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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Top images from around the web for Reconstructing Transcripts from RNA-seq Reads
Transcript Assembly Merge | Griffith Lab View original
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Reference-based RNAseq data analysis (long) View original
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Chapter 6: Transcriptomics – Applied Bioinformatics View original
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Transcript Assembly Merge | Griffith Lab View original
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Reference-based RNAseq data analysis (long) View original
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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: