Targeted RNA sequencing reveals the deep complexity of the human transcriptome

Tim R Mercer; Daniel J Gerhardt; Marcel E Dinger; Joanna Crawford; Cole Trapnell; Jeffrey A Jeddeloh; John S Mattick; John L Rinn

doi:10.1038/nbt.2024

Targeted RNA sequencing reveals the deep complexity of the human transcriptome

Nature Biotechnology ◽

10.1038/nbt.2024 ◽

2011 ◽

Vol 30 (1) ◽

pp. 99-104 ◽

Cited By ~ 294

Author(s):

Tim R Mercer ◽

Daniel J Gerhardt ◽

Marcel E Dinger ◽

Joanna Crawford ◽

Cole Trapnell ◽

...

Keyword(s):

Rna Sequencing ◽

Human Transcriptome

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Atlas of Subcellular RNA Localization Revealed by APEX-seq

10.1101/454470 ◽

2018 ◽

Cited By ~ 8

Author(s):

Furqan M. Fazal ◽

Shuo Han ◽

Pornchai Kaewsapsak ◽

Kevin R. Parker ◽

Jin Xu ◽

...

Keyword(s):

Rna Sequencing ◽

Messenger Rna ◽

Rna Localization ◽

Spatial Proximity ◽

Nuclear Pore ◽

Transcript Isoforms ◽

Human Transcriptome ◽

Inner Surface ◽

Peroxidase Enzyme ◽

Radial Organization

SUMMARYWe introduce APEX-seq, a method for RNA sequencing based on spatial proximity to the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome, revealing extensive and exquisite patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.

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APEX-seq: RNA subcellular localization by proximity labeling

10.21203/rs.2.1857/v1 ◽

2019 ◽

Author(s):

Furqan M. Fazal ◽

Shuo Han ◽

Kevin R. Parker ◽

Pornchai Kaewsapsak ◽

Jin Xu ◽

...

Keyword(s):

Subcellular Localization ◽

Rna Sequencing ◽

Nanometer Resolution ◽

Transcript Isoforms ◽

Human Transcriptome ◽

Peroxidase Enzyme

Abstract We introduce APEX‑seq, a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2. APEX‑seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome as a resource, revealing extensive and exquisite patterns of localization for diverse RNA classes and transcript isoforms. APEX‑seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.

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Illuminating the dark side of the human transcriptome with long read transcript sequencing

10.21203/rs.3.rs-23156/v1 ◽

2020 ◽

Author(s):

Richard Kuo ◽

Yuanyuan Cheng ◽

Runxuan Zhang ◽

John W.S. Brown ◽

Jacqueline Smith ◽

...

Keyword(s):

Error Correction ◽

Rna Sequencing ◽

Dark Side ◽

Sequencing Data ◽

Human Transcriptome ◽

Sequencing Technologies ◽

Long Read ◽

Read Error Correction ◽

Gene Models ◽

Genome Annotations

Abstract Background The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide stronger evidence for genes that were previously either undetectable or impossible to differentiate from sequencing noise such as rare transcripts, mono-exonic, and non-coding genes.Results We analyzed Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using the Transcriptome Annotation by Modular Algorithms (TAMA) software. We found that the convention of using mapping identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction leads to the thousands of erroneous gene models. Using genome assembly based error correction and gene feature evidence, we identified thousands of potentially functional novel genes.Conclusions The standard of using inter-read error correction for long read RNA sequencing data could be responsible for genome annotations with thousands of biologically inaccurate gene models. More than half of all real genes in the human genome may still be missing in current public annotations. We require better methods for differentiating sequencing noise from real genes in long read RNA sequencing data.

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