ArticleBMC bioinformatics2010
Dynamic probe selection for studying microbial transcriptome with high-density genomic tiling microarrays.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Thermodynamically optimal whole-genome tiling microarray design and validation.BMC research notes · 2016Article
- Roseomics: a blank slate.Current opinion in virology · 2014Review
- Comprehensive transcriptome analysis of the periodontopathogenic bacterium Porphyromonas gingivalis W83.Journal of bacteriology · 2012Article
- Strand-specific transcriptome profiling with directly labeled RNA on genomic tiling microarrays.BMC molecular biology · 2011Article
- Microarray oligonucleotide probe designer (MOPeD): A web service.Open access bioinformatics · 2010Article
- A hidden Markov support vector machine framework incorporating profile geometry learning for identifying microbial RNA in tiling array data.Bioinformatics (Oxford, England) · 2010Article
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Abstract
backgroundCurrent commercial high-density oligonucleotide microarrays can hold millions of probe spots on a single microscopic glass slide and are ideal for studying the transcriptome of microbial genomes using a tiling probe design. This paper describes a comprehensive computational pipeline implemented specifically for designing tiling probe sets to study microbial transcriptome profiles.
resultsThe pipeline identifies every possible probe sequence from both forward and reverse-complement strands of all DNA sequences in the target genome including circular or linear chromosomes and plasmids. Final probe sequence lengths are adjusted based on the maximal oligonucleotide synthesis cycles and best isothermality allowed. Optimal probes are then selected in two stages - sequential and gap-filling. In the sequential stage, probes are selected from sequence windows tiled alongside the genome. In the gap-filling stage, additional probes are selected from the largest gaps between adjacent probes that have already been selected, until a predefined number of probes is reached. Selection of the highest quality probe within each window and gap is based on five criteria: sequence uniqueness, probe self-annealing, melting temperature, oligonucleotide length, and probe position.
conclusionsThe probe selection pipeline evaluates global and local probe sequence properties and selects a set of probes dynamically and evenly distributed along the target genome. Unique to other similar methods, an exact number of non-redundant probes can be designed to utilize all the available probe spots on any chosen microarray platform. The pipeline can be applied to microbial genomes when designing high-density tiling arrays for comparative genomics, ChIP chip, gene expression and comprehensive transcriptome studies.
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