What is the Difference Between Short and Long-read RNA-Seq?

RNA-seq stands as the cornerstone for differential gene expression (DGE) analysis, a pivotal method in molecular biology. The conventional workflow commences with RNA extraction in the laboratory, succeeded by mRNA enrichment or removal of ribosomal RNA, cDNA synthesis, and the crafting of adapter-ligated sequencing libraries. These libraries are subsequently subjected to high-throughput sequencing, often employing Illumina platforms, generating reads ranging from 100 to 30 million per sample. The ensuing computational phase entails read alignment or assembly into transcriptomes, quantification of overlapping transcripts, inter-sample normalization and filtering, culminating in statistical modeling to discern significant alterations in gene or transcript expression levels across sample cohorts.

Historically, RNA-seq has embraced diverse platforms, prominently Illumina, renowned for its short read lengths, juxtaposed with long read sequencing platforms such as PacBio and Nanopore, each offering distinct advantages and trade-offs.

CD Genomics high-throughput sequencing and library construction services enable in-depth analysis of transcriptomes. CD Genomics provides short-read and long-read RNA-Seq service, ensuring robust transcriptome research in samples of the highest quality.

Short-read Sequencing vs. Long-read Sequencing

Short-read sequencing and long-read sequencing stand as distinct methodologies in DNA or RNA sequencing, each offering unique features and advantages in decoding genetic information.

Short-read sequencing involves the parsing of DNA or RNA sequences into shorter fragments. Prominent among short-read sequencing technologies is Illumina's platform, renowned for generating sequences spanning tens to hundreds of base pairs.

  • Advantages: Short-read sequencing boasts high accuracy, cost-effectiveness, and scalability, rendering it ideal for tasks such as genome assembly, gene expression profiling, and SNP detection.
  • Disadvantages: However, short-read sequencing may falter in delineating long repetitive sequences or overlapping regions with precision.

On the other hand, long-read sequencing excels in capturing lengthier DNA or RNA fragments, often spanning thousands to hundreds of thousands of base pairs.

  • Advantage: Its ability to decipher longer sequences addresses challenges posed by complex genomic structures and elusive structural variations, furnishing more comprehensive genomic insights.
  • Disadvantages: Long-read sequencing typically entails higher costs, elevated error rates, and increased complexity in data processing and analysis.

The selection between short-read and long-read sequencing hinges upon the specific requirements of the study. Short reads are well-suited for large-scale sequencing endeavors, SNP detection, and gene expression studies, while long reads shine in resolving intricate genomic structures, facilitating genome assembly, and pinpointing structural variations within genes. Frequently, researchers opt for a hybrid approach, leveraging both technologies to glean a more nuanced understanding of genetic landscapes.

Table 1 Short-read vs. long-read RNA-seq

Short-read cDNA-Seq Long-read cDNA-Seq Long-read RNA-Seq
Platforms Illumina, Ion Torrent PacBio Oxford Nanopore
Pros - Very high throughput: 100-1,000 times more reads per run than long-read platforms.
- Bias and error curves are easily understood.
- Offers numerous compatible methods and computational workflows for degraded RNA analysis.
- Captures many full-length transcripts in 1-50 kb long reads.
- Simplifies computational methods for ab initio transcriptome analysis.
- Long read segments spanning 1-50 kb effectively capture complete transcripts.
- Simplified calculations facilitate ab initio transcriptome analysis.
- Sample preparation without the need for reverse transcription or with reduced PCR bias.
- Detection of RNA base modifications is enabled, including the direct estimation of Poly(A) tail lengths through single-molecule sequencing.
Cons - Sample preparation (reverse transcription, PCR, size selection) introduces bias.
- Limited isoform detection and quantification.
- Requires ab initio transcriptome matching and/or assembly steps for transcript discovery.
- Low to medium throughput: 500,000 to 10 million reads per run.
- Sample preparation biases present for guide discovery, ab initio transcriptome analysis, and fusion transcript discovery.
- Not recommended for degraded RNA analysis.
- The technology offers low throughput, currently ranging from 500,000 to 1 million reads per run.
- Understanding of sample preparation and sequencing biases remains incomplete.
- Capable of detecting modifications in ribonucleic acid (RNA).
Key Applications - DGEWTA, small RNA, single-cell analysis, spatial omics, de novo RNA assembly, translatome analysis, structural and RNA-protein interaction analysis, etc. - Isoform discovery, ab initio transcriptome analysis, fusion transcript discovery, MHC, HLA, or other complex transcript analysis. - Isoform discovery, ab initio transcriptome analysis, fusion transcript discovery, MHC, HLA, or other complex transcript analysis.
- Capable of identifying modifications in ribonucleic acid (RNA).
Technological Considerations - High throughput with known biases.
- Computational workflows cater to various RNA analysis applications.
- Medium throughput with sample preparation biases.
- Direct detection of RNA modifications.
Recommendations - Suitable for a wide range of RNA analysis applications.
- Not recommended for degraded RNA analysis.
- Recommended for isoform discovery, fusion transcript analysis, and complex transcript analysis.
- Not suitable for degraded RNA analysis.
Sequencing excels in tasks such as isoform discovery, ab initio transcriptome analysis, fusion transcript detection, and the examination of complex transcripts like MHC and HLA.
However, it is not recommended for analyzing degraded RNA.
For Research Use Only. Not for use in diagnostic procedures.
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