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Transcriptome assembly and quantification are crucial steps in understanding gene expression. These processes involve reconstructing transcripts from RNA-seq reads and estimating their abundance, providing insights into cellular activity and genetic regulation.

Challenges like resolving isoforms and handling sequencing errors make assembly complex. Quantification methods, including read counting and normalization, help accurately measure gene expression levels. Quality assessment ensures reliable results for downstream analysis and biological interpretation.

Transcriptome Assembly for Gene Expression

Reconstructing Transcripts from RNA-seq Reads

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  • Transcriptome assembly reconstructs the complete set of transcripts expressed in a cell or tissue from short RNA-seq reads
  • RNA-seq reads are first aligned to a reference genome or transcriptome
    • Overlapping reads are then assembled into contigs or transcripts
  • Crucial for identifying novel transcripts, alternative splicing events, and gene fusions that may not be present in the reference genome
  • Accurate transcriptome assembly is essential for:
    • Quantifying gene expression levels
    • Detecting differential expression between conditions

Challenges in Transcriptome Assembly

  • Resolving isoforms
  • Handling low-expressed transcripts
  • Dealing with sequencing errors and biases

Reference-Based vs De Novo Assembly

Reference-Based Assembly

  • Involves aligning RNA-seq reads to a reference genome or transcriptome
    • Assembled aligned reads into transcripts
  • Computationally efficient
  • Can identify known transcripts
  • May miss novel or sample-specific transcripts

De Novo Assembly

  • Reconstructs transcripts directly from RNA-seq reads without the use of a reference genome
  • Can discover novel transcripts
  • Useful for non-model organisms or samples with significant genetic variations (highly mutated cancer samples)
  • Computationally intensive
  • May produce fragmented or misassembled transcripts due to:
    • Sequencing errors
    • Repeats
  • Hybrid approaches combine reference-based and de novo methods to leverage the advantages of both strategies

Quantifying Gene Expression from RNA-seq

Read Counting and Normalization Methods

  • Gene expression quantification estimates the abundance of each transcript or gene in the sample based on the number of mapped reads
  • Read counting methods assign reads to genes or transcripts based on their genomic coordinates
    • HTSeq
    • featureCounts
  • Normalized read counts account for differences in library size and gene length
    • RPKM (Reads Per Kilobase Million)
    • TPM (Transcripts Per Million)
  • Normalization methods use statistical models to correct for technical biases and variability in read counts across samples
    • DESeq2
    • edgeR

Isoform-Level Quantification and Batch Effects

  • Isoform-level quantification tools estimate the abundance of alternative splicing isoforms
    • Cufflinks
    • StringTie
  • Batch effects and other confounding factors should be identified and corrected for accurate gene expression quantification

Evaluating Transcriptome Assembly Quality

Quality Assessment Metrics

  • Quality assessment of transcriptome assemblies involves evaluating metrics such as:
    • Contiguity
    • Completeness
    • Accuracy
  • N50 and L50 values indicate the contiguity and length distribution of the assembled transcripts
  • Completeness can be assessed by:
    • Aligning the assembled transcripts to a reference transcriptome
    • Searching for conserved orthologous genes
  • Accuracy can be evaluated by:
    • Comparing the assembled transcripts to known gene models
    • Examining the alignment of reads back to the assembly

Quality Control and Biological Interpretation

  • Quality control of gene expression quantification includes:
    • Examining the distribution of read counts
    • Identifying outlier samples
    • Assessing the reproducibility of replicates
  • Differentially expressed genes should be validated using independent methods
    • qRT-PCR
    • Functional assays
  • Biological interpretation of gene expression results requires integration with:
    • Functional annotation
    • Pathway analysis
    • Other omics data
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AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.


© 2025 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

© 2025 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
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