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PubMed2005

A multiple-pattern biosequence analysis method for diverse source association mining.

BACKGROUND: In order to understand the intricacy of biomolecules more comprehensively, significant patterns extracted from related data collected from diverse sources must be integrated. These data sources may be local or distributed, possibly with different representation schemes. Often, related data from different sources correspond only with respect to some of their values. METHODS: In biological sequence analysis, a goal is to identify new, previously unknown, relevant patterns, to obtain additional insights into the biomolecule. This is known as a pattern discovery task, rather than a pattern matching task. In this research, we present a method to tackle this problem typically found in molecular sequence analysis when the alignment of the sequences is represented as a relation. In this article, we propose an information measure to select attribute values that reflect multiple patterns of significant interdependence information. Based on these selected values, the patterns are evaluated with data values from other sources. RESULTS: In the experiments, a cancer-suppressor gene known as TP53 (encoding tumour protein p53) is analysed with the mutation records of patients. The experiments identify previously unknown points in the molecule that have patterns negatively associated with the occurrence of cancer. CONCLUSION: Since the evaluated interdependence pattern is a global property of the molecule, we conjecture that the identified points might also be a reflection of the molecule's cancer-suppressor characteristics. The experiments also confirm the usefulness of the proposed method.

Amino Acid Sequence↗
PubMed2006

Towards a better solution to the shortest common supersequence problem: the deposition and reduction algorithm.

BACKGROUND: The problem of finding a Shortest Common Supersequence (SCS) of a set of sequences is an important problem with applications in many areas. It is a key problem in biological sequences analysis. The SCS problem is well-known to be NP-complete. Many heuristic algorithms have been proposed. Some heuristics work well on a few long sequences (as in sequence comparison applications); others work well on many short sequences (as in oligo-array synthesis). Unfortunately, most do not work well on large SCS instances where there are many, long sequences. RESULTS: In this paper, we present a Deposition and Reduction (DR) algorithm for solving large SCS instances of biological sequences. There are two processes in our DR algorithm: deposition process, and reduction process. The deposition process is responsible for generating a small set of common supersequences; and the reduction process shortens these common supersequences by removing some characters while preserving the common supersequence property. Our evaluation on simulated data and real DNA and protein sequences show that our algorithm consistently produces the best results compared to many well-known heuristic algorithms, and especially on large instances. CONCLUSION: Our DR algorithm provides a partial answer to the open problem of designing efficient heuristic algorithm for SCS problem on many long sequences. Our algorithm has a bounded approximation ratio. The algorithm is efficient, both in running time and space complexity and our evaluation shows that it is practical even for SCS problems on many long sequences.

Algorithms↗
PubMed1997

Application of genetic search in derivation of matrix models of peptide binding to MHC molecules.

T cells of the vertebrate immune system recognise peptides bound by major histocompatibility complex (MHC) molecules on the surface of host cells. Peptide binding to MHC molecules is necessary for immune recognition, but only a subset of peptides are capable of binding to a particular MHC molecule. Common amino acid patterns (binding motifs) have been observed in sets of peptides that bind to specific MHC molecules. Recently, matrix models for peptide/MHC interaction have been reported. These encode the rules of peptide/ MHC interactions for an individual MHC molecule as a 20 x 9 matrix where the contribution to binding of each amino acid at each position within a 9-mer peptide is quantified. The artificial intelligence techniques of genetic search and machine learning have proved to be very useful in the area of biological sequence analysis. The availability of peptide/MHC binding data can facilitate derivation of binding matrices using machine learning techniques. We performed a simulation study to determine the minimum number of peptide samples required to derive matrices, given the pre-defined accuracy of the matrix model. The matrices were derived using a genetic search. In addition, matrices for peptide binding to the human class I MHC molecules, HLA-B35 and -A24, were derived, validated by independent experimental data and compared to previously-reported matrices. The results indicate that at least 150 peptide samples are required to derive matrices of acceptable accuracy. This result is based on a maximum noise content of 5%, the availability of precise affinity measurements and that acceptable accuracy is determined by an area under the Relative Operating Characteristic curve (Aroc) of > 0.8. More than 600 peptide samples are required to derive matrices of excellent accuracy (Aroc > 0.9). Finally, we derived a human HLA-B27 binding matrix using a genetic search and 404 experimentally-tested peptides, and estimated its accuracy at Aroc > 0.88. The results of this study are expected to be of practical interest to immunologists for efficient identification of peptides as candidates for immunotherapy.

Amino Acid Sequence↗
PubMed2003

Minimal entropy probability paths between genome families.

We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non-rich vectors, does not involve variational theory and does not involve differential equations, but is a better approximation of the minimal entropy path distance than the distance //b-a//(2). We compute minimal entropy distance matrices for examples of DNA myostatin genes and amino-acid sequences across several species. Output tree dendograms for our minimal entropy metric are compared with dendograms based on BLAST and BLAST identity scores.

