A Strategy for High Throughput Microarray Oligo Probe Design Using 454 and Sanger Sequencing Data of Eisenia fetida Expressed Sequence Tags
Document Type
Conference Proceeding
Publication Date
12-1-2008
School
Computing Sciences and Computer Engineering
Abstract
High density oligonucleotide probe arrays have increasingly become an important tool in genomics studies. However, the lack of full genome sequences for many non-genomics model organisms has hampered researchers to pursue relevant ecological, toxicological, and other non-fundamental genomics questions. Historically, researchers have sequenced ESTs in the organism of interest prior to or in the absence of genome sequencing. Recent technological advances such as massively parallel pyrosequencing (454 sequencing) have provided ultra high throughput opportunities to sequence millions of bases in a few hours. Nevertheless, the sequence reads produced by the next generation sequencing technologies are relatively short and needs to be assembled into longer contigs, which created new challenges for probe design. Ideally, one oligo probe should target one unique gene or gene product (e.g., transcript). In the absence of genome sequence, one strategy is to target every unique transcript sequence while reduce the number of unique sequences (and costs of arrays) through bioinformatic analysis and experimental testing. Here we report a case study and demonstrate how this strategy was applied in the design of a transcriptome-wide oligo array for a non-gneomics model organisms Eisenia fetida.
Publication Title
Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
First Page
1040
Last Page
1043
Recommended Citation
Gong, P.,
Pirooznia, M.,
Guan, X.,
Vera, J.,
Liang, C.,
Deng, Y.,
Zhang, C.,
Perkins, E.
(2008). A Strategy for High Throughput Microarray Oligo Probe Design Using 454 and Sanger Sequencing Data of Eisenia fetida Expressed Sequence Tags. Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008, 1040-1043.
Available at: https://aquila.usm.edu/fac_pubs/17945