Lecture 8 RNA-Seq
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What is the primary purpose of RNA-Seq?

  • To perform genome assembly
  • To sequence DNA
  • To sequence and quantify RNA (correct)
  • To analyze protein structures

    • RNA-Seq allows for the discovery of novel transcripts without needing pre-designed probes.

    True

    Name one application of RNA-Seq in research.

    Cancer research

    RNA-Seq provides a more __________ view of the transcriptome compared to microarrays.

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

    Match the following benefits of RNA-Seq with their descriptions:

    <p>Higher sensitivity = Can detect low-abundance transcripts Greater dynamic range = More accurate quantification of genes Transcript discovery = Identifies novel coding and non-coding RNAs Alternative splicing = Discovery of isoforms and splice variants</p> Signup and view all the answers

    Which of the following is NOT an application of RNA-Seq?

    <p>Protein synthesis analysis</p> Signup and view all the answers

    RNA-Seq cannot measure transcript levels accurately.

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

    What does RNA-Seq reveal about gene interactions?

    <p>Co-expression networks</p> Signup and view all the answers

    Which type of RNA is primarily focused on in RNA-Seq experiments?

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

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

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

    What is the purpose of ribosomal RNA depletion in RNA-Seq?

    <p>To enrich for mRNA and allow for the capture of both polyadenylated and non-polyadenylated transcripts.</p> Signup and view all the answers

    The ______ method allows for the removal of rRNA without bias.

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

    Match the methods of RNA extraction with their characteristics:

    <p>TRIzol = A reagent for RNA extraction Column-based kits = Easy-to-use, often less toxic Poly-A selection = Enriches for mRNA only Ribodepletion = Removes rRNA without bias</p> Signup and view all the answers

    What is a key goal of experimental design in RNA-Seq?

    <p>Ensure reproducibility</p> Signup and view all the answers

    Biological replicates are essential to ensure random variation does not affect results.

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

    What main factors can affect RNA composition during RNA extraction?

    <p>Tissue types and conditions such as stress or disease.</p> Signup and view all the answers

    What is the main advantage of having more biological replicates in an experimental design?

    <p>It increases the accuracy of detecting differentially expressed genes.</p> Signup and view all the answers

    Higher sequencing depth decreases sensitivity to detect low-expressed genes.

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

    What is sequencing depth?

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

    RNA-Seq normalization corrects variations in total read counts to compare gene expression across different ______.

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

    Which of the following quantification methods is effective in counting reads with strong performance?

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

    Match the following RNA-Seq normalization methods with their descriptions:

    <p>RPKM = Normalizes for gene length and sequencing depth. FPKM = Similar to RPKM, but for paired-end reads. TPM = Normalizes within each sample for consistency among samples.</p> Signup and view all the answers

    How many reads are recommended to quantify highly expressed genes?

    <p>5 million</p> Signup and view all the answers

    80% of reads should map to the genome or transcriptome for reliable results.

    <p>True</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 normalization does not involve trimming extreme M-values.

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

    What is the primary purpose of RPKM in transcriptomic studies?

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

    The calculation of RPKM normalizes for both the size of the library and the length of the ________.

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

    Match the following normalization methods with their descriptions:

    <p>TMM = Trims extreme M-values and uses means for scaling counts RPKM = Normalizes read counts for gene length and library size Library size normalization = Adjusts counts based on total reads sampled Gene length normalization = Accounts for varying lengths of expressed genes</p> Signup and view all the answers

    What does TPM represent in RNA sequencing?

    <p>Transcripts per million</p> Signup and view all the answers

    The TPM values are considered a true measure of the concentration of an expressed gene.

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

    What is one major advantage of using TPM over RPKM in expression studies?

    <p>TPM is preferred for its better consistency across samples.</p> Signup and view all the answers

    TPM normalizes counts so that each replicate library has a total of __________ reads.

    <p>1,000,000</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>FPKM = Counts fragments from paired-end sequencing. TPM = Represents transcripts per million. RPKM = Normalizes reads to gene length. Paired-end reads = Sequencing method yielding two reads per DNA fragment.</p> Signup and view all the answers

    What is the first step in calculating TPM for a given transcript?

    <p>Divide the number of reads by the gene length.</p> Signup and view all the answers

    TPM values can be calculated without considering gene length.

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

    Which two metrics are commonly used to measure gene expression levels in RNA-Seq?

    <p>FPKM and TPM</p> Signup and view all the answers

    Which method is used by EdgeR for group normalization?

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

    Normalization factors are applied after intra-sample normalization in EdgeR.

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

    What does RPKM stand for in RNA-Seq analysis?

