RNA-Seq Experiment and Data Analysis

Transcriptome analysis and characteristics of drought resistance related genes in four varieties of foxtail millet [Setaria italica]

Foxtail millet [Setaria italica] plays a crucial role as a multigrain crop in agricultural production. However, due to future extreme weather conditions, drought remains the main abiotic stress that limits foxtail millet yield, it is highly significant to screen for drought-tolerant varieties throughout the entire growth period and identify the regulatory genes associated with drought resistance in foxtail millet breeding. We identified 217 foxtail millet seed resources for drought resistance during the maturity stage in the field, and subsequently categorized them into different levels of drought resistance. Two cultivars with extremely strong drought resistance during the maturity stage in the field, JKH4 (Chi 5422) and JKH6 (Chigu 26), as well as two cultivars with extremely weak drought resistance during the maturity stage in the field, JRK3 (17M1309) and JRK6 (Canggu 9), were selected for physiological comparison and transcriptome sequencing before and after drought treatment. Transcriptome analysis at the seedling stage revealed that JRK3 and JRK6 cultivar primarily regulated phenylpropanoid biosynthesis, MAPK signaling pathogen-plant, and plant hormone signal transduction pathway in response to drought stress. On the other hand, the fatty acid elongation pathway of JKH4 and JKH6 variety was found to be more significant. Furthermore, 22 drought resistance related genes were screened through transcriptome analysis of four foxtail millet varieties. These findings could offer valuable theoretical guidance for breeding foxtail millet with enhanced drought resistance and potentially facilitate the development of genetically engineered drought-resistant foxtail millet varieties.

RNA-Seq Experiment and Data Analysis

With the ability to obtain several millions of reads per sample, high-throughput RNA sequencing (RNA-Seq) enables investigation of any transcriptome at a fine resolution. Not just the messenger RNA (mRNA), but a wide variety of different RNA populations (e.g., total RNA, microRNA, long ncRNA, pre-mRNA) can also be investigated using RNA-Seq. While facilitating accurate quantification of gene expression, RNA-Seq offers the opportunity to estimate abundance of isoforms and find novel transcripts and allele-specific transcripts. In this chapter, we describe a protocol to construct an RNA-Seq library for sequencing on Illumina NGS platforms and a computational pipeline to perform RNA-Seq data analysis. The protocols described in this chapter can be applied to the analysis of differential gene expression in control versus 17β-estradiol treatment of in vivo or in vitro systems.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Background

Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.

Results

In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.

Conclusions

We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00336-1.

Simultaneous sequencing of coding and noncoding RNA reveals a human transcriptome dominated by a small number of highly expressed noncoding genes

Comparing the abundance of one RNA molecule to another is crucial for understanding cellular functions but most sequencing techniques can target only specific subsets of RNA. In this study, we used a new fragmented ribodepleted TGIRT sequencing method that uses a thermostable group II intron reverse transcriptase (TGIRT) to generate a portrait of the human transcriptome depicting the quantitative relationship of all classes of nonribosomal RNA longer than 60 nt. Comparison between different sequencing methods indicated that FRT is more accurate in ranking both mRNA and noncoding RNA than viral reverse transcriptase-based sequencing methods, even those that specifically target these species. Measurements of RNA abundance in different cell lines using this method correlate with biochemical estimates, confirming tRNA as the most abundant nonribosomal RNA biotype. However, the single most abundant transcript is 7SL RNA, a component of the signal recognition particle. Structured noncoding RNAs (sncRNAs) associated with the same biological process are expressed at similar levels, with the exception of RNAs with multiple functions like U1 snRNA. In general, sncRNAs forming RNPs are hundreds to thousands of times more abundant than their mRNA counterparts. Surprisingly, only 50 sncRNA genes produce half of the non-rRNA transcripts detected in two different cell lines. Together the results indicate that the human transcriptome is dominated by a small number of highly expressed sncRNAs specializing in functions related to translation and splicing.