Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing
Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Informa Healthcare
2008
|
| Online Access: | http://hdl.handle.net/20.500.11937/22968 |
| _version_ | 1848751021158825984 |
|---|---|
| author | Morin, R. Bainbridge, M. Fejes, A. Hirst, M. Krzywinski, M. Pugh, T. McDonald, H. Varhol, Richard Jones, S. Marra, M. |
| author_facet | Morin, R. Bainbridge, M. Fejes, A. Hirst, M. Krzywinski, M. Pugh, T. McDonald, H. Varhol, Richard Jones, S. Marra, M. |
| author_sort | Morin, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically do not robustly survey the majority of transcripts along their entire length. Here we show that massively parallel sequencing of randomly primed cDNAs, using a next-generation sequencing-by-synthesis technology, offers the potential to generate relative measures of mRNA and individual exon abundance while simultaneously profiling the prevalence of both annotated and novel exons and exon-splicing events. This technique identifies known single nucleotide polymorphisms (SNPs) as well as novel single-base variants. Analysis of these variants, and previously unannotated splicing events in the HeLa S3 cell line, reveals an overrepresentation of gene categories including those previously implicated in cancer. |
| first_indexed | 2025-11-14T07:46:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-22968 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:46:06Z |
| publishDate | 2008 |
| publisher | Informa Healthcare |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-229682017-09-13T13:56:21Z Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing Morin, R. Bainbridge, M. Fejes, A. Hirst, M. Krzywinski, M. Pugh, T. McDonald, H. Varhol, Richard Jones, S. Marra, M. Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically do not robustly survey the majority of transcripts along their entire length. Here we show that massively parallel sequencing of randomly primed cDNAs, using a next-generation sequencing-by-synthesis technology, offers the potential to generate relative measures of mRNA and individual exon abundance while simultaneously profiling the prevalence of both annotated and novel exons and exon-splicing events. This technique identifies known single nucleotide polymorphisms (SNPs) as well as novel single-base variants. Analysis of these variants, and previously unannotated splicing events in the HeLa S3 cell line, reveals an overrepresentation of gene categories including those previously implicated in cancer. 2008 Journal Article http://hdl.handle.net/20.500.11937/22968 10.2144/000112900 Informa Healthcare unknown |
| spellingShingle | Morin, R. Bainbridge, M. Fejes, A. Hirst, M. Krzywinski, M. Pugh, T. McDonald, H. Varhol, Richard Jones, S. Marra, M. Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title | Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title_full | Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title_fullStr | Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title_full_unstemmed | Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title_short | Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing |
| title_sort | profiling the hela s3 transcriptome using randomly primed cdna and massively parallel short-read sequencing |
| url | http://hdl.handle.net/20.500.11937/22968 |