An evaluation of custom microarray applications: the oligonucleotide design challenge

Abstract

The increase in feature resolution and the availability of multipack formats from microarray providers has opened the way to various custom genomic applications. However, oligonucleotide design and selection remains a bottleneck of the microarray workflow. Several tools are available to perform this work, and choosing the best one is not an easy task, nor are the choices obvious. Here we review the oligonucleotide design field to help users make their choice. We have first performed a comparative evaluation of the available solutions based on a set of criteria including: ease of installation, user-friendly access, the number of parameters and settings available. In a second step, we chose to submit two real cases to a selection of programs. Finally, we used a set of tests for the in silico benchmark of the oligo sets obtained from each type of software. We show that the design software must be selected according to the goal of the scientist, depending on factors such as the organism used, the number of probes required and their localization on the target sequence. The present work provides keys to the choice of the most relevant software, according to the various parameters we tested.

Figure 1.

Comparison of the sensitivity of the oligonucleotides designed for the custom mouse array. For each oligonucleotide set created we plot the distribution for all oligonucleotides in the set of T_m (A) and free energies of the most probable secondary structure (B). The name of the software used for design is displayed on the x-axis. AOS stands for ArrayOligoSelector.

Figure 2.

Evaluation of custom oligonucleotide specificity. (A) Duplex free energies between oligonucleotides and their best off-target hit. (B) Distribution of the distance between the 5′ of the oligonucleotide and the 3′ of the target gene sequence for each designed oligoset. The name of the software used for design is displayed on the x-axis. AOS stands for ArrayOligoSelector.

Figure 3.

Comparison of the sensitivity of the oligonucleotides designed for tiling array. For each oligonucleotide set created we plot the distribution for all oligonucleotides in the set of T_m (A), GC percent (B) and free energies of the most probable secondary structure (C).

Figure 4.

Evaluation of tiling oligonucleotide specificity. (A) Distribution of the distance in base pair between oligonucleotide that follows each other on the tiling path. (B) Distribution of the number of oligonucleotide by transcript. (C) Distribution of the number of BLAST hits by oligonucleotide using the parameters described in the ‘Material and methods’ section. The y-axis is log scaled. To clearly display these distributions we removed all oligonucleotides with only one hit.