Catálogo de publicaciones - libros

A data-adaptive approach for cDNA microarray image enhancement is presented. Through the weighting coefficients adaptively determined from local microarray image statistics, the proposed technique tunes the overall filter’s detail-preserving and noise-attenuating characteristics and uses both the spatial and spectral correlation of the cDNA image during processing. Noise removal is performed by tuning a membership function which utilizes the aggregated absolute differences between the cDNA microarray inputs localized within a processing window sliding over the image.

RNA interference (RNAi) is a posttranscriptional gene silencing mechanism frequently used to study gene functions and knock down viral genes. RNAi has been regarded as a highly effective means of gene repression. However, an “off-target effect” deteriorates its specificity and applicability. The complete off-target effects can only be characterized by examining all factors through systematic investigation of each gene in a genome. However, this complete investigation is too expensive to conduct experimentally which motivates a computational study. The sequence matching between an siRNA and its target mRNA allows for mismatches, G-U wobbles, and the secondary structure bulges, in addition to exact matches. To simulate these matching features, we propose string kernels measuring the similarity between two oligonucleotides and develop novel efficient implementations for RNAi off-target detection. We apply the algorithms for off-target errors in and human.

A new kernel has been developed for vectors derived from a coding scheme of the tri-peptide composition for protein sequences. This kernel defines the sequence similarity through a mapping that transforms a tri-peptide coding vector into a new vector based on a matrix formed by the high BLOSUM scores associated with pairs of tri-peptides. In conjunction with the use of support vector machines, the effectiveness of the new kernel is evaluated against the conventional coding schemes of -peptide ( ≤ 3) for the prediction of subcellular localizations of proteins in Gram-negative bacteria. It is demonstrated that the new method outperforms all the other methods in a 5-fold cross-validation.

In this paper, we propose a method for reconstructing phylogenetic trees of a given set of prokaryote organisms by randomly sampling relatively small oligopeptides of a .xed length from their complete proteomes. For each of the organisms, a vector of frequencies of those sampled oligopeptides is generated and used as a building block in reconstructing phylogenetic trees. By this procedure, phylogenetic trees are generated independently, and a consensus tree of the resulting trees is obtained. We have applied our method to a set of 109 organisms, including 16 Archaea, 87 Bacteria, and 6 Eukarya, using less 10 of all the 3,200,000 oligopeptides of length 5. Our consensus tree agrees with the tree of Bergey’s Manual in most of the basic taxa. In addition, they have almost the same quality as the trees of the same organisms reconstructed using the 20 oligopeptides of length = 5 and 6 given by Qi . Thus we can conclude that, the frequencies of a relatively small number of oligopeptides of length 5, even if those oligopeptides are determined in a random method, has phylogenetic information almost equivalent to the frequencies of the oligopeptides of length 5 or 6.

Phylogenetic networks model evolutionary histories in the presence of non-treelike events such as hybrid speciation and horizontal gene transfer. In spite of their widely acknowledged importance, very little is known about phylogenetic networks, which have so far been studied mostly for specific datasets.

Even when the evolutionary history of a set of species is non-treelike, individual genes in these species usually evolve in a treelike fashion. An important question, then, is whether a gene tree is “contained” inside a species network. This information is used to detect the presence of events such as horizontal gene transfer and hybrid speciation. Another question of interest for biologists is whether a group of taxa forms a clade based on a given phylogeny. This can be efficiently answered when the phylogeny is a tree simply by inspecting the edges of the tree, whereas no efficient solution currently exists for the problem when the phylogeny is a network. In this paper, we give polynomial-time algorithms for answering the above two questions.

There are several computer programs that align mRNA with its genomic counterpart to determine exon boundaries. Though most of these programs perform such alignment efficiently and accurately, they can only tolerate a relatively small number of sequencing errors. These programs also highly depend on the GT/AG rule in finding splice sites. Both properties make them less desirable in the case of aligning EST reconstructed transcript with genomic DNA to identify splicing variants, where a lot of sequencing errors and non-canonical splice sites are expected. Using a novel heuristic algorithm, we developed a tool that can handle much more sequencing errors. Test dataset results indicated that SWAT (Sequencing-error Well-handled Alignment Tool) has a much stronger error-handling ability than Sim4 and Spidey, two other popular spliced alignment tools. In the presence of up to 10 percent randomly introduced sequencing errors, it can still give the precise number of exons and exon boundaries in most cases. The robustness of SWAT makes it a desirable tool in cases where sequencing error is a concern. A web service is freely available at http://app1.unmc.edu/swat/swat.html.

Comparative RNA sequence analyses have contributed remarkably accurate predictions. The recent determination of the 30S and 50S ribosomal subunits brought more supporting evidence. Several inference tools are combining free-energy minimisation and comparative analysis to improve the quality of secondary structure predictions. Using many input sequences should improve the accuracy, reduce the likelihood that bad predictions are made, but also lower the sensitivity. To investigate these claims, we have extended the software system Dynalign to use three input sequences, rather than two, and tested our algorithm with 10 tRNAs and 13 5S rRNAs. The following hypotheses were tested: 1) the use of three input sequences improves the average accuracy compared to predictions based on two input sequences. Also, it should be less likely that all three input sequences simultaneously fold into a bad free-energy minimum compared to predictions based on two sequences, consequently, 2) the worse prediction (minimum accuracy) for any sequence should be more accurate when three input sequences are used rather than two. Finally, the consensus structure of three sequences is probably less representative of the individual sequences. 3) Therefore, the average coverage should be less.

This paper presents an innovative, adaptive variant of Kohonen’s self-organizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on the best architecture for the self-organizing map. Like the traditional SOMs, this clustering technique also provides useful information about the relationship between the resulting clusters. Applications of the resulting software to clustering biological data are discussed in detail.

This paper deals with the computational problem of inferring complete information on haplotypes from haplotypes with missing data. This problem is one of the main issues in , as the current DNA sequencing technology often produces haplotypes with missing bases and thus the complete information on haplotypes has to be inferred through computational methods. In this paper we propose a new algorithmic approach to the problem that assumes both the Coalescent and the Minimum Entropy models and we provide an experimental analysis relating it to the previously investigated approaches. In particular, the reconstruction of a perfect phylogeny from haplotypes with missing data is addressed.

In this paper, we improve the homology search performance by the combination of the predicted protein secondary structures and protein sequences. Previous research suggested that the straightforward combination of predicted secondary structures did not improve the homology search performance, mostly because the errors in the structure prediction. We solved this problem by taking into account the confidence scores output by the prediction programs.