12419359
Primer design for multiplex PCR using a genetic algorithm
Multiplex Polymerase Chain Reaction (PCR) experiments are used for amplifying several segments of the target DNA simultaneously and thereby to conserve template DNA, reduce the experimental time, and minimize the experimental expense. The success of the experiment is dependent on primer design. However, this can be a dreary task as there are many constrains such as melting temperatures, primer length, GC content and complementarity that need to be optimized to obtain a good PCR product. Motivated by the lack of primer design tools for multiplex PCR genotypic assay, we propose a multiplex PCR primer design tool using a genetic algorithm, which is a stochastic approach based on the concept of biological evolution, biological genetics and genetic operations on chromosomes, to find an optimal selection of primer pairs for multiplex PCR experiments. The presented experimental results indicate that the proposed algorithm is capable of finding a series of primer pairs that obeies the design properties in the same tube.
17764116
Identifying transcriptional regulatory sites in the human genome using an integrated computation
Recently, biological databases and analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and to analyze the gene upstreams using various predictive tools. Beyond methods for predicting transcriptional regulatory sites, new automated and integrated methods for analyzing gene upstream sequences on a higher level are urgently required. We present an integrated system to predict transcriptional regulatory sites and detect co-occurrence of these regulatory sites after a set of genes are input. The system comprises a biological data warehousing system, pattern discovery programs, pattern occurrence association detectors and user interfaces. User profiles and history pages enable users to trace the sequence analyses for transcriptional regulatory sites.
1404678
SpliceInfo: an information repository for mRNA alternative splicing in human genome
We have developed an information repository named SpliceInfo to collect the occurrences of the four major alternative-splicing (AS) modes in human genome; these include exon skipping, 5′-alternative splicing, 3′-alternative splicing and intron retention. The dataset is derived by comparing the nucleotide and protein sequences available for a given gene for evidence of AS. Additional features such as the tissue specificity of the mRNA, the protein domain contained by exons, the GC-ratio of exons, the repeats contained within the exons, and the Gene Ontology are annotated computationally for each exonic region that is alternatively spliced. Motivated by a previous investigation of AS-related motifs such as exonic splicing enhancer and exonic splicing silencer, this resource also provides a means of identifying motifs candidates and this should help to identify potential regulatory mechanisms within a particular exonic sequence set and its two flanking intronic sequence sets. This is carried out using motif discovery tools to identify motif candidates related to alternative splicing regulation and together with a secondary structure prediction tool, will help in the identification of the structural properties of such regulatory motifs. The integrated resource is now available on http://SpliceInfo.mbc.NCTU.edu.tw/.
3255925
A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome
Very large-scale gene expression analysis, i.e., UniGene and dbEST, are provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differential gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.
6014802
Identifying the combination of genetic factors that determine susceptibility to cervical cancer
Cervical cancer is common among women all over the world. Although infection with high-risk types of human papillomavirus (HPV) has been identified as the primary cause of cervical cancer, only some of those infected go on to develop cervical cancer. Obviously, the progression from HPV infection to cancer involves other environmental and host factors. Recent population-based twin and family studies have demonstrated the importance of the hereditary component of cervical cancer, associated with genetic susceptibility. Consequently, single-nucleotide polymorphism (SNP) markers and microsatellites should be considered genetic factors for determining what combinations of genetic factors are involved in precancerous changes to cervical cancer. This study employs a Bayesian network and four different decision tree algorithms, and compares the performance of these learning algorithms. The results of this study raise the possibility of investigations that could identify combinations of genetic factors, such as SNPs and microsatellites, that influence the risk associated with common complex multifactorial diseases, such as cervical cancer. The web site associated with this study is http://140.115.155.8/FactorAnalysis/.
