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Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation

Nature Methods volume 2, pages 105–111 (2005)Cite this article

Abstract

We have developed a DNA tag sequencing and mapping strategy called gene identification signature (GIS) analysis, in which 5′ and 3′ signatures of full-length cDNAs are accurately extracted into paired-end ditags (PETs) that are concatenated for efficient sequencing and mapped to genome sequences to demarcate the transcription boundaries of every gene. GIS analysis is potentially 30-fold more efficient than standard cDNA sequencing approaches for transcriptome characterization. We demonstrated this approach with 116,252 PET sequences derived from mouse embryonic stem cells. Initial analysis of this dataset identified hundreds of previously uncharacterized transcripts, including alternative transcripts of known genes. We also uncovered several intergenically spliced and unusual fusion transcripts, one of which was confirmed as a trans-splicing event and was differentially expressed. The concept of paired-end ditagging described here for transcriptome analysis can also be applied to whole-genome analysis of cis-regulatory and other DNA elements and represents an important technological advance for genome annotation.

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Figure 1: Schematic view of the GIS analysis method.
Figure 2: Examples of previously uncharacterized transcripts identified by GIS analysis and verified by PCR and sequencing.
Figure 3: Schematic view of the Ppp2r4-Set fusion transcript identified by GIS analysis in E14 cells and RT-PCR verification of its existence in various tissues.

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Acknowledgements

The authors acknowledge Q. Yu of the National University of Singapore and P. Kondu, W.Y. Au Yong, H.C. Yong, M. Hirwan and M. Rebhan of the Bioinformatics Institute of Singapore for information technology and bioinformatics support, as well as P. Li for providing the E14 cells. This work was funded by the Agency for Science, Technology and Research (A*STAR), Singapore.

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Author notes

  1. Patrick Ng and Chia-Lin Wei: These authors contributed equally to this work.

Authors and Affiliations

  1. Genome Institute of Singapore, 60 Biopolis Street, Genome #02-01, Singapore, 138672

    Patrick Ng, Chia-Lin Wei, Wing-Kin Sung, Kuo Ping Chiu, Leonard Lipovich, Chin Chin Ang, Sanjay Gupta, Edison T Liu & Yijun Ruan

  2. Bioinformatics Institute, 30 Biopolis Street, Matrix #08-01, Singapore, 138671

    Atif Shahab, Azmi Ridwan & Chee Hong Wong

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  1. Patrick Ng
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Corresponding authors

Correspondence to Edison T Liu or Yijun Ruan.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

An example of GIS PET structure and the strategy of mapping PET to genome (PDF 869 kb)

Supplementary Fig. 2

Mapping specificity and quality of PETs to known transcripts (PDF 290 kb)

Supplementary Fig. 3

Quantitative comparison of GIS PETs and EST enumeration (PDF 513 kb)

Supplementary Fig. 4

Flow diagram summarizing the PET mapping and validation process (PDF 474 kb)

Supplementary Fig. 5

Transcripts from different categories identified by PETs and verified by PCR and DNA sequencing (PDF 4512 kb)

Supplementary Fig. 6

The DNA sequence, ORF, and the translated peptide sequence of the Ppp2r4-Set trans-spliced fusion transcript (PDF 1534 kb)

Supplementary Fig. 7

Exon arrangement of two putative fusion transcripts identified by GIS analysis (PDF 391 kb)

Supplementary Table 1

PET mapping statistics. (PDF 45 kb)

Supplementary Table 2

Mapping characteristics of the top 10 most abundant PET clusters and their corresponding genes. (PDF 58 kb)

Supplementary Table 3

Putative intergenically-spliced transcripts identified by GIS ditag analysis. (PDF 59 kb)

Supplementary Table 4

Oligonucleotide and adapter sequences used in the GIS analysis method. (PDF 78 kb)

Supplementary Data

Further GIS analysis (PDF 134 kb)

Supplementary Protocol

GIS Analysis (PDF 449 kb)

Supplementary Methods (PDF 138 kb)

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Ng, P., Wei, CL., Sung, WK. et al. Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation. Nat Methods 2, 105–111 (2005). https://doi.org/10.1038/nmeth733

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