Limits...
Integrating heterogeneous sequence information for transcriptome-wide microarray design; a Zebrafish example.

Rauwerda H, de Jong M, de Leeuw WC, Spaink HP, Breit TM - BMC Res Notes (2010)

Bottom Line: If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence.With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy.The annotation of the microarray is carried out simultaneously with the design.

View Article: PubMed Central - HTML - PubMed

Affiliation: Microarray Department & Integrative Bioinformatics Unit, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. t.m.breit@uva.nl.

ABSTRACT

Background: A complete gene-expression microarray should preferably detect all genomic sequences that can be expressed as RNA in an organism, i.e. the transcriptome. However, our knowledge of a transcriptome of any organism still is incomplete and transcriptome information is continuously being updated. Here, we present a strategy to integrate heterogeneous sequence information that can be used as input for an up-to-date microarray design.

Findings: Our algorithm consists of four steps. In the first step transcripts from different resources are grouped into Transcription Clusters (TCs) by looking at the similarity of all transcripts. TCs are groups of transcripts with a similar length. If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence. Secondly, all TCs are mapped to a genome assembly and gene information is added to the design. Thirdly TC members are ranked according to their trustworthiness and the most reliable sequence is used for the probe design. The last step is the actual array design. We have used this strategy to build an up-to-date zebrafish microarray.

Conclusions: With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy. By changing the parameters in the procedure it is possible to control the similarity within the TCs and thus the amount of candidate sequences for the design. The annotation of the microarray is carried out simultaneously with the design.

No MeSH data available.


Microarray Design Workflow. The diagram of the microarray design workflow is shown together with the rules that are applied at the different stages of the workflow. The parameterization of the Zebrafish array design example, the scripts used, and the counts of the number of entities at each step in the workflow are shown at the right of the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2913925&req=5

Figure 2: Microarray Design Workflow. The diagram of the microarray design workflow is shown together with the rules that are applied at the different stages of the workflow. The parameterization of the Zebrafish array design example, the scripts used, and the counts of the number of entities at each step in the workflow are shown at the right of the figure.

Mentions: The purpose of the Microarray Design Workflow (Figure 2) is to define, over a set of transcript resources, distinct groups of transcripts that represent distinguishable and non-redundant transcripts. These groups, or Transcription Clusters (TCs), will supply the candidate sequences on which the actual microarray probes are designed. Differences between the TCs should be large enough to make the design of a non-redundant probe likely to be successful. Also, the similarity in a TC should be high enough not to merge biologically different transcripts. The design procedure is organized in 4 steps (Figure 2). First the transcripts are clustered. Secondly the TCs are mapped to a gene assembly and reorganized. In the third step the sequences within cluster are ranked according to trustworthiness. Finally the array can be designed using any oligonucleotide design software.


Integrating heterogeneous sequence information for transcriptome-wide microarray design; a Zebrafish example.

Rauwerda H, de Jong M, de Leeuw WC, Spaink HP, Breit TM - BMC Res Notes (2010)

Microarray Design Workflow. The diagram of the microarray design workflow is shown together with the rules that are applied at the different stages of the workflow. The parameterization of the Zebrafish array design example, the scripts used, and the counts of the number of entities at each step in the workflow are shown at the right of the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2913925&req=5

Figure 2: Microarray Design Workflow. The diagram of the microarray design workflow is shown together with the rules that are applied at the different stages of the workflow. The parameterization of the Zebrafish array design example, the scripts used, and the counts of the number of entities at each step in the workflow are shown at the right of the figure.
Mentions: The purpose of the Microarray Design Workflow (Figure 2) is to define, over a set of transcript resources, distinct groups of transcripts that represent distinguishable and non-redundant transcripts. These groups, or Transcription Clusters (TCs), will supply the candidate sequences on which the actual microarray probes are designed. Differences between the TCs should be large enough to make the design of a non-redundant probe likely to be successful. Also, the similarity in a TC should be high enough not to merge biologically different transcripts. The design procedure is organized in 4 steps (Figure 2). First the transcripts are clustered. Secondly the TCs are mapped to a gene assembly and reorganized. In the third step the sequences within cluster are ranked according to trustworthiness. Finally the array can be designed using any oligonucleotide design software.

Bottom Line: If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence.With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy.The annotation of the microarray is carried out simultaneously with the design.

View Article: PubMed Central - HTML - PubMed

Affiliation: Microarray Department & Integrative Bioinformatics Unit, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. t.m.breit@uva.nl.

ABSTRACT

Background: A complete gene-expression microarray should preferably detect all genomic sequences that can be expressed as RNA in an organism, i.e. the transcriptome. However, our knowledge of a transcriptome of any organism still is incomplete and transcriptome information is continuously being updated. Here, we present a strategy to integrate heterogeneous sequence information that can be used as input for an up-to-date microarray design.

Findings: Our algorithm consists of four steps. In the first step transcripts from different resources are grouped into Transcription Clusters (TCs) by looking at the similarity of all transcripts. TCs are groups of transcripts with a similar length. If a transcript is much smaller than a TC to which it is highly similar, it will be annotated as a subsequence of that TC and is used for probe design only if the probe designed for the TC does not query the subsequence. Secondly, all TCs are mapped to a genome assembly and gene information is added to the design. Thirdly TC members are ranked according to their trustworthiness and the most reliable sequence is used for the probe design. The last step is the actual array design. We have used this strategy to build an up-to-date zebrafish microarray.

Conclusions: With our strategy and the software developed, it is possible to use a set of heterogeneous transcript resources for microarray design, reduce the number of candidate target sequences on which the design is based and reduce redundancy. By changing the parameters in the procedure it is possible to control the similarity within the TCs and thus the amount of candidate sequences for the design. The annotation of the microarray is carried out simultaneously with the design.

No MeSH data available.