Lecture 8 RNA-Seq
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Which of the following is a primary advantage of RNA-Seq over microarrays?

  • RNA-Seq cannot measure gene expression levels
  • RNA-Seq requires pre-designed probes for detection
  • RNA-Seq can only detect abundant transcripts
  • RNA-Seq offers a greater dynamic range (correct)

    • RNA-Seq is used exclusively for cancer research.

    False

    What is the primary purpose of RNA-Seq?

    To sequence and quantify RNA in a sample.

    RNA-Seq enables comprehensive analysis of the ______, which is the complete set of RNA transcripts produced by the genome.

    <p>transcriptome</p> Signup and view all the answers

    Match the RNA-Seq applications with their descriptions:

    <p>Quantifying gene expression = Measuring gene expression levels across conditions Transcript discovery = Identifying novel RNA molecules Alternative splicing = Discovering isoforms and splice variants Network analysis = Revealing gene-gene interactions</p> Signup and view all the answers

    What does RNA-Seq help to quantify?

    <p>Gene expression levels</p> Signup and view all the answers

    RNA-Seq can only detect known transcripts.

    <p>False</p> Signup and view all the answers

    Name one application of RNA-Seq in research.

    <p>Applications include cancer study, neuroscience, and developmental biology.</p> Signup and view all the answers

    What is the recommended number of reads to adequately capture most differentially expressed genes?

    <p>10-20 million reads</p> Signup and view all the answers

    More biological replicates lead to decreased accuracy in RNA-Seq data analysis.

    <p>False</p> Signup and view all the answers

    What does sequencing depth refer to?

    <p>The number of reads generated per sample.</p> Signup and view all the answers

    RNA-Seq normalization methods correct for variations in total read counts and allow for comparison of gene expression between ______.

    <p>samples</p> Signup and view all the answers

    Match the normalization methods with their descriptions:

    <p>RPKM = Normalizes for gene length and sequencing depth FPKM = Similar to RPKM for paired-end reads TPM = Normalizes within each sample, consistent across samples None = Indicates no normalization method used</p> Signup and view all the answers

    Which of the following pipelines is known for strong performance in counting reads?

    <p>HTSeq</p> Signup and view all the answers

    Uniform coverage across the transcriptome is desired to avoid biases in downstream analyses.

    <p>True</p> Signup and view all the answers

    What are the goals for having a sufficient sequencing depth in RNA-Seq?

    <p>To detect low-expressed genes and quantify highly expressed genes.</p> Signup and view all the answers

    Which type of RNA typically represents the focus of RNA-Seq experiments?

    <p>Messenger RNA (mRNA)</p> Signup and view all the answers

    Ribosomal RNA constitutes approximately 50-60% of total RNA in a cell.

    <p>False</p> Signup and view all the answers

    What are the two main types of replicates considered in RNA-Seq experimental design?

    <p>Biological replicates and technical replicates</p> Signup and view all the answers

    Poly-A selection enriches for _____ by binding to polyadenylated tails.

    <p>mRNA</p> Signup and view all the answers

    Match the following RNA extraction methods with their description:

    <p>TRIzol = A reagent used for extracting RNA from samples Column-based kits = Commercial kits utilizing columns for RNA purification Poly-A selection = A method focusing on polyadenylated mRNA enrichment Ribodepletion = A technique for removing rRNA without selection bias</p> Signup and view all the answers

    What is a primary challenge in RNA sample preparation?

    <p>RNA is fragile and prone to degradation</p> Signup and view all the answers

    Library preparation creates cDNA from RNA.

    <p>True</p> Signup and view all the answers

    What is the main goal of RNA-Seq experimental design?

    <p>To maximize biological signal detection while minimizing technical noise and bias.</p> Signup and view all the answers

    What does RPKM stand for?

    <p>Reads per kilobase of transcript per million reads</p> Signup and view all the answers

    TMM is only applicable for comparing different genes within the same sample.

