Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Abstract

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

ClusTrast: a short readde novotranscript isoform assembler guided by clustered contigs

“…These examples, and others, e.g. Hayer et al (2015) and Thind et al (2021), demonstrate that there is still much room for improvement in de novo transcript isoform assembly.…”

“…However, when the goal is to find as many supported transcript isoforms as possible, compactness is not in itself desirable and could in fact be counter productive. In a recent review by Thind et al (2021), the authors point out that there is a need for metrics that better capture the performance with regards to tran-script isoforms. Our use of SQANTI’s approach to evaluation is an attempt to address this.…”

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

“…Nowadays, RNAseq has become the gold standard and the basic tool for transcriptomic profiling [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. In addition to measuring gene activities, RNAseq has also the potential of detecting mutations and overall tumor mutational burden [ 51 ], gene splice isoforms [ 52 ], and oncogenic fusion transcripts [ 53 , 54 , 55 , 56 ]. During the RNAseq era, a new group of cross-platform data comparison methods was developed [ 27 ].…”

A Framework for Comparison and Assessment of Synthetic RNA-Seq Data

“…The method has been established as a standard when studying how gene expression is altered by various experimental conditions, diseases or environments. A recent publication provides an overview of new bioinformatics algorithms developed for the purpose of retrieving previously inaccessible information from available RNA-seq data such as: cell type composition (deconvolution), copy number alteration, microbial contamination, and quantification of transposable elements and neoantigen prediction [ 3 ].…”

“…The computational analysis part incorporates filtering out low quality reads and adapter sequences, alignment to reference transcriptome, read abundance quantification, gene-based read counting and filtering, and normalization between samples and batches. The relevance of bulk RNA-seq is supported by large public databases (dbGAP, GEO) and its common use in translational research [ 3 ].…”