RiboaptDB: A Comprehensive Database of Ribozymes and Aptamers

BACKGROUND: Catalytic RNA molecules are called ribozymes. The aptamers are DNA or RNA molecules that have been selected from vast populations of random sequences, through a combinatorial approach known as SELEX. The selected oligo-nucleotide sequences (~200 bp in length) have the ability to recognize a broad range of specific ligands by forming binding pockets. These novel aptamer sequences can bind to nucleic acids, proteins or small organic and inorganic chemical compounds and have many potential uses in medicine and technology. RESULTS: The comprehensive sequence information on aptamers and ribozymes that have been generated by in vitro selection methods are included in this RiboaptDB database. Such types of unnatural data generated by in vitro methods are not available in the public 'natural' sequence databases such as GenBank and EMBL. The amount of sequence data generated by in vitro selection experiments has been accumulating exponentially. There are 370 artificial ribozyme sequences and 3842 aptamer sequences in the total 4212 sequences from 423 citations in this RiboaptDB. We included general search feature, and individual feature wise search, user submission form for new data through online and also local BLAST search. CONCLUSION: This database, besides serving as a storehouse of sequences that may have diagnostic or therapeutic utility in medicine, provides valuable information for computational and theoretical biologists. The RiboaptDB is extremely useful for garnering information about in vitro selection experiments as a whole and for better understanding the distribution of functional nucleic acids in sequence space. The database is updated regularly and is publicly available at

Novel Implementation of Conditional Co-Regulation by Graph Theory to Derive Co-Expressed Genes from Microarray Data

BackgroundMost existing transcriptional databases like Comprehensive Systems-Biology Database (CSB.DB) and Arabidopsis Microarray Database and Analysis Toolbox (GENEVESTIGATOR) help to seek a shared biological role (similar pathways and biosynthetic cycles) based on correlation. These utilize conventional methods like Pearson correlation and Spearman rank correlation to calculate correlation among genes. However, not all are genes expressed in all the conditions and this leads to their exclusion in these transcriptional databases that consist of experiments performed in varied conditions. This leads to incomplete studies of co-regulation among groups of genes that might be linked to the same or related biosynthetic pathway. ResultsWe have implemented an alternate method based on graph theory that takes into consideration the biological assumption – conditional co-regulation is needed to mine a large transcriptional data bank and properties of microarray data. The algorithm calculates relationships among genes by converting discretized signals from the time series microarray data (AtGenExpress) to output strings. A \u27score\u27 is generated by using a similarity index against all the other genes by matching stored strings for any gene queried against our database. Taking carbohydrate metabolism as a test case, we observed that those genes known to be involved in similar functions and pathways generate a high \u27score\u27 with the queried gene. We were also able to recognize most of the randomly selected correlated pairs from Pearson correlation in CSB.DB and generate a higher number of relationships that might be biologically important. One advantage of our method over previously described approaches is that it includes all genes regardless of its expression values thereby highlighting important relationships absent in other contemporary databases. ConclusionBased on promising results, we understand that incorporating conditional co-regulation to study large expression data helps us identify novel relationships among genes. The other advantage of our approach is that mining expression data from various experiments, the genes that do not express in all the conditions or have low expression values are not excluded, thereby giving a better overall picture. This results in addressing known limitations of clustering methods in which genes that are expressed in only a subset of conditions are omitted. Based on further scope to extract information, ASIDB implementing above described approach has been initiated as a model database. ASIDB is available at http://www.asidb.co

Batch Blast Extractor: an automated blastx parser application

MotivationBLAST programs are very efficient in finding similarities for sequences. However for large datasets such as ESTs, manual extraction of the information from the batch BLAST output is needed. This can be time consuming, insufficient, and inaccurate. Therefore implementation of a parser application would be extremely useful in extracting information from BLAST outputs. ResultsWe have developed a java application, Batch Blast Extractor, with a user friendly graphical interface to extract information from BLAST output. The application generates a tab delimited text file that can be easily imported into any statistical package such as Excel or SPSS for further analysis. For each BLAST hit, the program obtains and saves the essential features from the BLAST output file that would allow further analysis. The program was written in Java and therefore is OS independent. It works on both Windows and Linux OS with java 1.4 and higher. It is freely available from: http://mcbc.usm.edu/BatchBlastExtractor

An improved approach for the segmentation of starch granules in microscopic images

<p>Abstract</p> <p>Background</p> <p>Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules.</p> <p>Results</p> <p>We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully.</p> <p>Conclusions</p> <p>We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.</p

Identification of new members of hydrophobin family using primary structure analysis

BACKGROUND: Hydrophobins are fungal proteins that can turn into amphipathic membranes at hydrophilic/hydrophobic interfaces by self-assembly. The assemblages by Class I hydrophobins are extremely stable and possess the remarkable ability to change the polarity of the surface. One of its most important industrial applications is its usage as paint. Without detailed knowledge of the 3D structure and self-assembly principles of hydrophobins, it is difficult to make significant progress in furthering its research. RESULTS: In order to provide useful information to hydrophobin researchers, we analyzed primary structure of hydrophobins to gain more insight about these proteins. In this paper, we presented an in-depth primary sequence analysis using batch BLAST search of the database, sequence filtering by programming and motif finding by MEME. We used batch BLAST to find similar sequences in the NCBI nr database. Then we used MEME to find out motifs. Based on the newly found motifs and the well-known C-CC-C-C-CC-C pattern we used MAST to search the entire nr database. At the end, domain search and phylogenetic analysis were conducted to confirm the result. After searching the nr database with the new PSSM-format motifs identified by MEME, many sequences from various species were found by MAST. Filtering process by pattern, domain and length left 9 qualified candidates. CONCLUSION: All of 9 newly identified potential hydrophobins possess the common pattern and hydrophobin domain. From the multiple sequence alignment result, we can see that some of them are grouped very close to other known hydrophobins, which means their phylogenetic relationship is very close and it is highly plausible that they are indeed hydrophobin proteins

Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

Nucleosome Structure Incorporated Histone Acetylation Site Prediction in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

BackgroundAcetylation is a crucial post-translational modification for histones, and plays a key role in gene expression regulation. Due to limited data and lack of a clear acetylation consensus sequence, a few researches have focused on prediction of lysine acetylation sites. Several systematic prediction studies have been conducted for human and yeast, but less for Arabidopsis thaliana. ResultsConcerning the insufficient observation on acetylation site, we analyzed contributions of the peptide-alignment-based distance definition and 3D structure factors in acetylation prediction. We found that traditional structure contributes little to acetylation site prediction. Identified acetylation sites of histones in Arabidopsis thaliana are conserved and cross predictable with that of human by peptide based methods. However, the predicted specificity is overestimated, because of the existence of non-observed acetylable site. Here, by performing a complete exploration on the factors that affect the acetylability of lysines in histones, we focused on the relative position of lysine at nucleosome level, and defined a new structure feature to promote the performance in predicting the acetylability of all the histone lysines in A. thaliana. ConclusionWe found a new spacial correlated acetylation factor, and defined a ε-N spacial location based feature, which contains five core spacial ellipsoid wired areas. By incorporating the new feature, the performance of predicting the acetylability of all the histone lysines in A. Thaliana was promoted, in which the previous mispredicted acetylable lysines were corrected by comparing to the peptide-based prediction

An Ensemble Learning Approach to Reverse-Engineering Transcriptional Regulatory Networks from Time-Series Gene Expression Data

Background One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations. Results Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences. Conclusion The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

Background One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations. Results Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences. Conclusion The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well