Volume: 2, Issue: 4

Review Article

Predicting Protein Subcellular Localization: Past, Present, and Future

Functional characterization of every single protein is a major challenge of the post- genomic era. The large-scale analysis of a cell’s proteins, proteomics, seeks to provide these proteins with reliable annotations regarding their interaction part- ners and functions in the cellular machinery. An important step on this way is to determine the subcellular localization of each protein. Eukaryotic cells are divided into subcellular compartments, or organelles. Transport across the membrane into the organelles is a highly regulated and complex cellular process. Predicting the subcellular localization by computational means has been an area of vivid activ- ity during recent years. The publicly available prediction methods differ mainly in four aspects: the underlying biological motivation, the computational method used, localization coverage, and reliability, which are of importance to the user. This review provides a short description of the main events in the protein sorting process and an overview of the most commonly used methods in this field.
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Page 209-215

Review Article

A Brief Review of Computational Gene Prediction Methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational meth- ods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene predic- tion methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.
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Page 216-221

Research Article

Granulometric Analysis of Spots in DNA Microarray Images

As the topological properties of each spot in DNA microarray images may vary from one another, we employed granulometries to understand the shape-size con- tent contributed due to a significant intensity value within a spot. Analysis was performed on the microarray image that consisted of 240 spots by using concepts from mathematical morphology. In order to find out indices for each spot and to further classify them, we adopted morphological multiscale openings, which provided microarrays at multiple scales. Successive opened microarrays were sub- tracted to identify the protrusions that were smaller than the size of structuring element. Spot-wise details, in terms of probability of these observed protrusions, were computed by placing a regularly spaced grid on microarray such that each spot was centered in each grid. Based on the probability of size distribution func- tions of these protrusions isolated at each level, we estimated the mean size and texture index for each spot. With these characteristics, we classified the spots in a microarray image into bright and dull categories through pattern spectrum and shape-size complexity measures. These segregated spots can be compared with those of hybridization levels.
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Page 222-236

Research Article

EST-based Analysis of Gene Expression in the Porcine Brain

Since pig is an important livestock species worldwide, its gene expression has been investigated intensively, but rarely in brain. In order to study gene expression profiles in the pig central nervous system, we sequenced and analyzed 43,122 high- quality 5′ end expressed sequence tags (ESTs) from porcine cerebellum, cortex cerebrum, and brain stem cDNA libraries, involving several different prenatal and postnatal developmental stages. The initial ESTs were assembled into 16,101 clus- ters and compared to protein and nucleic acid databases in GenBank. Of these se- quences, 30.6% clusters matched protein databases and represented function known sequences; 75.1% had significant hits to nucleic acid databases and partial repre- sented known function; 73.3% matched known porcine ESTs; and 21.5% had no matches to any known sequences in GenBank. We used the categories defined by the Gene Ontology to survey gene expression in the porcine brain.
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Page 237-244

Letter

Fast Tree Search for A Triangular Lattice Model of Protein Folding

Using a triangular lattice model to study the designability of protein folding, we overcame the parity problem of previous cubic lattice model and enumerated all the sequences and compact structures on a simple two-dimensional triangular lattice model of size 4+5+6+5+4. We used two types of amino acids, hydrophobic and polar, to make up the sequences, and achieved 223+212 different sequences excluding the reverse symmetry sequences. The total string number of distinct compact structures was 219,093, excluding reflection symmetry in the self-avoiding path of length 24 triangular lattice model. Based on this model, we applied a fast search algorithm by constructing a cluster tree. The algorithm decreased the computation by computing the objective energy of non-leaf nodes. The parallel experiments proved that the fast tree search algorithm yielded an exponential speed-up in the model of size 4+5+6+5+4. Designability analysis was performed to understand the search result.
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Page 245-252

Brief Report

A Novel Method for N-terminal Acetylation Prediction

The NetAcet method has been developed to make predictions of N-terminal acety- lation sites, but more information of the data set could be utilized to improve the performance of the model. By employing a new way to extract patterns from sequences and using a sample balancing mechanism, we obtained a correlation coefficient of 0.85, and a sensitivity of 93% on an independent mammalian data set. A web server utilizing this method has been constructed and is available at http://166.111.24.5/acetylation.html.
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Page 253-255

Research Resource

Timeline of Genomics (1977–2004)*

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Page 256-267