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

Nielsen KL, Høgh AL, Emmersen J - Nucleic Acids Res. (2006)

Bottom Line: Sample preparation and handling are greatly simplified compared to Serial Analysis of Gene Expression (SAGE).We compare DeepSAGE and LongSAGE data and demonstrate greater power of detection and multiplexing of samples derived from potato.The transcript analysis revealed a great abundance of up-regulated potato transcripts associated with stress in dormant potatoes compared to harvest.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University DK-9000 Aalborg, Denmark.

ABSTRACT
Digital transcriptomics with pyrophosphatase based ultra-high throughput DNA sequencing of di-tags provides high sensitivity and cost-effective gene expression profiling. Sample preparation and handling are greatly simplified compared to Serial Analysis of Gene Expression (SAGE). We compare DeepSAGE and LongSAGE data and demonstrate greater power of detection and multiplexing of samples derived from potato. The transcript analysis revealed a great abundance of up-regulated potato transcripts associated with stress in dormant potatoes compared to harvest. Importantly, many transcripts were detected that cannot be matched to known genes, but is likely to be part of the abiotic stress-response in potato.

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Related in: MedlinePlus

Correlation of tag counts extracted from (A) forward and reverse sequences, respectively. Data sets consisted of 167 159 forward sequences and 199 413 reverse sequences. Using tags observed at least once in both directions only (12 025 tags) the R2 = 0.9611. (B) Counts extracted from two different sequencing runs. Data sets consisted of 96 427 tags from the first run and 26 673 tags from the second run. Using tags observed at least once in both runs only (6631 tags) the R2 = 0.9609.
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fig2: Correlation of tag counts extracted from (A) forward and reverse sequences, respectively. Data sets consisted of 167 159 forward sequences and 199 413 reverse sequences. Using tags observed at least once in both directions only (12 025 tags) the R2 = 0.9611. (B) Counts extracted from two different sequencing runs. Data sets consisted of 96 427 tags from the first run and 26 673 tags from the second run. Using tags observed at least once in both runs only (6631 tags) the R2 = 0.9609.

Mentions: To further address sequence accuracy and the impact on tag based transcriptome analysis, the forward sequences from both runs were sorted by their identification key into 91 580 from the HAR sample and 122 100 from the DOR sample. We determined the sequence error rates using SAGEscreen (9) for both the DeepSAGE datasets and for a LongSAGE DOR library of 53 688 tags. Tags observed more than 50 times (87 229 and 141 tags for LongSAGE, DeepSAGE DOR and HAR, respectively). The results are shown in Table 1. Overall estimates of sequence error containing tags in DeepSAGE are in fact lower (9.1–12.4%) than LongSAGE (16.6%). The overall estimates are composed of lower substitution error rate in DeepSAGE compared to LongSAGE (5.2–9.2% versus 15.3% of tags) and a higher insertion (2.2–2.5% versus 0.72%) and deletion rate (1.3–1.7% versus 0.8%) in agreement with what was previously found for ultra-high throughput pyrophosphate sequencing (6). Presumably, the higher sequence accuracy of DeepSAGE is obtained because tag sequences are extracted from nt 33 to approximately 73 (dependent on variation in MmeI cleavage) of DNA sequences, well within the first 90 nt which are determined with the highest accuracy (6). Indeed, correlation analysis of tags extracted from forward and reverse sequences (Figure 2A) indicated good sequence fidelity and reliable tag extraction (R2 = 0.96). Reproducibility was confirmed by performing a second limited run yielding 119 835 tags and comparing the two runs (R2 = 0.96) (Figure 2B).


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

Nielsen KL, Høgh AL, Emmersen J - Nucleic Acids Res. (2006)

Correlation of tag counts extracted from (A) forward and reverse sequences, respectively. Data sets consisted of 167 159 forward sequences and 199 413 reverse sequences. Using tags observed at least once in both directions only (12 025 tags) the R2 = 0.9611. (B) Counts extracted from two different sequencing runs. Data sets consisted of 96 427 tags from the first run and 26 673 tags from the second run. Using tags observed at least once in both runs only (6631 tags) the R2 = 0.9609.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1636492&req=5

fig2: Correlation of tag counts extracted from (A) forward and reverse sequences, respectively. Data sets consisted of 167 159 forward sequences and 199 413 reverse sequences. Using tags observed at least once in both directions only (12 025 tags) the R2 = 0.9611. (B) Counts extracted from two different sequencing runs. Data sets consisted of 96 427 tags from the first run and 26 673 tags from the second run. Using tags observed at least once in both runs only (6631 tags) the R2 = 0.9609.
Mentions: To further address sequence accuracy and the impact on tag based transcriptome analysis, the forward sequences from both runs were sorted by their identification key into 91 580 from the HAR sample and 122 100 from the DOR sample. We determined the sequence error rates using SAGEscreen (9) for both the DeepSAGE datasets and for a LongSAGE DOR library of 53 688 tags. Tags observed more than 50 times (87 229 and 141 tags for LongSAGE, DeepSAGE DOR and HAR, respectively). The results are shown in Table 1. Overall estimates of sequence error containing tags in DeepSAGE are in fact lower (9.1–12.4%) than LongSAGE (16.6%). The overall estimates are composed of lower substitution error rate in DeepSAGE compared to LongSAGE (5.2–9.2% versus 15.3% of tags) and a higher insertion (2.2–2.5% versus 0.72%) and deletion rate (1.3–1.7% versus 0.8%) in agreement with what was previously found for ultra-high throughput pyrophosphate sequencing (6). Presumably, the higher sequence accuracy of DeepSAGE is obtained because tag sequences are extracted from nt 33 to approximately 73 (dependent on variation in MmeI cleavage) of DNA sequences, well within the first 90 nt which are determined with the highest accuracy (6). Indeed, correlation analysis of tags extracted from forward and reverse sequences (Figure 2A) indicated good sequence fidelity and reliable tag extraction (R2 = 0.96). Reproducibility was confirmed by performing a second limited run yielding 119 835 tags and comparing the two runs (R2 = 0.96) (Figure 2B).

Bottom Line: Sample preparation and handling are greatly simplified compared to Serial Analysis of Gene Expression (SAGE).We compare DeepSAGE and LongSAGE data and demonstrate greater power of detection and multiplexing of samples derived from potato.The transcript analysis revealed a great abundance of up-regulated potato transcripts associated with stress in dormant potatoes compared to harvest.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University DK-9000 Aalborg, Denmark.

ABSTRACT
Digital transcriptomics with pyrophosphatase based ultra-high throughput DNA sequencing of di-tags provides high sensitivity and cost-effective gene expression profiling. Sample preparation and handling are greatly simplified compared to Serial Analysis of Gene Expression (SAGE). We compare DeepSAGE and LongSAGE data and demonstrate greater power of detection and multiplexing of samples derived from potato. The transcript analysis revealed a great abundance of up-regulated potato transcripts associated with stress in dormant potatoes compared to harvest. Importantly, many transcripts were detected that cannot be matched to known genes, but is likely to be part of the abiotic stress-response in potato.

Show MeSH
Related in: MedlinePlus