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At least 19 recordsLinked to original sources
PubMed2004

Training HMM structure with genetic algorithm for biological sequence analysis.

SUMMARY: Hidden Markov models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimizing the structure of HMMs would be highly desirable. However, this raises two important issues; first, the new HMMs should be biologically interpretable, and second, we need to control the complexity of the HMM so that it has good generalization performance on unseen sequences. In this paper, we explore the possibility of using a genetic algorithm (GA) for optimizing the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum-Welch training within their evolutionary cycle. Furthermore, operators that alter the structure of HMMs can be designed to favour interpretable and simple structures. In this paper, a training strategy using GAs is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium Campylobacter jejuni. The proposed GA for hidden Markov models (GA-HMM) allows, HMMs with different numbers of states to evolve. To prevent over-fitting, a separate dataset is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has been published previously.

Algorithms↗
PubMedDate not supplied

The applicability of recurrent neural networks for biological sequence analysis.

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Algorithms↗
PubMed2000

PepTool and GeneTool: platform-independent tools for biological sequence analysis.

Although we are unable to discuss all of the functionality available in PepTool and GeneTool, it should be evident from this brief review that both packages offer a great deal in terms of functionality and ease-of-use. Furthermore, a number of useful innovations including platform-independent GUI design, networked parallelism, direct internet connectivity, database compression, and a variety of enhanced or improved algorithms should make these two programs particularly useful in the rapidly changing world of biological sequence analysis. More complete descriptions of the programs, algorithms and operation of PepTool and GeneTool are available on the BioTools web site (www.biotools.com), in the associated program user manuals and in the on-line Help pages.

Amino Acid Sequence↗
PubMed2004

Parametric inference for biological sequence analysis.

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Algorithms↗
PubMed2006

Statistical significance in biological sequence analysis.

One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.

Computational Biology↗
PubMed2004

Biological sequence analysis through the one-dimensional percolation transform and its enhanced version.

MOTIVATION: The necessity to characterize the spatial uniformity (or lack of it) of symbols in biological sequences, given its implications for identification of the properties of the structures associated with the sequences. METHODS: A one-dimensional version of a recently introduced percolation-based approach is presented, which allows the accurate quantification of symbol distributions even in the presence of co-existing densities. An enhanced version of this methodology, which uses an agglomerative process to organize hierarchically the sequence into subsequences, is also proposed and illustrated. 3. RESULTS: The potential of the proposed methodology is illustrated with respect to synthetic and real data (1881 zebrafish and 1200 Xenopus proteins) and compared to two alternative multiscale methodologies, with encouraging results including the possibility to identify particularly remarkable amino acid arrangements in proteins. 4. CONTACT: luciano@if.sc.usp.br.

Algorithms↗
PubMed1993

Inductive learning and biological sequence analysis. The PLAGE program.

Inductive learning, also called 'learning from examples', is a subfield of artificial intelligence. Inductive learning methods are able to deal with 'structural descriptions'. These portray objects as composite structures consisting of various components. The use of structural descriptions to represent biological objects is appealing. For instance, they have been used by Rawlings et al [1] for symbolically and comprehensively representing the folding of proteins. This paper shows how inductive learning techniques may be used for extracting information from biological objects. We briefly describe some general techniques for describing objects in a structural way and for learning from these descriptions. We present details of a program that we developed, PLAGE, and show the application of this program for a study on signal peptides, which was done in collaboration with A Danchin [2,3]. Finally, we survey some other approaches and applications of inductive learning to molecular biology.

Algorithms↗
PubMed1995

Pruning of a large network by optimal brain damage and surgeon: an example from biological sequence analysis.

Optimal Brain Damage (OBD) and Optimal Brain Surgeon (OBS) represent two popular pruning procedures; however, pruning large networks trained on voluminous data sets using these methods easily becomes intractable. We present a number of approximations and discuss practical issues in real-world pruning, and use as an example a network trained to predict protein coding regions in DNA sequences. The efficiency of OBS on large networks is compared to OBD, and it turns out that OBD is preferable to OBS, since more weights can be removed using less computational effort.

Base Sequence↗
PubMed2000

Sequence complexity for biological sequence analysis.

A new statistical model for DNA considers a sequence to be a mixture of regions with little structure and regions that are approximate repeats of other subsequences, i.e. instances of repeats do not need to match each other exactly. Both forward- and reverse-complementary repeats are allowed. The model has a small number of parameters which are fitted to the data. In general there are many explanations for a given sequence and how to compute the total probability of the data given the model is shown. Computer algorithms are described for these tasks. The model can be used to compute the information content of a sequence, either in total or base by base. This amounts to looking at sequences from a data-compression point of view and it is argued that this is a good way to tackle intelligent sequence analysis in general.

Algorithms↗
PubMed2002

Phylogenetic similarity of the canine parvovirus wild-type isolates on the basis of VP1/VP2 gene fragment sequence analysis.

Biological material was taken from dogs with diarrhoea. Faecal samples were taken from within live animals and intestinal tract fragments (i.e. small intestine, and stomach) were taken from dead animals. In total, 18 specimens were investigated from dogs housed alone or in large groups. To test for the presence of the virus, latex (On Site Biotech, Uppsala, Sweden) and direct immunofluorescence tests were performed. At the same time, polymerase chain reaction (PCR) with primers complementary to a conservative region of VP1/VP2 was carried out. The products of amplification were analysed on 2% agarose gel. The purified products were cloned with the Template Generation System (Finnzymes, Espoo, Finland) using a transposition reaction and positive clones were searched using the 'colony screening by PCR' method. The sequencing gave 12 sequences of VP1/VP2 gene fragments that were of high similarity. Among the 12 analysed sequences, six exhibited 88% similarity, four exhibited 100% similarity and two exhibited 71% similarity.

