Computational Biology

(BIOSC 1540)

Sep 17, 2024

Lecture 07:
Transcriptomics

Announcements

• A02 will be graded by Sunday
• A03 is due Thursday by 11:59 pm
• The bioinformatics exam is in 16 days
  • If you have DRS-approved accommodations, please request an exam time
• We will have a review session the day before the exam
  • I will post a list of concepts that will be on the exam
  • We will have a poll where you can vote on topics to go over
• Programming+ recitations are Fridays from 2:00 - 3:30 pm in 315 Clapp

After today, you should be able to

1.  Define transcriptomics and explain its role in understanding gene expression patterns.
2.  Discuss emerging trends in transcriptomics.
3.  Compare and contrast transcriptomics and genomics.
4.  Explain the principles of RNA-seq technology and its advantages over previous methods.
5.  Outline the computational pipeline for RNA-seq data analysis.

Transcriptomics: A real-time microscope

Transcriptomics works with the complete set of RNA transcripts

See RNA in action

• Cell type: A neuron will have a different gene expression profile than a liver cell
• Developmental stage: The genes active in an embryo differ from those in an adult
• Environmental conditions: Cells respond to stress, nutrients, or pathogens by changing gene expression

Transcriptomics is versatile

Transcriptomics reveals alternative splicing and isoforms

Example: Dscam in Drosophila

After today, you should be able to

1.  Define transcriptomics and explain its role in understanding gene expression patterns.
2.  Discuss emerging trends in transcriptomics.
3.  Compare and contrast transcriptomics and genomics.
4.  Explain the principles of RNA-seq technology and its advantages over previous methods.
5.  Outline the computational pipeline for RNA-seq data analysis.

Single-cell transcriptomics revolutionizes resolution

• Captures gene expression in individual cells
• Reveals cellular heterogeneity within tissues
• While powerful, data is sparse and noisy

Spatial transcriptomics maps gene expression to location

• Preserves spatial information of transcripts within tissue sections
• Reveals how cellular neighborhoods influence gene expression

TopHat questions

Which of the following scenarios would likely benefit most from using single-cell transcriptomics over bulk RNA-seq?

After today, you should be able to

1.  Define transcriptomics and explain its role in understanding gene expression patterns.
2.  Discuss emerging trends in transcriptomics.
3.  Compare and contrast transcriptomics and genomics.
4.  Explain the principles of RNA-seq technology and its advantages over previous methods.
5.  Outline the computational pipeline for RNA-seq data analysis.

Functional insights

• Captures real-time cellular responses

After today, you should be able to

1.  Define transcriptomics and explain its role in understanding gene expression patterns.
2.  Discuss emerging trends in transcriptomics.
3.  Compare and contrast transcriptomics and genomics.
4.  Explain the principles of RNA-seq technology and its advantages over previous methods.
5.  Outline the computational pipeline for RNA-seq data analysis.

RNA quality is critical for successful sequencing

• rRNA makes up a large (~85%) of our RNA
• Based on the ratio of 28S and 18S rRNA vs. all RNA

mRNA enrichment focuses sequencing on protein-coding transcripts

Reverse transcription introduces unique challenges

RNA is converted to cDNA using reverse transcriptase

• Random or oligo(dT) primers influence transcript representation
• Second-strand synthesis method can preserve strand information

Once upon a time, we had microarrays

Microarrays have some caveats

• Limited to known sequences: Can only detect pre-defined sequences
• Cross-hybridization: Similar sequences may cause false positives
• Limited dynamic range: May miss very low or high abundance transcripts
• Normalization challenges: Complex process, potential for bias

RNA sequencing changed the game

Now we just sequence the cDNA

• RNA-seq doesn't require prior knowledge of sequences
• Enables discovery of novel transcripts and isoforms
• Provides absolute quantification rather than relative concentration

Advantages

TopHat questions

What is the primary advantage of RNA-seq over microarray technology?

After today, you should be able to

1.  Define transcriptomics and explain its role in understanding gene expression patterns.
2.  Discuss emerging trends in transcriptomics.
3.  Compare and contrast transcriptomics and genomics.
4.  Explain the principles of RNA-seq technology and its advantages over previous methods.
5.  Outline the computational pipeline for RNA-seq data analysis.

Read Alignment:
Mapping Transcripts to the Genome

• Consideration of splice junctions and gene isoforms
• Needs to account for known and novel splice sites
• Requires specialized alignment algorithms (e.g., STAR, HISAT2)
• Critical for accurate transcript reconstruction and quantification

Quantification:
Measuring Gene Expression Levels

• Counting aligned reads with HTSeq or featureCounts
• Transcript-level quantification with Salmon or Kallisto
• Normalization methods: For example, TPM (transcripts per million)
• Distinguishing between different isoforms of the same gene
• Requires probabilistic models for read assignment

Differential Expression Analysis:
Identifying Key Genes

• Compares gene expression levels
• Statistical testing with DESeq2 or edgeR
• Visualization of results (volcano plots)
• Clustering of differentially expressed genes
• Results in lists of up- and down-regulated genes

Dimensionality Reduction:
Visualizing Complex Data

• Reduces high-dimensional data to 2D or 3D for visualization
• Reveals patterns and clustering in the data
• Techniques include PCA, t-SNE, and UMAP
• This practice is widely used, but extreme caution needs to be used and is not generally recommended