    <p>Reads Per Kilobase of transcript per Million mapped reads</p> Signup and view all the answers

    In RNA-Seq, genes expressed in a leaf tissue may differ significantly from those expressed in ______ tissue.

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

    Match the genes with their expression levels in leaf and root tissues:

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

    Which of the following can indicate a problem in replicate comparisons?

    <p>Replicates being less alike than different treatments</p> Signup and view all the answers

    TPM accounts for differences in gene lengths and library sizes.

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

    What are the two main normalization methods mentioned in the content?

    <p>RPKM and TPM</p> Signup and view all the answers

    Study Notes

    RNA-Seq Overview

    • RNA-Seq (RNA sequencing) is a high-throughput method for sequencing and quantifying RNA in a sample.
    • It provides a comprehensive analysis of the transcriptome, which is the complete set of RNA transcripts produced by the genome.
    • RNA-Seq is useful for quantifying gene expression, identifying splicing events, discovering novel transcripts, and understanding gene regulatory networks.
    • It is used in cancer research, neuroscience, developmental biology, and plant biology.

    RNA-Seq Workflow

    • RNA extraction from biological samples (e.g., tissue, cells).
    • Library preparation: converting RNA to cDNA.
    • Sequencing using Illumina or PacBio technologies.
    • Aligning reads to a reference genome or transcriptome.
    • Quantifying transcript abundance and further downstream analysis.

    RNA-Seq vs. Microarrays

    • RNA-Seq provides a more comprehensive and unbiased view of the transcriptome compared to microarrays.
    • RNA-Seq has higher sensitivity and can detect low-abundance transcripts.
    • It doesn't rely on pre-designed probes; it can discover novel transcripts.
    • RNA-Seq has a greater dynamic range allowing for more accurate quantification of highly and lowly expressed genes.

    RNA-Seq Sample Preparation

    • The crucial starting point is obtaining high-quality RNA from biological samples.
    • Common RNA types include mRNA (a main focus), rRNA, tRNA, and non-coding RNAs.
    • Sample preparation is challenging due to RNA's fragility and propensity for degradation.
    • Methods like TRIzol and column-based kits are used for RNA extraction, and sample source and conditions (stress or disease) impact RNA composition.

    Library Creation (Illumina TruSeq protocol)

    • RNA sequencing library creation typically begins with poly-A selection using magnetic beads.
    • Fragmentation and random priming is followed by first and second-strand cDNA synthesis.
    • End-repair, phosphorylation, and A-tailing.
    • Adapter ligation, PCR amplification, and sequencing.

    Ribosomal RNA (rRNA) Depletion

    • rRNA often constitutes 80-90% of total RNA in a cell.
    • Depleting rRNA from samples allows researchers to focus on mRNA and less abundant transcripts.
    • Methods for rRNA depletion include Poly-A selection and Ribodepletion.

    RNA-Seq Experimental Design

    • Careful experimental design is essential for generating meaningful, reproducible data.
    • Poor design leads to biased results, incorrect biological conclusions, and wasted resources.
    • Defining specific research questions is crucial.
    • Key goals include maximizing biological signal detection and minimizing technical noise and bias.
    • Biological replicates are essential to ensure reliable results, not only technical replicates.

    RNA-Seq Data Analysis

    • Quality Control: Evaluating raw sequences to identify issues like low-quality bases, adapter contamination, and overrepresented sequences. Tools such as FASTQC and MultiQC are used.
    • Read Mapping: Aligning short reads to a genome or transcriptome reference to determine where each read originates. Tools include HISAT2 and STAR. Identifying and handling spliced reads is key. Repetitive regions pose a challenge.
    • Transcript Quantification: Estimating expression levels of genes or transcripts after read mapping using methods such as HTSeq, RSEM, Salmon, and Kallisto.
    • Normalization: Adjusting for differences in library size and composition to ensure fair comparisons across samples. Common methods include RPKM, FPKM, TPM, and TMM.
    • Differential Gene Expression Analysis: Identifying genes exhibiting significant expression changes across samples. Programs like DESeq2, edgeR, NOISeq, and limma are used. Methods for visualizing results: Volcano plots, dot plots, heatmaps.

    RNA-Seq Data Visualization

    • Visual representations like Volcano plots, dot plots, and heatmaps effectively present RNA-Seq data.

    Data Interpretation

    • Analyzing the findings, conducting further research, and ultimately drawing conclusions and interpretations.

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    Related Documents

    RNA-Seq Lecture 8 PDF

    Description

    This quiz explores the essential concepts of RNA-Seq, a vital technique in molecular biology. It covers the workflow from RNA extraction to transcript quantification, and compares RNA-Seq with microarrays. Understand the significance of this method in various research fields such as cancer and developmental biology.

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