Animals↗
PubMed1984

BIOLOG - a DNA sequence analysis system in PROLOG.

BIOLOG contains facilities for the analysis of nucleic acid sequences. These facilities are available through queries and commands of the underlying implementation language PROLOG. Familiarity with PROLOG is gained by using the built-in BIOLOG functions. This experience should enable the user to extend the current system and define new facilities.

Amino Acid Sequence↗
PubMed2002

Conservative region of the genes encoding four adhesins of Helicobacter pylori: cloning, sequence analysis and biological information analysis.

OBJECTIVE: To clone the conserved regions of the genes encoding the 4 adhesins (BabA, AlpA, AlpB and HopZ) of Helicobacter pylori (H. pylori) and analyze their sequences and biological information, thus facilitating further research in the molecular mechanism and immunogenicity of H. pylori adhesins. METHODS: Common conserved region (designated as CB) was identified from the confirmed sequences (by ANTHEPROT V4.3c software package) of the 4 adhesin proteins. Their DNA sequences were deduced, according to which primers specific to CB were designed for subsequent PCR, and the products were inserted directionally into pET-22b(+) vector to construct recombinant clones of the conserved region. The DNA sequences were determined with the basic local alignment sequence tool (BLAST) and the biological properties analyzed with ANTHEPROT V4.3c software package. RESULTS: The recombinant plasmid containing the CB sequence was constructed. DNA sequencing showed an open reading frame of 588 bp in length, encoding 195 amino acids. The homogencity of conservative region of the 4 adhesion genes was above 50%. The corresponding protein possessed a relative molecular mass (Mr) of 22 500 as predicted by ANTHEPROT V4.3c software prediction, with excellent antigenicity and hydrophobicity. There were 836 767 sequences analyzed with BLAST, in which those with homogencity of 40% with the identified CB sequence were categorized into H. pylori sequences. CONCLUSION: There are conservative regions in the 4 adhesin genes with similar homogencity, suggesting similar molecular basis for adhesion of the adhesins. Biological information analysis indicates that CB has excellent immunogenicity and strict species specificity.

Adhesins, Bacterial↗
PubMed2001

Advances in sequence analysis.

In its early days, the entire field of computational biology revolved almost entirely around biological sequence analysis. Over the past few years, however, a number of new non-sequence-based areas of investigation have become mainstream, from the analysis of gene expression data from microarrays, to whole-genome association discovery, and to the reverse engineering of gene regulatory pathways. Nonetheless, with the completion of private and public efforts to map the human genome, as well as those of other organisms, sequence data continue to be a veritable mother lode of valuable biological information that can be mined in a variety of contexts. Furthermore, the integration of sequence data with a variety of alternative information is providing valuable and fundamentally new insight into biological processes, as well as an array of new computational methodologies for the analysis of biological data.

Computational Biology↗
PubMed1986

On computer-assisted analysis of biological sequences: proline punctuation, consensus sequences, and apolipoprotein repeats.

During the past several years, the use of computer programs in the analysis of protein and DNA sequences has become commonplace. In all but the simplest procedures, the ability to critically review the results obtained with computer methods requires a basic knowledge of the algorithms employed (and the assumptions upon which they are based), an awareness of the capabilities and limitations of the particular program that implements an algorithm, and some familiarity with probability and statistics. We describe a number of computer methods that have been applied to the analysis of apolipoprotein sequences. We discuss the suitability of these methods for particular problems, how the choice of initial "parameters" can affect the results, and what the results can tell us about protein or gene sequences. We also identify some outstanding problems of apolipoprotein sequence analysis where further work is needed.

Amino Acid Sequence↗
PubMed1984

Analysis of biological sequences on small computers.

We review a wide range of programs for micro- and minicomputers that facilitate the collection and analysis of DNA, RNA, or protein sequences. Special-purpose programs that perform a single function and general-purpose programs that perform a set of functions are both considered. The information presented should be useful to any biologist who wishes to obtain a computer program to aid in sequence investigations.

Amino Acid Sequence↗
PubMed2000

V3 sequence analysis and biological characterization of HIV-1 isolates from asymptomatic and early symptomatic Tanzanian individuals.

The aim of this study was to determine HIV-1 V3 sequences, in vitro biological characteristics and co-receptor usage of virus isolates from Tanzania. Virus was isolated from 14 of 17 samples investigated. Four of the isolates induced syncytia in MT-2 cells and used the CXCR4 co-receptor, while the remaining 10 isolates used the CCR5 co-receptor characteristic of non-MT-2 tropic viruses. One of the four MT-2 tropic isolates also used the CCR5 and CCR3 co-receptors. Proviral DNA was detected in all 14 isolates and PCR products were subjected to DNA sequencing. Unambiguous V3 amino acid sequences were obtained from 11 amplificates. Phylogenetic analysis indicated that these sequences were divergent and clustered in HIV-1 subtypes A, C or D. Sequences from the viruses that induced syncytia in MT-2 cells presented characteristic V3 phenotype-associated amino acids. Results of co-receptor analysis are in concordance with the isolate phenotype as determined by replication and induction of syncytia in MT-2 cells. The considerable diversity illustrated by a limited number of isolates from Tanzania is in accordance with reports from other regions of Africa.

Adolescent↗