Dynamic probe selection for studying microbial transcriptome with high-density genomic tiling microarrays

Abstract

Background: Current 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.

Results: The 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.

Conclusions: The 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.

Figure 1

Overview of the tiling array probe design pipeline.

Figure 2

Schematics of the probe evaluation within selection windows. A) Collection of all qualified probe sequences within each window. All probe properties are collected for every probe sequence that starts within the same selection window. Probes closer than minimal distance to existing selected probes are excluded and windows containing already selected probes due to repeated sequences are omitted. B) Selection of the highest quality probe within the window. Screening for the best probe within each selection window starts in the order of cross-hybridization level 1 through 4. Probe sequences of the same level are evaluated based on the following criteria: deviance from median Tm; probe self-annealing; BLAST percentage identity and identity stretch; and, probe length and position.

Figure 3

Schematics of the gap-filling. A) Collect all qualified probe sequences within each gap. This involves removing probe sequences at the gap ends and other repeated probe sequences that appear within minimal distance to already selected probes. B) Collect the highest quality probe within the gap. In the first probe screening step, one best probe representative from each gap is collected based on the same ranking criteria used in Figure 2B. C) Selection of the best probe among all gaps. The best probes, one highest quality probe from each gap, are ordered by descending gap size and then by the following criteria: BLAST percentage identity and stretch; deviance from median Tm; probe self-annealing; and, probe length and position. The highest quality probe from the largest gap is selected. Every additional probe selected generates two new gaps which are orderly added to the existing gaps. The gap-filling is repeated until all probe spots on the target microarray are used.