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PubMed2005

Understanding platelets. Lessons from proteomics, genomics and promises from network analysis.

New large-scale analysis techniques such as bioinformatics, mass spectrometry and SAGE data analysis will allow a new framework for understanding platelets. This review analyses some important options and tasks for these tools and examines an outline of the new, refined picture of the platelet outlined by these new techniques. Looking at the platelet-specific building blocks of genome, (active) transcriptome and proteome (notably secretome and phospho-proteome), we summarize current bioinformatical and biochemical approaches, tasks as well as their limitations. Understanding the surprisingly complex platelet regarding compartmentalization, key cascades, and pathways including clinical implications will remain an exciting and hopefully fruitful challenge for the future.

Animals↗

PubMed2006

Genome-wide expression studies of atherosclerosis: critical issues in methodology, analysis, interpretation of transcriptomics data.

During the past 6 years, gene expression profiling of atherosclerosis has been used to identify genes and pathways relevant in vascular (patho)physiology. This review discusses some critical issues in the methodology, analysis, and interpretation of the data of gene expression studies that have made use of vascular specimens from animal models and humans. Analysis of gene expression studies has evolved toward the genome-wide expression profiling of large series of individual samples of well-characterized donors. Despite the advances in statistical and bioinformatical analysis of expression data sets, studies have not yet fully exploited the potential of gene expression data sets to obtain novel insights into the molecular mechanisms underlying atherosclerosis. To assess the potential of published expression data, we compared the data of a CC chemokine gene cluster between 18 murine and human gene expression profiling articles. Our analysis revealed that an adequate comparison is mainly hindered by the incompleteness of available data sets. The challenge for future vascular genomic profiling studies will be to further improve the experimental design, statistical, and bioinformatical analysis and to make data sets freely accessible.

Animals↗

PubMed2003

Identification of novel genes preferentially expressed in the retina using a custom human retina cDNA microarray.

PURPOSE: To construct a custom cDNA microarray for comprehensive human retinal gene expression profiling and apply it to the identification of genes that are preferentially expressed in the retina. METHODS: A cDNA microarray was constructed based on the predicted human retina gene expression profile according to expressed sequence tag (EST) databases. Gene expression profiles were obtained from five human retinas, two livers, and the cerebral cortical regions of two brains. Each sample was studied in duplicate, using a reference sample experimental design. Retina-enriched genes were identified by using the significance analysis for microarray (SAM) algorithm. Quantitative real time PCR was used to confirm microarray results. Bioinformatic analysis was performed to compare the array results with expression data available from public databases. RESULTS: The cDNA microarray contains 10,034 sequences: 67% represent known genes and 33% represent ESTs. Differential hybridization with the array identified, in addition to known retinal genes, 186 retina-enriched genes that do not have known retinal function. Of these, 96 represent novel genes. Quantitative real-time PCR of 11 of the identified genes and ESTs confirmed their retina-enriched expression pattern. Bioinformatic analysis of EST databases suggests that of the 186 genes, approximately 40% are predominantly expressed in the retina, whereas the remainder show significant expression in other tissues. Comparison of this study's microarray-based retina-enriched gene set with three published similar sets identified using complementary high-throughput approaches demonstrated only limited overlap of the identified genes. CONCLUSIONS: Because previous studies have demonstrated that many retina-enriched genes are crucial for maintaining normal retinal function, the genes identified here are likely to include ones that have important roles in the retina and ones that when mutated can cause or modulate retinal disease. In addition, the retina custom array should provide a useful resource for comparing expression profiles between normal and diseased human retinas.

Adult↗

PubMed2004

Characterization of new transcripts enriched in the mouse retina and identification of candidate retinal disease genes.

PURPOSE: Most retinal disease genes are preferentially expressed in photoreceptors, the light-sensitive cells involved in phototransduction. In addition, some of the genes linked to retinal diseases are essential for normal retinal development. The goal of this study was to identify new transcripts enriched in photoreceptors involved in retinal development or diseases. METHODS: To isolate uncharacterized retinal transcripts, the bioinformatic method Digital Differential Display (DDD) was used. RNA in situ hybridization was used to characterize gene-expression patterns. RESULTS: Twenty-seven mouse ESTs highly represented in retinal libraries were identified. Eight ESTs were predominantly expressed in photoreceptors and/or in the retinal pigment epithelium (RPE), whereas transcripts for other ESTs were detected more ubiquitously in the retinal cells or abundantly in ganglion cells and/or the inner nuclear layer. Mapping of the corresponding human orthologues of the photoreceptor/RPE-enriched genes revealed that two of them are candidate disease genes for retinitis pigmentosa, loci RP22 and RP28. Both of these are predominantly expressed in rod photoreceptors. The candidate RP22 gene codes for a putative transmembrane protein showing homology to Cln8 (ceroid lipofuscinosis, neuronal 8), in which gene mutations are associated with photoreceptors degeneration in mice. Also identified were two genes expressed in photoreceptors that are candidate disease genes for recessive Bardet-Biedl syndrome type 3 (BBS3) and recessive ataxia with RP (AXPC1). CONCLUSIONS: This study demonstrates how bioinformatic analysis can be used to identify novel tissue-specific genes relevant to development and diseases.

