genomics, transcriptomics, proteomics, metabolomics, and computational biology to advance research and clarify mechanisms underlying complex diseases [ 37 , 38 , 39 ]. The study of transcriptome changes and the comparison of gene expression profiles involved in various pathologies have the potential to advance both therapeutics and diagnostics in health care and promote preventive medicine.…”

Ambiguous genes due to aligners and their impact on RNA-seq data analysis

“…The aspect of comparing reproducibility and the impact of the alignment process on further analysis has been the subject of a large number of publications. Recently, the challenges of aligning RNA-seq reads to a reference genome have been discussed, in particular the handling of spliced junctions—different aligners may handle these spliced junctions differently, leading to variations in results 12 . It has also been noted that RNA-seq alignment tools typically have a user-defined threshold for tolerating mismatches in the alignment, although it is also important to distinguish between sequencing errors and true variation between transcripts, as in some cases reads may contain mismatches caused either by sequencing errors or biological variation due to mutations 12 , 13 .…”

“…Recently, the challenges of aligning RNA-seq reads to a reference genome have been discussed, in particular the handling of spliced junctions—different aligners may handle these spliced junctions differently, leading to variations in results 12 . It has also been noted that RNA-seq alignment tools typically have a user-defined threshold for tolerating mismatches in the alignment, although it is also important to distinguish between sequencing errors and true variation between transcripts, as in some cases reads may contain mismatches caused either by sequencing errors or biological variation due to mutations 12 , 13 . It has been argued that the differences depend on the specific requirements of the research project, including considerations of accuracy, run time, memory usage, output format, and specific features such as handling multi-reads or splice junctions, which may influence the choice of aligner.…”