Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.

Diverse genetic alteration dysregulates neuropeptide and intracellular signalling in the suprachiasmatic nuclei

In mammals, intrinsic 24 h or circadian rhythms are primarily generated by the suprachiasmatic nuclei (SCN). Rhythmic daily changes in the transcriptome and proteome of SCN cells are controlled by interlocking transcription-translation feedback loops (TTFLs) of core clock genes and their proteins. SCN cells function as autonomous circadian oscillators, which synchronize through intercellular neuropeptide signalling. Physiological and behavioural rhythms can be severely disrupted by genetic modification of a diverse range of genes and proteins in the SCN. With the advent of next generation sequencing, there is unprecedented information on the molecular profile of the SCN and how it is affected by genetically targeted alteration. However, whether the expression of some genes is more readily affected by genetic alteration of the SCN is unclear. Here, using publicly available datasets from recent RNA-seq assessments of the SCN from genetically altered and control mice, we evaluated whether there are commonalities in transcriptome dysregulation. This was completed for four different phases across the 24 h cycle and was augmented by Gene Ontology Molecular Function (GO:MF) and promoter analysis. Common differentially expressed genes (DEGs) and/or enriched GO:MF terms included signalling molecules, their receptors, and core clock components. Finally, examination of the JASPAR database indicated that E-box and CRE elements in the promoter regions of several commonly dysregulated genes. From this analysis, we identify differential expression of genes coding for molecules involved in SCN intra- and intercellular signalling as a potential cause of abnormal circadian rhythms.

A comprehensive workflow for optimizing RNA-seq data analysis

BACKGROUND

Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge.

RESULTS

By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz.

CONCLUSION

The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Evaluation of Near-Infrared Laser Effects on 143B Cells: A System Biology Approach

Introduction: Photothermal therapy (PTT) by using a near-infrared (NIR) laser, as a successful treatment of cancer, has attracted extensive attention of researchers. Its advantages as a noninvasive and suitable method have been confirmed. Discovery of the NIR laser molecular mechanism at the cellular level via system biology assessment to identify the crucial targeted genes is the aim of this study. Methods: RNA-seq series of six samples were retrieved from Gene Expression Omnibus (GEO) and pre-evaluated by the GEO2R program for more analysis. The significant differentially expressed genes (DEGs) were determined and studied via gene expression analysis, protein-protein interaction (PPI) network assessment, action map evaluation, and gene ontology enrichment. Results: HSPA5, DDIT3, TRIB3, PTGS2, HMOX1, ASNS, GDF15, SLC7A11, and SQSTM1 were identified as central genes. Comparing the central genes and the determined crucial genes via gene expression analysis, actin map results, and gene ontology enrichment led to the introduction of HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 as critical genes in response to the NIR laser. Conclusion: The results indicated that the principle biological process "Endoplasmic reticulum unfolded protein response" and HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 are the crucial targets of the NIR laser. The results also showed that the NIR laser induces stress conditions in the irradiated cells.

Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report. •MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

Estimating Transcriptome Diversity and Specialization in Capsicum annuum L

Chili pepper fruits of the genus Capsicum represent excellent experimental models to study the growth, development, and ripening processes in a non-climacteric species at the physiological, biochemical, and molecular levels. Fruit growth, development, and ripening involve a complex, harmonious, and finely controlled regulation of gene expression. The purpose of this study was to estimate the changes in transcriptome diversity and specialization, as well as gene specificities during fruit development in this crop, and to illustrate the advantages of estimating these parameters. To achieve these aims, we programmed and made publicly available an R package. In this study, we applied these methods to a set of 179 RNA-Seq libraries from a factorial experiment that includes 12 different genotypes at various stages of fruit development. We found that the diversity of the transcriptome decreases linearly from the flower to the mature fruit, while its specialization follows a complex and non-linear behavior during this process. Additionally, by defining sets of genes with different degrees of specialization and applying Gene Ontology enrichment analysis, we identified processes, functions, and components that play a central role in particular fruit development stages. In conclusion, the estimation of diversity, specialization, and specificity summarizes the global properties of the transcriptomes, providing insights that are difficult to achieve by other means.

sRNAflow: A Tool for the Analysis of Small RNA-Seq Data

The analysis of small RNA sequencing data across a range of biofluids is a significant research area, given the diversity of RNA types that hold potential diagnostic, prognostic, and predictive value. The intricate task of segregating the complex mixture of small RNAs from both human and other species, including bacteria, fungi, and viruses, poses one of the most formidable challenges in the analysis of small RNA sequencing data, currently lacking satisfactory solutions. This study introduces sRNAflow, a user-friendly bioinformatic tool with a web interface designed for the analysis of small RNAs obtained from biological fluids. Tailored to the unique requirements of such samples, the proposed pipeline addresses various challenges, including filtering potential RNAs from reagents and environment, classifying small RNA types, managing small RNA annotation overlap, conducting differential expression assays, analysing isomiRs, and presenting an approach to identify the sources of small RNAs within samples. sRNAflow also encompasses an alternative alignment-free analysis of RNA-seq data, featuring clustering and initial RNA source identification using BLAST. This comprehensive approach facilitates meaningful comparisons of results between different analytical methods.

Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data

Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS-the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing.