Tabu search algorithm for DNA sequencing by hybridization with isothermic libraries

An Algorithm for Sequencing by Hybridization Based on an Alternating DNA Chip

Sequencing by hybridization allows the reconstruction of the DNA string of a given length from smaller fragments. These fragments are obtained in the hybridization experiment in which the DNA hybridizes to a DNA chip. In a classical approach, the chip consists of all oligonucleotides of a given length, with only one type of oligonucleotide for each probe of the chip. In this paper, we propose an algorithm solving the non-classical case of SBH, where the chip probes consist set of oligonucleotides described by some specific pattern. We will present the definition of such a non-classical DNA chip and the algorithm solving a sequencing problem related to such a chip. Unlike recent metaheuristic approaches to the classical SBH problem, the proposed algorithm tries to find an exact sequence, and even in the presence of all the hybridization errors in spectrum is very often able to do so in a short time. If only negative errors from repetitions are allowed, then the algorithm is able to reconstruct sequences having length of thousands nucleotides.

A multilevel ant colony optimization algorithm for classical and isothermic DNA sequencing by hybridization with multiplicity information available

The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip.

Whole genome assembly from 454 sequencing output via modified DNA graph concept

Recently, 454 Life Sciences Corporation proposed a new biochemical approach to DNA sequencing (the 454 sequencing). It is based on the pyrosequencing protocol. The 454 sequencing aims to give reliable output at a low cost and in a short time. The produced sequences are shorter than reads produced by classical methods. Our paper proposes a new DNA assembly algorithm which deals well with such data and outperforms other assembly algorithms used in practice. The constructed SR-ASM algorithm is a heuristic method based on a graph model, the graph being a modified DNA graph proposed for DNA sequencing by hybridization procedure. Other new features of the assembly algorithm are, among others, temporary compression of input sequences, and a new and fast multiple alignment heuristics taking advantage of the way the output data for the 454 sequencing are presented and coded. The usefulness of the algorithm has been proved in tests on raw data generated during sequencing of the whole 1.84Mbp genome of Prochlorococcus marinus bacteria and also on a part of chromosome 15 of Homo sapiens. The source code of SR-ASM can be downloaded from http://bio.cs.put.poznan.pl/ in the section 'Current research'--> 'DNA Assembly'. Among publicly available assemblers our algorithm appeared to generate the best results, especially in the number of produced contigs and in the lengths of the contigs with high similarity to the genome sequence.

Optimal probe length varies for targets with high sequence variation: implications for probe library design for resequencing highly variable genes

BACKGROUND

Sequencing by hybridisation is an effective method for obtaining large amounts of DNA sequence information at low cost. The efficiency of SBH depends on the design of the probe library to provide the maximum information for minimum cost. Long probes provide a higher probability of non-repeated sequences but lead to an increase in the number of probes required whereas short probes may not provide unique sequence information due to repeated sequences. We have investigated the effect of probe length, use of reference sequences, and thermal filtering on the design of probe libraries for several highly variable target DNA sequences.

RESULTS

We designed overlapping probe libraries for a range of highly variable drug target genes based on known sequence information and develop a formal terminology to describe probe library design. We find that for some targets these libraries can provide good coverage of a previously unseen target whereas for others the coverage is less than 30%. The optimal probe length varies from as short at 12 nt to as large as 19 nt and depends on the sequence, its variability, and the stringency of thermal filtering. It cannot be determined from inspection of an example gene sequence.

CONCLUSIONS

Optimal probe length and the optimal number of reference sequences used to design a probe library are highly target specific for highly variable sequencing targets. The optimum design cannot be determined simply by inspection of input sequences or of alignments but only by detailed analysis of the each specific target. For highly variable sequences, shorter probes can in some cases provide better information than longer probes. Probe library design would benefit from a general purpose tool for analysing these issues. The formal terminology developed here and the analysis approaches it is used to describe will contribute to the development of such tools.

Dealing with repetitions in sequencing by hybridization

DNA sequencing by hybridization (SBH) induces errors in the biochemical experiment. Some of them are random and disappear when the experiment is repeated. Others are systematic, involving repetitions in the probes of the target sequence. A good method for solving SBH problems must deal with both types of errors. In this work we propose a new hybrid genetic algorithm for isothermic and standard sequencing that incorporates the concept of structured combinations. The algorithm is then compared with other methods designed for handling errors that arise in standard and isothermic SBH approaches. DNA sequences used for testing are taken from GenBank. The set of instances for testing was divided into two groups. The first group consisted of sequences containing positive and negative errors in the spectrum, at a rate of up to 20%, excluding errors coming from repetitions. The second group consisted of sequences containing repeated oligonucleotides, and containing additional errors up to 5% added into the spectra. Our new method outperforms the best alternative procedures for both data sets. Moreover, the method produces solutions exhibiting extremely high degree of similarity to the target sequences in the cases without repetitions, which is an important outcome for biologists. The spectra prepared from the sequences taken from GenBank are available on our website http://bio.cs.put.poznan.pl/.

Machine learning in bioinformatics

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

Multistage isothermic sequencing by hybridization

Despite the impressive progress in many areas of biological sciences reading DNA sequences still remains one of the most important problems. One of the methods of DNA sequencing is sequencing by hybridization which is composed of two stages--the biochemical one and the computational one. Although this method is quite modern it suffers sensitivity to errors appearing in the biochemical stage. This is a motivation for developing some new variants of the method which should be more resistant to errors of these types. Two of such non-standard approaches are multistage and isothermic sequencing by hybridization. Each of the methods is less sensitive to some types of the errors appearing in the biochemical stage, in comparison to the standard version of the method, but on the other hand, they are more sensitive to remaining types of errors. However, a combination of the two approaches reduces the sensitivity of the components.