    <p>False</p> Signup and view all the answers

    What is the purpose of RPKM normalization?

    <p>To allow the comparison of transcripts within and between samples.</p> Signup and view all the answers

    The TMM method trims extreme values from M-values to calculate normalization factors, using the mean of the remaining M-values to scale counts for ______.

    <p>samples</p> Signup and view all the answers

    Match the following methods with their key characteristics:

    <p>TMM = Trims extreme values and uses the mean of the remaining M-values RPKM = Corrects for size of the library and length of the gene Both methods = Enable comparison of transcripts across different contexts</p> Signup and view all the answers

    What is the purpose of TMM normalization in RNA-Seq analysis?

    <p>It creates an ‘effective library size’ for the whole library.</p> Signup and view all the answers

    Differences in library content can affect gene expression levels in RNA-Seq analysis.

    <p>True</p> Signup and view all the answers

    The method used by EdgeR for group normalization is called _____.

    <p>TMM</p> Signup and view all the answers

    Which gene had the highest expression level in the leaf tissue based on the provided data?

    <p>Rubisco</p> Signup and view all the answers

    Match the following genes with their expression levels in Leaf Tissue and Root Tissue:

    <p>AtCul1 = 50, 250 Rubisco = 400, 0 AD = 25, 125 SFT = 25, 125</p> Signup and view all the answers

    Samples that are similar to each other do not require TMM normalization.

    <p>True</p> Signup and view all the answers

    What does a Spearman coefficient help assess in RNA-Seq analysis?

    <p>Quality control of replicates</p> Signup and view all the answers

    What does TPM stand for in gene expression analysis?

    <p>Transcript Per Million</p> Signup and view all the answers

    TPM values are considered a true measure of a gene's concentration across different samples.

    <p>False</p> Signup and view all the answers

    What is the primary purpose of using FPKM in RNA expression studies?

    <p>To accommodate paired-end read data and avoid double counting fragments.</p> Signup and view all the answers

    To calculate TPM, divide the number of reads for a transcript by the ______ of the gene.

    <p>length</p> Signup and view all the answers

    Match the following gene types with their respective lengths:

    <p>Gene A = 2000 Gene B = 4000 Gene C = 1000</p> Signup and view all the answers

    Which of the following statements about RNA sequencing is true?

    <p>TPM is proportional to the average concentration of a transcript.</p> Signup and view all the answers

    What adjustment is made when normalizing replicate libraries for TPM calculations?

    <p>Each replicate library is normalized as if it had 1,000,000 reads total.</p> Signup and view all the answers

    RPKM and TPM values should be considered interchangeable in expression studies.

    <p>False</p> Signup and view all the answers

    Study Notes

    RNA-Seq Overview

    • RNA-Seq is a high-throughput method used to sequence and quantify RNA in a sample.
    • It enables a comprehensive analysis of the transcriptome, which is the complete set of RNA transcripts produced by the genome.
    • This is important for quantifying gene expression, discovering novel transcripts, identifying splicing events, and understanding regulatory networks.
    • Applications include cancer, neuroscience, developmental biology, and plant biology research.

    RNA-Seq Workflow

    • RNA extraction from biological samples (e.g., tissue, cells)
    • Library preparation, converting RNA into cDNA
    • Sequencing using Illumina or PacBio technologies
    • Read alignment to a reference genome or transcriptome
    • Quantification of transcript abundance and further downstream analysis (e.g., differential gene expression)

    RNA-Seq Sample Preparation

    • High-quality RNA from biological samples is crucial for RNA-Seq experiments.
    • mRNA typically represents 1-2% of total RNA in samples.
    • Other RNA types include rRNA, tRNA, and non-coding RNAs.
    • RNA is fragile and prone to degradation; handling must be careful.
    • Methods include TRIzol or column-based RNA extraction kits.

