How to perform a gene expression analysis with rna sequencing?10 answersTo perform a gene expression analysis with RNA sequencing (RNA-seq), a comprehensive approach integrating various methodologies and tools is essential for accurate and insightful outcomes. Initially, RNA-seq technology is employed to quantify RNA levels in a biological sample, leveraging high-throughput sequencing to capture genome-wide gene expression data. This process begins with the extraction of sample RNA, followed by enrichment and cDNA library preparation, which are crucial steps to ensure the quality and reliability of the sequencing data.
For specific applications, such as profiling gene expression without the need for alternative splicing information, mRNA 3′ termini counting approaches like 3′ Tag-Seq or MTAS-seq offer cost-effective alternatives. These methods focus on the 3′ end of transcripts, enabling efficient and accurate transcript abundance estimation. The 3’seq-RNA Profiling (3’SRP) technique further enhances this by allowing genome-wide transcriptional profiling at a reduced cost through sample multiplexing and molecular indexing.
Once sequencing is completed, quality control checks are imperative. Tools like FastQC can assess the quality of the raw sequencing data, followed by trimming of sequence adapters. The next step involves aligning the quality-checked reads to a reference genome, a process that can be accomplished using alignment tools such as STAR. For differential gene expression analysis, software packages like DESeq2 are used to compare gene count data across different conditions or samples.
In cases where single-cell resolution is desired, single-cell RNA sequencing (scRNA-seq) technologies enable the profiling of gene expression in individual cells. This approach is particularly useful for identifying different cell subpopulations and their marker genes, as well as for inferring cellular developmental or differentiation trajectories.
Finally, the integration of multi-omic analysis and functional annotation, utilizing databases and tools for gene ontology, protein-protein interactions, and pathway analysis, enriches the interpretation of RNA-seq data. This allows for the identification of key genes, biological pathways, and potential regulatory elements such as transcription factors and miRNAs involved in specific conditions or diseases.
In summary, performing gene expression analysis with RNA-seq involves a series of steps from sample preparation, sequencing, quality control, alignment, and differential expression analysis, to functional annotation and multi-omic integration, each facilitated by specific methodologies and computational tools.
When was the Transcriptome Shotgun Assembly (TSA) database established?5 answersThe Transcriptome Shotgun Assembly (TSA) database was established in the year 2006 as part of the TIGR Plant Transcript Assemblies (TA) database, which utilizes expressed sequences from the NCBI GenBank Nucleotide database for transcript assembly. The TSA approach was also employed in the study of bunching onion transcriptomes using next-generation sequencing technologies to generate unigenes for marker development and genetic mapping. Additionally, the concept of reconstruction of graphs and simplicial complexes using shotgun assembly techniques was explored in various studies, indicating the reconstructability of complex structures based on specific parameters.
How to campare transcriptome analysis of two genetic species?4 answersComparative transcriptome analysis of two genetic species can be performed using a combination of alignment-dependent and alignment-independent methods. Alignment of RNA sequencing reads to a pooled reference index, followed by classification of reads by species and re-alignment with individual genomes, can achieve high accuracy across a range of species ratios. Alternatively, alignment-independent methods such as convolutional neural networks can be used to extract conserved patterns of sequences from two species and classify RNA sequencing reads with high accuracy. Additionally, converting gene expression patterns into co-expression networks and applying network module-finding algorithms can help establish one-to-one mapping of developmental stages between two species. These methods provide valuable strategies for comparing transcriptome analysis of different genetic species.
What are the most recent advances in single-cell transcriptomics?4 answersRecent advances in single-cell transcriptomics include the development of high-throughput scRNA-seq protocols using microchip technologies, such as microfluidics and microwell-based platforms, for profiling the transcriptome of thousands of individual cells at single-cell resolution. Another advancement is the improvement of differential expression (DE) analysis tools for scRNA-seq and spatially resolved transcriptomics (SRT) data, addressing the unique characteristics and challenges of detecting DE genes in these datasets. Additionally, the introduction of scFAST-seq, a single-cell full-length RNA sequencing method, enables the exploration of the entire transcriptome, including non-polyadenylated transcripts, with advantages in sensitivity, transcript length coverage, splice junction identification, and RNA velocity calculation. Furthermore, the development of Live-seq allows temporal analysis of single-cell transcriptomic profiles by using picoliter scale single-cell cytoplasmic biopsies. In the field of plant biology, single-cell transcriptomics has revolutionized the study of plant growth and development, with recent advancements in obtaining high-resolution transcriptomics data and the potential for advancing research in plant biotechnology.
How did the first modern RNA-sequencing?4 answersThe first modern RNA-sequencing method involved sequencing RNA messages multiple times to map the boundaries of expressed sequences across the genome. Initially, short-read sequencing methods were used, where each sequenced fragment contributed around 30 nucleotides of usable sequence. However, this approach required computational assembly of the fragments to reconstruct the full-length transcripts, which was challenging due to splicing and sequencing errors. To overcome these limitations, long-read sequencing technologies were developed. For example, the Pacific Biosciences platform enabled the sequencing of full-length RNA molecules, providing a more accurate picture of the transcriptome. Another advancement in RNA sequencing was the development of third-generation sequencing (TGS) platforms, which allowed sequencing of single molecules without the need for amplification or modification steps. This opened the door for true-direct RNA sequencing, including ancient RNA (aRNA). Additionally, a method using protein nanopores on the Oxford Nanopore MinION sequencing platform enabled the direct sequencing of complete RNA virus genomes, eliminating the need for cDNA synthesis.
When did RNA seq start?10 answers