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Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA
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Illuminating the dark side of the human transcriptome with long read transcript sequencing.

Richard I Kuo1, Yuanyuan Cheng2,3, Runxuan Zhang4

  • 1The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK. richard.kuo@roslin.ed.ac.uk.

BMC Genomics
|October 31, 2020
PubMed
Summary

New long read sequencing technology and the TAMA software reveal thousands of previously undiscovered genes in the human genome, including rare non-coding and mono-exonic types. This advances transcriptome annotation beyond current limitations.

Keywords:
AnnotationBioinformaticsGene modelsHumanIso-SeqLong read RNA sequencingNanoporePacbioTAMATranscriptome

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Human transcriptome annotation is extensive but biased towards multi-exonic protein-coding genes due to sequencing technology limitations.
  • Rare transcripts, such as mono-exonic and non-coding genes, are often undetectable or indistinguishable from noise.
  • High-throughput long read transcript sequencing offers potential for improved detection of these rare gene types.

Purpose of the Study:

  • To develop and validate a novel software tool, Transcriptome Annotation by Modular Algorithms (TAMA), for processing long read transcript sequencing data.
  • To address limitations in current data processing pipelines for accurate gene and transcript model prediction.
  • To leverage long read sequencing to identify novel genes, particularly rare and non-coding types, within the human genome.

Main Methods:

  • Development of the Transcriptome Annotation by Modular Algorithms (TAMA) software.
  • Benchmarking TAMA using simulated and real Pacific Biosciences (PacBio) and Nanopore sequencing data.
  • Analysis of PacBio Sequel II Iso-Seq sequencing data from Universal Human Reference RNA (UHRR).
  • Evaluation of error correction methods and their impact on gene model prediction accuracy.

Main Results:

  • TAMA demonstrated high sensitivity and precision in gene and transcript model predictions using both reference-guided and unguided approaches.
  • Alignment identity is an unreliable metric for assessing error correction performance in transcript models.
  • Inter-read error correction can lead to significant mapping changes and potentially over 6,000 erroneous gene models.
  • TAMA identified 2,566 putative novel non-coding genes and 1,557 putative novel protein-coding gene models.

Conclusions:

  • Long read transcript sequencing data enables the discovery of novel genes within the human genome.
  • The TAMA software facilitates in-depth exploration of eukaryotic transcriptomes through parameter tuning and comprehensive output.
  • Thousands of unannotated genes have been identified using long read data, highlighting the potential of this technology.
  • Further advancements in library preparation and data processing are needed to reliably distinguish sequencing noise from genuine novel genes.