    RNA-Seq Library Creation

    • Illumina TruSeq protocol is a common approach:
    • Poly-A selection of mRNA
    • Fragmentation and random priming
    • First and second strand cDNA synthesis
    • End-repair, phosphorylation, and A-tailing
    • Adapter ligation and PCR amplification
    • The library is ready for clustering and sequencing.

    Ribosomal RNA (rRNA) Depletion

    • rRNA constitutes ~80-90% of total RNA in a cell.
    • If mRNA is the focus, depleting rRNA is necessary.
    • Methods include poly-A selection or ribodepletion.
    • Poly-A selection targets polyadenylated RNA, but not all types.
    • Ribodepletion removes rRNA without bias, including both polyadenylated and non-polyadenylated RNA.

    RNA-Seq Experimental Design

    • Well-designed experiments lead to reproducible and meaningful data.
    • Poor design results in biased results, incorrect conclusions, and wasted resources.
    • The key goals are to maximize biological signal detection and minimize technical noise and bias.
    • Replication is important (both biological and technical).
    • Sequencing depth depends on the goals of the experiment (5 million reads for highly expressed genes, >50 million for lowly expressed genes, <1 million for single cell analysis).

    Bulk RNA-Seq Analysis

    • Quality Control: Check the quality of raw sequencing data.
    • Read Mapping: Align reads to a reference genome or transcriptome.
    • Transcript Quantification: Estimate expression levels for genes or transcripts.
    • Differential Expression Analysis: Identify genes expressed differently across conditions.

    RNA-Seq Quality Control

    • Ensures sequencing data is of sufficient quality for downstream analysis.
    • Tools like FASTQC and MultiQC are used.
    • Common issues to look for include low-quality bases, adapter contamination, and overrepresented sequences.
    • Poor-quality data can be trimmed or filtered before mapping.

    Mapping Reads to a Reference

    • Aligning short RNA-Seq reads to a reference genome or transcriptome to identify their origins.
    • Crucial for quantifying gene expression and identifying novel transcripts.
    • Tools include HISAT2 and STAR for their speed, accuracy, and handling of spliced reads.
    • RNA-Seq reads often span exon-exon junctions.
    • Repetitive regions in genomes present challenges to unique read assignment.

    Transcript Quantification Methods

    • Different pipelines for RNA-Seq analysis can influence accuracy.
    • HTSeq is known for efficient counting with union and intersection methods.
    • RSEM is effective in transcript quantification by summing transcript-level estimates.
    • Salmon and Kallisto are pseudoaligners offering speed and precision, but potentially sacrificing some accuracy compared to traditional methods.

    RNA-Seq Normalization

    • Normalization corrects for differences in total read counts among samples.
    • Common methods include RPKM, FPKM, TPM, and TMM.
    • RPKM normalizes for length and sequencing depth by dividing raw counts by kilobases per million mapped reads.
    • FPKM does the same, but for paired-end reads.
    • TPM normalizes within each sample and is more consistent for comparisons among samples.
    • TMM calculates normalization factors by comparing log fold changes between samples, trimming extreme values, and using the mean of the remaining M-values to scale counts.

    Differential Gene Expression Analysis

    • Using quantified expression levels to determine how gene expression changes across samples.
    • Differentially expressed (DE) genes are identified.
    • Methods include DESeq2, edgeR, NOISeq, and limma.

    Data Visualization

    • Various techniques visualize RNA-Seq results (e.g., volcano plots, dot plots, heat maps, line graphs).

    Mock Experiment and Replication

    • Assess data quality and false positives due to the random nature of p-values.
    • Use of statistical measures like Benjamini-Hochberg to control false discovery rate.
    • Independence Filtering removes genes with high occurrences and re-evaluates differentially expressed genes.

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    RNA-Seq Lecture 8 PDF

    Description

    This quiz provides an overview of the RNA-Seq technique and its workflow, from RNA extraction to sequencing and analysis. It covers crucial aspects such as transcriptome analysis and applications in various fields like cancer and neuroscience. Test your knowledge on the steps and importance of RNA-Seq in research!

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