7391844
Database of repetitive elements in complete genomes and data mining using transcription factor binding sites
Approximately 43% of the human genome is occupied by repetitive elements. Even more, around 51% of the rice genome is occupied by repetitive elements. The analysis presented here indicates that repetitive elements in complete genomes may have been very important in the evolutionary genomics. In this study, a database, called the Repeat Sequence Database, is first designed and implemented to store complete and comprehensive repetitive sequences. See http://rsdb.csie.ncu.edu.tw for more information. The database contains direct, inverted and palindromic repetitive sequences, and each repetitive sequence has a variable length ranging from seven to many hundred nucleotides. The repetitive sequences in the database are explored using a mathematical algorithm to mine rules on how combinations of individual binding sites are distributed among repetitive sequences in the database. Combinations of transcription factor binding sites in the repetitive sequences are obtained and then data mining techniques are applied to mine association rules from these combinations. The discovered associations are further pruned to remove insignificant associations and obtain a set of associations. The mined association rules facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Experiments are performed on several genomes including C. elegans, human chromosome 22, and yeast.
12475257
ProClass Protein Family Database
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html
35235754
Gene Family Identification Network Design for Protein Sequence Analysis
With the exponential accumulation of sequence data, continued progress in the Human Genome Project will depend increasingly on advanced computational tools to manage and analyze the data. Utilizing information embedded within families of homologous sequences, a gene family identification approach may facilitate the understanding of gene functions. We have developed a GeneFIND (Gene Family Identification Network Design) system for database searching against gene families. It provides rapid and accurate protein family identification by combining global and motif sequence similarities and incorporating ProClass family information. Multi-level filters are used, starting with the MOTIFIND neural networks and BLAST search, followed by SSEARCH alignment, motif pattern match, hidden Markov modeling of motifs and ClustalW motif alignment. GeneFIND has been implemented as a full-scale system for the classification of more than 1200 ProSite and 6000 PIR families. It has been used to identify thousands of new family members and is well suited for genomic sequence analysis. The system is available for on-line family identification from our WWW server ().
172381
Robust VQ-based digital watermarking for memoryless binary symmetric channel
Vector quantization (VQ) has been distinguished for its high compression rate in lossy data compression applications. And VQ-based watermarking plays a newly developed branch in digital watermarking research fields. In this paper, we propose optimized schemes for VQ-based image watermarking. We overcome the VQ index assignment problem with genetic algorithm, which is suitable for transmitting the watermarked image over noisy channels. We obtain better robustness of the watermarking algorithm against the effects caused by channel noise in our simulations after inspecting the results from several test images. In addition, to compare with existing schemes in literature, the watermarked image quality in our scheme has approximately the same quality, with better performance in robustness, to the schemes proposed by other researchers. This also proves the effectiveness of our proposed schemes in VQ-based image watermarking for copyright protection.
635773
Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected 3D objects and has been used in many applications in medical imaging. Previous SPHARM techniques use the first order ellipsoid for establishing surface correspondence and aligning objects. However, this first order information may not be sufficient in many cases; a more general method for establishing surface correspondence would be to minimize the mean squared distance between two corresponding surfaces. In this paper, a new surface matching algorithm is proposed for 3D SPHARM models to achieve this goal. This algorithm employs a useful rotational property of spherical harmonic basis functions for a fast implementation. Applications of medical image analysis (e.g., spatio-temporal modeling of heart shape changes) are used to demonstrate this approach. Theoretical proofs and experimental results show that our approach is an accurate and flexible surface correspondence alignment method.
2900872
Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation
MOTIVATION
A critical challenge of the post-genomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis regulatory features and map such features into expression patterns to classify genes into distinct networks. However, these methods are not focused on distinguishing between differentially regulated genes within a given network. Here we describe an unsupervised machine learning method, termed GPS for gene promoter scan, that discriminates among co-regulated promoters by simultaneously considering both cis-acting regulatory features and gene expression. GPS is particularly useful for knowledge discovery in environments with reduced datasets and high levels of uncertainty.


RESULTS
Application of this method to the enteric bacteria Escherichia coli and Salmonella enterica uncovered novel members, as well as regulatory interactions in the regulon controlled by the PhoP protein that were not discovered using previous approaches. The predictions made by GPS were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription, and is a central element in a highly connected network.


AVAILABILITY
The scripts and programs used in this work are accessible from the gps-tools.wustl.edu website. Data and predictions are available by request.