Answer: The genome is the complete set of DNA in an organism, containing all the genetic information required for growth, development, and functioning. The transcriptome, on the other hand, is the complete set of RNA transcripts produced from the genome under specific conditions. The transcriptome represents the actively expressed genes at any given time, reflecting the dynamic nature of gene expression.

Answer: Transcriptomics provides insights into which genes are being transcribed into RNA, the levels of their expression, and how these levels change in response to different conditions. This information helps to uncover the regulatory mechanisms controlling gene expression, such as the role of transcription factors, enhancers, and silencers.

Answer: RNA-seq offers higher resolution and sensitivity compared to microarrays. It can detect a broader range of transcripts, including low-abundance and novel transcripts, and provides more accurate quantification of gene expression levels. Additionally, RNA-seq is not limited by the predefined probes used in microarrays, allowing for the discovery of previously unknown genes and splice variants.

Answer: The main steps in an RNA-seq experiment include RNA extraction from biological samples, RNA fragmentation, conversion of RNA into complementary DNA (cDNA), library preparation, high-throughput sequencing, and data analysis. The data analysis typically involves quality control, read alignment, transcript assembly, quantification of gene expression, and differential expression analysis.

Answer: The quality of RNA-seq data is assessed using various metrics such as base quality scores, GC content, sequence duplication levels, and the presence of adapter sequences. Tools like FastQC are commonly used to evaluate these metrics and identify any issues with the raw sequence data. Ensuring high-quality data is crucial for accurate downstream analysis.

Answer: Normalizing RNA-seq data is essential to account for variations in sequencing depth and other technical biases between different samples. Normalization methods, such as RPKM (Reads Per Kilobase of transcript, per Million mapped reads) or TPM (Transcripts Per Million), adjust the raw read counts to allow for accurate comparison of gene expression levels across samples.

Answer: STAR (Spliced Transcripts Alignment to a Reference) is an ultrafast aligner that provides high accuracy and speed for mapping RNA-seq reads to reference genomes. It is capable of handling large and complex genomes and can align reads across splice junctions, making it particularly suitable for transcriptomic studies. STAR also offers options for detecting novel splice junctions and fusion transcripts.

Answer: Both DESeq2 and EdgeR are R packages used for differential expression analysis in RNA-seq data, but they use different statistical methods. DESeq2 employs a model based on the negative binomial distribution to estimate variance and test for differential expression. EdgeR also uses the negative binomial distribution but incorporates empirical Bayes methods to shrink dispersion estimates, which can improve the accuracy of differential expression calls in small sample sizes.

Answer: By comparing gene expression profiles between healthy and diseased tissues, transcriptomics can identify specific RNA molecules that are differentially expressed. These differentially expressed genes can serve as biomarkers for disease diagnosis, prognosis, and treatment response. Biomarkers identified through transcriptomics can aid in the development of personalized medicine approaches, improving patient outcomes.

Answer: Challenges associated with scRNA-seq include the technical difficulty of isolating individual cells, the low amount of starting material, which can lead to high levels of technical noise and dropout events, and the complexity of data analysis. Specialized computational tools and algorithms are required to handle the sparsity and variability of scRNA-seq data and to accurately interpret the results. Despite these challenges, scRNA-seq provides valuable insights into cellular heterogeneity and rare cell populations.

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