Animals↗

PubMed2004

High-throughput siRNA-based functional target validation.

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.

Base Sequence↗

PubMed2003

T-cell epitopes within the complementarity-determining and framework regions of the tumor-derived immunoglobulin heavy chain in multiple myeloma.

The idiotypic structure of the monoclonal immunoglobulin (Ig) in multiple myeloma (MM) might be regarded as a tumor-specific antigen. The present study was designed to identify T-cell epitopes of the variable region of the Ig heavy chain (VH) in MM (n = 5) using bioinformatics and analyze the presence of naturally occurring T cells against idiotype-derived peptides. A large number of human-leukocyte-antigen (HLA)-binding (class I and II) peptides were identified. The frequency of predicted epitopes depended on the database used: 245 in bioinformatics and molecular analysis section (BIMAS) and 601 in SYFPEITHI. Most of the peptides displayed a binding half-life or score in the low or intermediate affinity range. The majority of the predicted peptides were complementarity-determining region (CDR)-rather than framework region (FR)-derived (52%-60% vs 40%-48%, respectively). Most of the predicted peptides were confined to the CDR2-FR3-CDR3 "geographic" region of the Ig-VH region (70%), and significantly fewer peptides were found within the flanking (FR1-CDR1-FR2 and FR4) regions (P <.01). There were 8- to 10-amino acid (aa) long peptides corresponding to the CDRs and fitting to the actual HLA-A/B haplotypes that spontaneously recognized, albeit with a low magnitude, type I T cells (interferon gamma), indicating an ongoing major histocompatibility complex (MHC) class I-restricted T-cell response. Most of those peptides had a low binding half-life (BIMAS) and a low/intermediate score (SYFPEITHI). Furthermore, 15- to 20-aa long CDR1-3-derived peptides also spontaneously recognized type I T cells, indicating the presence of MHC class II-restricted T cells as well. This study demonstrates that a large number of HLA-binding idiotypic peptides can be identified in patients with MM. Such peptides may spontaneously induce a type I MHC class I- as well as class II-restricted memory T-cell response.

Aged↗

PubMed2002

Homology induction: the use of machine learning to improve sequence similarity searches.

BACKGROUND: The inference of homology between proteins is a key problem in molecular biology The current best approaches only identify approximately 50% of homologies (with a false positive rate set at 1/1000). RESULTS: We present Homology Induction (HI), a new approach to inferring homology. HI uses machine learning to bootstrap from standard sequence similarity search methods. First a standard method is run, then HI learns rules which are true for sequences of high similarity to the target (assumed homologues) and not true for general sequences, these rules are then used to discriminate sequences in the twilight zone. To learn the rules HI describes the sequences in a novel way based on a bioinformatic knowledge base, and the machine learning method of inductive logic programming. To evaluate HI we used the PDB40D benchmark which lists sequences of known homology but low sequence similarity. We compared the HI methodology with PSI-BLAST alone and found HI performed significantly better. In addition, Receiver Operating Characteristic (ROC) curve analysis showed that these improvements were robust for all reasonable error costs. The predictive homology rules learnt by HI by can be interpreted biologically to provide insight into conserved features of homologous protein families. CONCLUSIONS: HI is a new technique for the detection of remote protein homology--a central bioinformatic problem. HI with PSI-BLAST is shown to outperform PSI-BLAST for all error costs. It is expect that similar improvements would be obtained using HI with any sequence similarity method.

Algorithms↗

PubMed2002

The SGS3 protein involved in PTGS finds a family.

