Motivation: To attain acceptable sample misassignment rates, current approaches to multiplex single-molecule real-time sequencing require upstream quality improvement, which is obtained from multiple passes over the sequenced insert and significantly reduces the effective read length. In order to fully exploit the raw read length on multiplex applications, robust barcodes capable of dealing with the full single-pass error rates are needed. Results: We present a method for designing sequencing barcodes that can withstand a large number of insertion, deletion and substitution errors and are suitable for use in multiplex single-molecule real-time sequencing. The manuscript focuses on the design of barcodes for full-length single-pass reads, impaired by challenging error rates in the order of 11%. The proposed barcodes can multiplex hundreds or thousands of samples while achieving sample misassignment probabilities as low as 10-7 under the above conditions, and are designed to be compatible with chemical constraints imposed by the sequencing process. Availability and Implementation: Software tools for constructing watermark barcode sets and demultiplexing barcoded reads, together with example sets of barcodes and synthetic barcoded reads, are freely available at www.cifasis-conicet.gov.ar/ezpeleta/NS-watermark . Contact: [email protected].

BACKGROUND: Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. RESULTS: A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. CONCLUSIONS: A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.