Lecture 8 RNA-Seq

True (A)

Cancer research

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

<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

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

<p>False (B)</p> Signup and view all the answers

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

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

<p>False (B)</p> Signup and view all the answers

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

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

<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

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

<p>True (A)</p> Signup and view all the answers

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

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

<p>False (B)</p> Signup and view all the answers

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

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

<p>HTSeq (A)</p> Signup and view all the answers

<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

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

<p>True (A)</p> Signup and view all the answers

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

<p>False (B)</p> Signup and view all the answers

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

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

<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

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

<p>False (B)</p> Signup and view all the answers

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

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

<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

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

<p>False (B)</p> Signup and view all the answers

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

<p>TMM (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

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

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

<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

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

<p>True (A)</p> Signup and view all the answers

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

RNA sequencing; a high-throughput method to sequence and quantify RNA in a sample, enabling analysis of the transcriptome.

The complete set of RNA transcripts produced by a genome.

RNA-Seq is more sensitive, flexible, and has greater dynamic range than microarrays, enabling the discovery of novel transcripts and more accurate quantification.

Measuring gene expression levels under different conditions or treatments using RNA-Seq.

Finding new types of RNA molecules using RNA-Seq.

RNA-Seq helps discover different versions of a gene's product due to varying splicing patterns.

Widely used in various biological fields like cancer research, neuroscience, developmental biology, plant biology, and other research areas.

A process involving steps like RNA extraction and sequencing. A detailed step-by-step procedure is needed.

Obtaining RNA from biological samples like tissue or cells for RNA Sequencing

Converting RNA to cDNA for sequencing; essential for RNA-Seq analysis

Removing rRNA from RNA samples, leaving mostly mRNA for analysis

Selecting mRNA by binding to polyadenylated tails

Removing rRNA, unbiased method including non-polyadenylated RNAs

Independent samples from each condition tested for RNA-Seq

Repeats of library preparation, sequencing, etc. to assess variability

Planning RNA-Seq experiments to maximize signal and minimize bias

TMM (Trimmed Mean of M-values) is a normalization method for RNA-Seq data that accounts for differences in library size and gene length. It calculates normalization factors by comparing the M-values (log fold changes) between samples, trimming extreme values, and using the mean of the remaining M-values to scale counts.

RPKM (Reads Per Kilobase of transcript per Million reads mapped) is a normalization method for RNA-Seq data that takes into account both the length of the transcript and the total number of reads in a library. It allows for comparison of transcripts within and between samples, correcting for differences in transcript length and library size.

RPKM accounts for library size by dividing the read counts in each replicate by the total number of mapped reads in that replicate, then multiplying by 10^9. This ensures that the counts are normalized to a consistent library size, allowing for comparison across different samples.

RPKM accounts for gene length by dividing the normalized read counts by the length of the gene in kilobases (kb). This ensures that the counts are normalized to a length of 1kb, allowing for comparison across different genes.

Normalization is crucial for RNA-Seq data analysis because it ensures that the data is comparable across different samples, libraries, and genes. By accounting for factors like library size and gene length, normalization allows for accurate quantification and interpretation of gene expression differences.

A measure of gene expression that takes into account the length of the gene and the total number of reads in a sample. It is calculated by dividing the number of reads for a transcript by the length of the gene in kilobases and then multiplying by 1 million.

Sequences obtained from both ends of a DNA fragment, providing information about the orientation and length of the fragment.

A normalized measure of gene expression that takes into account the length of the gene and the total number of reads in a sample. It represents the number of transcripts for a given gene if there were 1 million transcripts sequenced.

TPM provides a true measure of ‘concentration’ of an expressed gene, taking into account differences in library sizes and gene lengths.

1. Divide the number of reads for a transcript by the gene length. 2. Normalize as if each replicate library had 1,000,000 reads total.

TPM provides a more accurate measure of gene expression by accounting for library size and gene length, enabling consistent comparisons across samples.

A high-throughput method to sequence and quantify RNA in a sample, enabling analysis of the transcriptome.

The choice between increasing biological replicates and sequencing depth in RNA-Seq. More replicates provide better statistical power and reduced technical variability, while higher sequencing depth allows for detection of low-expressed transcripts.

The total number of reads generated per sample in RNA-Seq. It indicates the depth of sequencing coverage, influencing the ability to detect low-abundance transcripts.

The process of aligning RNA-Seq reads to a reference genome or transcriptome. This step is crucial for identifying transcripts and quantifying their expression levels.

High mapping accuracy ensures that reads are aligned to the correct location on the genome or transcriptome, minimizing errors in transcript quantification and downstream analyses.

The even distribution of RNA-Seq reads across the transcriptome. It's important because uneven coverage can bias downstream analyses and lead to inaccurate gene expression estimates.

Different pipelines used to quantify gene or transcript expression levels after RNA-Seq reads are mapped. Popular methods include HTSeq, RSEM, Salmon, and Kallisto, each with its strengths and weaknesses.

Adjusting for differences in total read counts across RNA-Seq libraries to allow for accurate comparison of gene expression between samples.

Adjusting read counts in RNA-Seq to account for differences in library size, ensuring fair comparison between samples.

Methods for normalizing RNA-Seq data based on read counts, gene length, and library size, giving relative expression levels.

A normalization method that accounts for library size and gene length, giving 'transcripts per million' to directly compare gene expression.

A normalization method used by EdgeR, working on the assumption that most genes are not differentially expressed. It determines a normalization factor for each gene and applies a scaling factor to create an ‘effective library size’ for the whole library.

Analyzing replicates to assess data quality and consistency by checking how similar replicates are to each other compared to different treatments.

A statistical measure used to assess the strength and direction of the relationship between two variables, often used for replicate comparisons.

Variation in the genes expressed in different tissues or conditions, leading to differences in RNA-Seq read counts for specific genes.

Identifying genes whose expression levels differ significantly between groups of samples, often used to find genes involved in specific conditions or treatments.