BACKGROUND: Post transcriptional gene silencing (PTGS) is a recently discovered phenomenon that is an area of intense research interest. Components of the PTGS machinery are being discovered by genetic and bioinformatics approaches, but the picture is not yet complete. RESULTS: The gene for the PTGS impaired Arabidopsis mutant sgs3 was recently cloned and was not found to have similarity to any other known protein. By a detailed analysis of the sequence of SGS3 we have defined three new protein domains: the XH domain, the XS domain and the zf-XS domain, that are shared with a large family of uncharacterised plant proteins. This work implicates these plant proteins in PTGS. CONCLUSION: The enigmatic SGS3 protein has been found to contain two predicted domains in common with a family of plant proteins. The other members of this family have been predicted to be transcription factors, however this function seems unlikely based on this analysis. A bioinformatics approach has implicated a new family of plant proteins related to SGS3 as potential candidates for PTGS related functions.

Amino Acid Sequence↗

PubMed2005

Evaluation of BioCreAtIvE assessment of task 2.

BACKGROUND: Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed. RESULTS: The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein--GO term--article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment. CONCLUSION: Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

Computational Biology↗

PubMed2005

Improving interoperability between microbial information and sequence databases.

BACKGROUND: Biological resources are essential tools for biomedical research. Their availability is promoted through on-line catalogues. Common Access to Biological Resources and Information (CABRI) is a service for distribution of biological resources and related data collected by 28 European culture collections. Linking this information to bioinformatics databanks can make the collections' holdings more visible after a search in molecular biology databanks and vice-versa. Identification of links to sequence databases can be useful, but annotation and indexing problems, together with compilation errors, immediately arise. In this paper, we present our efforts for the identification of cross-references between CABRI catalogues and the EMBL Data Library and related results. RESULTS: An SRS site with both EMBL and CABRI catalogues has been set up. Ad-hoc changes in indexing scripts allowed to achieve homogeneous index keys and SRS link features have been used to identify links between databases. After manual checking and comparison with an alternative procedure, about 67,500 valid cross-references were identified, added to the EMBL Data Library and are now distributed with it. HTML links can be established from EMBL to CABRI network service. Procedures can be executed whenever needed. CONCLUSION: Links between EMBL and CABRI catalogues constitute an improved access to micro-organisms of certified quality and can produce positive effects on biomedical research. Further links between CABRI catalogues and other bioinformatics databases can now easily be defined by using these cross-references. Linking genetic information onto natural resources information may stand model for the integration of other databases containing empirical data on these materials.

Base Sequence↗

PubMed2005

HmtDB, a human mitochondrial genomic resource based on variability studies supporting population genetics and biomedical research.

BACKGROUND: Population genetics studies based on the analysis of mtDNA and mitochondrial disease studies have produced a huge quantity of sequence data and related information. These data are at present worldwide distributed in differently organised databases and web sites not well integrated among them. Moreover it is not generally possible for the user to submit and contemporarily analyse its own data comparing them with the content of a given database, both for population genetics and mitochondrial disease data. RESULTS: HmtDB is a well-integrated web-based human mitochondrial bioinformatic resource aimed at supporting population genetics and mitochondrial disease studies, thanks to a new approach based on site-specific nucleotide and aminoacid variability estimation. HmtDB consists of a database of Human Mitochondrial Genomes, annotated with population data, and a set of bioinformatic tools, able to produce site-specific variability data and to automatically characterize newly sequenced human mitochondrial genomes. A query system for the retrieval of genomes and a web submission tool for the annotation of new genomes have been designed and will soon be implemented. The first release contains 1255 fully annotated human mitochondrial genomes. Nucleotide site-specific variability data and multialigned genomes can be downloaded. Intra-human and inter-species aminoacid variability data estimated on the 13 coding for proteins genes of the 1255 human genomes and 60 mammalian species are also available. HmtDB is freely available, upon registration, at http://www.hmdb.uniba.it. CONCLUSION: The HmtDB project will contribute towards completing and/or refining haplogroup classification and revealing the real pathogenic potential of mitochondrial mutations, on the basis of variability estimation.

Computational Biology↗

PubMed2006

A high level interface to SCOP and ASTRAL implemented in python.

BACKGROUND: Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity. Taken together these two resources provide a 'ground truth' for assessing structural bioinformatics algorithms. We present a small and easy to use API written in python to enable construction of datasets from these resources. RESULTS: We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases. The modules are designed to work as part of the Biopython distribution. Python users can now manipulate and use the SCOP hierarchy from within python programs, and use ASTRAL to return sequences of domains in SCOP, as well as clustered representations of SCOP from ASTRAL. CONCLUSION: The modules make the analysis and generation of datasets for use in structural genomics easier and more principled.

Database Management Systems↗

PubMed2006

Agile methods in biomedical software development: a multi-site experience report.

