DeepSAGE—digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples

Nielsen, Kåre Lehmann; Høgh, Annabeth Laursen; Emmersen, Jeppe

doi:10.1093/nar/gkl714

Cited by 109 publications

(75 citation statements)

References 22 publications

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“…Furthermore these approaches are more efficient at detecting very rare transcripts and variation in highly expressed genes (because of higher resolution) and the analyses therefore have a greater dynamic range. Another advantage of digital transcriptomics over microarrays is the ability to detect expression levels in previously unknown genes (Nielsen et al, 2006). In a recent study, there was a very high correlation between the number of 454 reads mapping to a gene and microarray determined gene expression of it (Kristiansson et al, 2009), thus validating the RNA-Seq methodology for non-model organisms.…”

Section: Gene Expression Profilingmentioning

confidence: 99%

“…Next generation sequencing in non-model organisms R Ekblom and J Galindo example deep serial analysis of gene expression (see Glossary) (Nielsen et al, 2006) always outputs the same sequence tag for a given transcript, facilitating data analysis. The utility of this approach for surveying gene expression in a non-model organism was recently verified in a study of drought stress responses of chick pea (Cicer arietinum) roots (Molina et al, 2008).…”

Section: Gene Expression Profilingmentioning

confidence: 99%

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Applications of next generation sequencing in molecular ecology of non-model organisms

2010

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As most biologists are probably aware, technological advances in molecular biology during the last few years have opened up possibilities to rapidly generate large-scale sequencing data from non-model organisms at a reasonable cost. In an era when virtually any study organism can 'go genomic', it is worthwhile to review how this may impact molecular ecology. The first studies to put the next generation sequencing (NGS) to the test in ecologically well-characterized species without previous genome information were published in 2007 and the beginning of 2008. Since then several studies have followed in their footsteps, and a large number are undoubtedly under way. This review focuses on how NGS has been, and can be, applied to ecological, population genetic and conservation genetic studies of non-model species, in which there is no (or very limited) genomic resources. Our aim is to draw attention to the various possibilities that are opening up using the new technologies, but we also highlight some of the pitfalls and drawbacks with these methods. We will try to provide a snapshot of the current state of the art for this rapidly advancing and expanding field of research and give some likely directions for future developments.

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Section: Gene Expression Profilingmentioning

confidence: 99%

Section: Gene Expression Profilingmentioning

confidence: 99%

Applications of next generation sequencing in molecular ecology of non-model organisms

2010

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“…To compare the differences in transcriptomic responses to excess uracil between wild type and the pyrG89 mutant, we examined genome-wide transcriptional responses to excess uracil by digital gene expression (DGE) profiling [21]. Briefly, the spores of A. nidulans wild type or the pyrG89 mutant were inoculated into 150-ml flasks containing 75 mL liquid medium (MMV+1×uridine+4×uracil and MMV+1× uridine+1×uracil) and incubated at 28°C with shaking at 180 r min 1 for 30 h. Then RNA was extracted from three replicate samples for the DGE profiling assay.…”

Section: Analysis Of Transcriptomic Responses To Excess Uracil By Digmentioning

confidence: 99%

pyrG is required for maintaining stable cellular uracil level and normal sporulation pattern under excess uracil stress in Aspergillus nidulans

Sun

Zhu

et al. 2013

Sci. China Life Sci.

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Tight control of the intracellular uracil level is believed to be important to reduce the occurrence of uracil incorporation into DNA. The pyrG gene of Aspergillus nidulans encodes orotidine 5′-phosphate decarboxylase, which catalyzes the conversion of orotidine monophosphate (OMP) to uridine monophosphate (UMP). In this study, we found that pyrG is critical for maintaining uracil at a low concentration in A. nidulans cells in the presence of exogenous uracil. Excess uracil and its derivatives had a stronger inhibitory effect on the growth of the pyrG89 mutant with defective OMP decarboxylase activity than on the growth of wild type, and induced sexual development in the pyrG89 mutant but not in wild type. Analysis of transcriptomic responses to excess uracil by digital gene expression profiling (DGE) revealed that genes related to sexual development were transcriptionally activated in the pyrG89 mutant but not in wild type. Quantitative analysis by HPLC showed that the cellular uracil level was 6.5 times higher in the pyrG89 mutant than in wild type in the presence of exogenous uracil. This study not only provides new information on uracil recycling and adaptation to excess uracil but also reveals the potential effects of OMP decarboxylase on fungal growth and development. orotidine 5′-phosphate decarboxylase, stress, uracil, sexual development Citation:Sun X Y, Zhu J F, Bao L, et al. pyrG is required for maintaining stable cellular uracil level and normal sporulation pattern under excess uracil stress in

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“…Such technologies offer up to two orders of magnitude increase in per base cost efficiency compared to capillary sequencing (von Bubnoff 2008). These platforms have made feasible previously cost-prohibitive projects such as genome resequencing (Green et al 2006;Bentley et al 2008;Ley et al 2008;Wang et al 2008b) and deep transcriptome and noncoding RNA sequencing (Nielsen et al 2006;Weber et al 2007;Marioni et al 2008;Morin et al 2008;Rosenkranz et al 2008), as well as genome-wide protein bindingsite surveys (ChIP-seq) (Jothi et al 2008;Wederell et al 2008). …”

mentioning

confidence: 99%

Next-generation tag sequencing for cancer gene expression profiling

Morrissy¹,

Morin²,

Delaney³

et al. 2009

Genome Res.

295

259

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We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tagseq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.

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DeepSAGE—digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples

Cited by 109 publications

References 22 publications

Applications of next generation sequencing in molecular ecology of non-model organisms

Applications of next generation sequencing in molecular ecology of non-model organisms

pyrG is required for maintaining stable cellular uracil level and normal sporulation pattern under excess uracil stress in Aspergillus nidulans

Next-generation tag sequencing for cancer gene expression profiling

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