Limits...
A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome.

Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, Scoté-Blachon C, Peyron C, Gandrillon O - BMC Genomics (2008)

Bottom Line: Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript.We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis.In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR5534 CNRS Université Claude Bernard Lyon1, Université de Lyon, Institut Fédératif des Neurosciences de Lyon, Lyon cedex, France. lucie.hanriot@hotmail.fr

ABSTRACT

Background: "Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression), LongSAGE and MPSS (Massively Parallel Signature Sequencing) are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered.

Results: In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method.

Conclusion: We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

Show MeSH

Related in: MedlinePlus

Effect of the library size on the number of unique tags identified. The three figures represent the number of unique tags identified as a function of the total number of tags in random libraries. These libraries were obtained by random sampling of X tags in the library considered (Sanger_Hypo or Solexa_Hypo), where X vary from 1 to the total number of tags in this library. In each of the obtained samples, we also calculated the number of unique tags that matches to the mouse genome (dotted lines). We considered that a tag matches to the genome when it has 100% identity over its whole length (21 bp). A: Figure for the Sanger_Hypo library. B: Figure for the Solexa_Hypo library. C: Figure comparing the number of unique tags identified as a function of the total number of tags between the Sanger_Hypo and the Solexa_Hypo library. The size of the random samples varies consequently from 1 to the size of the Sanger_Hypo library (the smallest of the two libraries).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2562395&req=5

Figure 5: Effect of the library size on the number of unique tags identified. The three figures represent the number of unique tags identified as a function of the total number of tags in random libraries. These libraries were obtained by random sampling of X tags in the library considered (Sanger_Hypo or Solexa_Hypo), where X vary from 1 to the total number of tags in this library. In each of the obtained samples, we also calculated the number of unique tags that matches to the mouse genome (dotted lines). We considered that a tag matches to the genome when it has 100% identity over its whole length (21 bp). A: Figure for the Sanger_Hypo library. B: Figure for the Solexa_Hypo library. C: Figure comparing the number of unique tags identified as a function of the total number of tags between the Sanger_Hypo and the Solexa_Hypo library. The size of the random samples varies consequently from 1 to the size of the Sanger_Hypo library (the smallest of the two libraries).

Mentions: To analyze the depth of transcriptome sampling in the Sanger_Hypo and Solexa_Hypo libraries, we studied the rate of increase of the number of unique tags identified as the size of the corresponding library increases (Figure 5). As shown in Figure 5A, this rate of increase is still high, even when the library size reached the total number of tags in the Sanger_Hypo library. This suggests that we are far from having distinguished each potential tag sequence of the initial hypothalamic sample. In contrary, the rate of increase of the number of unique tags identified decline drastically as we consider several millions of tags from the Solexa_Hypo library (Figure 5B).


A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome.

Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, Scoté-Blachon C, Peyron C, Gandrillon O - BMC Genomics (2008)

Effect of the library size on the number of unique tags identified. The three figures represent the number of unique tags identified as a function of the total number of tags in random libraries. These libraries were obtained by random sampling of X tags in the library considered (Sanger_Hypo or Solexa_Hypo), where X vary from 1 to the total number of tags in this library. In each of the obtained samples, we also calculated the number of unique tags that matches to the mouse genome (dotted lines). We considered that a tag matches to the genome when it has 100% identity over its whole length (21 bp). A: Figure for the Sanger_Hypo library. B: Figure for the Solexa_Hypo library. C: Figure comparing the number of unique tags identified as a function of the total number of tags between the Sanger_Hypo and the Solexa_Hypo library. The size of the random samples varies consequently from 1 to the size of the Sanger_Hypo library (the smallest of the two libraries).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2562395&req=5

Figure 5: Effect of the library size on the number of unique tags identified. The three figures represent the number of unique tags identified as a function of the total number of tags in random libraries. These libraries were obtained by random sampling of X tags in the library considered (Sanger_Hypo or Solexa_Hypo), where X vary from 1 to the total number of tags in this library. In each of the obtained samples, we also calculated the number of unique tags that matches to the mouse genome (dotted lines). We considered that a tag matches to the genome when it has 100% identity over its whole length (21 bp). A: Figure for the Sanger_Hypo library. B: Figure for the Solexa_Hypo library. C: Figure comparing the number of unique tags identified as a function of the total number of tags between the Sanger_Hypo and the Solexa_Hypo library. The size of the random samples varies consequently from 1 to the size of the Sanger_Hypo library (the smallest of the two libraries).
Mentions: To analyze the depth of transcriptome sampling in the Sanger_Hypo and Solexa_Hypo libraries, we studied the rate of increase of the number of unique tags identified as the size of the corresponding library increases (Figure 5). As shown in Figure 5A, this rate of increase is still high, even when the library size reached the total number of tags in the Sanger_Hypo library. This suggests that we are far from having distinguished each potential tag sequence of the initial hypothalamic sample. In contrary, the rate of increase of the number of unique tags identified decline drastically as we consider several millions of tags from the Solexa_Hypo library (Figure 5B).

Bottom Line: Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript.We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis.In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR5534 CNRS Université Claude Bernard Lyon1, Université de Lyon, Institut Fédératif des Neurosciences de Lyon, Lyon cedex, France. lucie.hanriot@hotmail.fr

ABSTRACT

Background: "Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression), LongSAGE and MPSS (Massively Parallel Signature Sequencing) are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered.

Results: In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method.

Conclusion: We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

Show MeSH
Related in: MedlinePlus