BACKGROUND: Agile is an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments. We have successfully used agile development approaches to create and maintain biomedical software, including software for bioinformatics. This paper reports on a qualitative study of our experiences using these methods. RESULTS: We have found that agile methods are well suited to the exploratory and iterative nature of scientific inquiry. They provide a robust framework for reproducing scientific results and for developing clinical support systems. The agile development approach also provides a model for collaboration between software engineers and researchers. We present our experience using agile methodologies in projects at six different biomedical software development organizations. The organizations include academic, commercial and government development teams, and included both bioinformatics and clinical support applications. We found that agile practices were a match for the needs of our biomedical projects and contributed to the success of our organizations. CONCLUSION: We found that the agile development approach was a good fit for our organizations, and that these practices should be applicable and valuable to other biomedical software development efforts. Although we found differences in how agile methods were used, we were also able to identify a set of core practices that were common to all of the groups, and that could be a focus for others seeking to adopt these methods.

Algorithms↗

PubMed2006

PROMPT: a protein mapping and comparison tool.

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from http://webclu.bio.wzw.tum.de/prompt/.

Computational Biology↗

PubMed2006

EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.

BACKGROUND: Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms. RESULTS: We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is applied to the motif discovery problem for the first time. The algorithm is tested on a benchmark dataset generated from E. coli RegulonDB. The EMD algorithm has achieved 22.4% improvement in terms of the nucleotide level prediction accuracy over the best stand-alone component algorithm. The advantage of the EMD algorithm is more significant for shorter input sequences, but most importantly, it always outperforms or at least stays at the same performance level of the stand-alone component algorithms even for longer sequences. CONCLUSION: We proposed an ensemble approach for the motif discovery problem by taking advantage of the availability of a large number of motif discovery programs. We have shown that the ensemble approach is an effective strategy for improving both sensitivity and specificity, thus the accuracy of the prediction. The advantage of the EMD algorithm is its flexibility in the sense that a new powerful algorithm can be easily added to the system.

Algorithms↗

PubMed2006

SNP-PHAGE--High throughput SNP discovery pipeline.

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/. CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Base Sequence↗

PubMed2006

RiboSubstrates: a web application addressing the cleavage specificities of ribozymes in designated genomes.

BACKGROUND: RNA-dependent gene silencing is becoming a routine tool used in laboratories worldwide. One of the important remaining hurdles in the selection of the target sequence, if not the most important one, is the designing of tools that have minimal off-target effects (i.e. cleaves only the desired sequence). Increasingly, in the current dawn of the post-genomic era, there is a heavy reliance on tools that are suitable for high-throughput functional genomics, consequently more and more bioinformatic software is becoming available. However, to date none have been designed to satisfy the ever-increasing need for the accurate selection of targets for a specific silencing reagent. RESULTS: In order to overcome this hurdle we have developed RiboSubstrates http://www.riboclub.org/ribosubstrates. This integrated bioinformatic software permits the searching of a cDNA database for all potential substrates for a given ribozyme. This includes the mRNAs that perfectly match the specific requirements of a given ribozyme, as well those including Wobble base pairs and mismatches. The results generated allow rapid selection of sequences suitable as targets for RNA degradation. The current web-based RiboSubstrates version permits the identification of potential gene targets for both SOFA-HDV ribozymes and for hammerhead ribozymes. Moreover, a minimal template for the search of siRNAs is also available. This flexible and reliable tool is easily adaptable for use with any RNA tool (i.e. other ribozymes, deoxyribozymes and antisense), and may use the information present in any cDNA bank. CONCLUSION: RiboSubstrates should become an essential step for all, even including "non-RNA biologists", who endeavor to develop a gene-inactivation system.

Animals↗

PubMed2006

GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima.

BACKGROUND: Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sampling based technique for TFBS discovery is attempted through an approach that is widely known, but which has never been investigated before: reduction of the effect of local optima. RESULTS: To alleviate the vulnerability of Gibbs sampling to local optima trapping, we propose to combine a thermodynamic method, called simulated tempering, with Gibbs sampling. The resultant algorithm, GibbsST, is then validated using synthetic data and actual promoter sequences extracted from Saccharomyces cerevisiae. It is noteworthy that the marked improvement of the efficiency presented in this paper is attributable solely to the improvement of the search method. CONCLUSION: Simulated tempering is a powerful solution for local optima problems found in pattern discovery. Extended application of simulated tempering for various bioinformatic problems is promising as a robust solution against local optima problems.

Algorithms↗