Algorithms↗
PubMed2000

DNAssist: the integrated editing and analysis of molecular biology sequences in windows.

MOTIVATION: The programs currently available for the analysis of nucleic acid and protein sequences suffer from a variety of problems: Web-based programs often require inconvenient reformatting of sequences when proceeding from one analysis to the next, and commercial-console-based programs are cost prohibitive. Here, we report the development of DNASSIST:, an inexpensive, multiple-document, interface program for the fully integrated editing and analysis of nucleic acid and protein sequences in the familiar environment of Microsoft Windows.

Animals↗
PubMed2025

Panaln: indexing pangenome for read alignment.

MOTIVATION: Pangenome indexing is a critical supporting technology in biological sequence analysis such as read alignment applications. The need to accurately identify billions of small sequencing fragments carrying sequencing errors and genomic variants drives the development of scalable and efficient pangenome indexing approach. RESULTS: We propose a new wavelet tree-based approach, called Panaln, for indexing pangenome and introduce a batch computation approach for fast count query over Panaln. We present a simple and effective seeding strategy and develop a pangenome program that uses the seed-and-extend paradigm for read alignment. Experimental results on simulated and real data demonstrate that Panaln uses significantly less space for the compared pangenome methods with generally higher accuracy. We provide a scalable index construction by representing pangenome with a linear model. Additionally, Panaln brings enhanced accuracy compared to the popular single reference methods. AVAILABILITY AND IMPLEMENTATION: Package: https://anaconda.org/bioconda/panaln and source code: https://github.com/Lilu-guo/Panaln.

Software↗
PubMed2005

Using evolutionary Expectation Maximization to estimate indel rates.

MOTIVATION: The Expectation Maximization (EM) algorithm, in the form of the Baum-Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Statistical Alignment). Recently, we showed how to do this for the point substitution component of the evolutionary process; here, we extend these results to the indel process. RESULTS: We present an algorithm for maximum-likelihood estimation of insertion and deletion rates from multiple sequence alignments, using EM, under the single-residue indel model owing to Thorne, Kishino and Felsenstein (the 'TKF91' model). The algorithm converges extremely rapidly, gives accurate results on simulated data that are an improvement over parsimonious estimates (which are shown to underestimate the true indel rate), and gives plausible results on experimental data (coronavirus envelope domains). Owing to the algorithm's close similarity to the Baum-Welch algorithm for training hidden Markov models, it can be used in an 'unsupervised' fashion to estimate rates for unaligned sequences, or estimate several sets of rates for sequences with heterogenous rates. AVAILABILITY: Software implementing the algorithm and the benchmark is available under GPL from http://www.biowiki.org/

Algorithms↗
PubMed2005

Integrating protein annotation resources through the Distributed Annotation System.

Using the Distributed Annotation System (DAS) we have created a protein annotation resource available at our web page: http://www.cbs.dtu.dk, as a part of the BioSapiens Network of Excellence EU FP6 project. The DAS protocol allows us to gather layers of annotation data for a given sequence and thereby gain an overview of the sequence's features. A user-friendly graphical client has also been developed (http://www.cbs.dtu.dk/cgi-bin/das), which demonstrates the possibility of integrating DAS annotation data from multiple sources into a simple graphical view. The client displays protein feature annotations from the Center for Biological Sequence Analysis as well as from the BioSapiens reference UniProt server (http://www.ebi.ac.uk/das-srv/uniprot/das) at the European Bioinformatics Institute. Other DAS data sources for protein annotation will be added as they become available.

Computer Graphics↗
PubMed2002

Extranodal marginal zone B cell lymphoma of MALT type involving the mucosa of both the urinary bladder and stomach.

Primary lymphoma of the urinary bladder is a very rare tumour. A bladder tumour was found in a 57 year old man with obstructive dysuria. It was found by histological and immunohistohistochemical investigation to be an extranodal marginal zone B cell lymphoma. Lymphoepithelial lesions were absent, but were found in a clinically silent gastric lymphoma discovered four weeks later during staging investigations; this gastric lymphoma was negative for Helicobacter pylori by breath test and molecular biological analysis. Sequencing of the clonal immunoglobulin heavy chain gene in both tumours indicated the same precursor cell, of follicular or post follicular origin. In synopsis, the data suggested that this was a case of primary lymphoma of the bladder with involvement of the stomach. The application of a chromosome 3 specific alpha satellite probe revealed trisomy 3. A tumour with these characteristics arising as a lymphoma of the bladder with a metachronous involvement of the gastric mucosa has not been described previously.

Humans↗
PubMed2006

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

BACKGROUND: The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. RESULTS: In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. CONCLUSION: A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Databases, Protein↗
PubMed2006

Fast index based algorithms and software for matching position specific scoring matrices.

BACKGROUND: In biological sequence analysis, position specific scoring matrices (PSSMs) are widely used to represent sequence motifs in nucleotide as well as amino acid sequences. Searching with PSSMs in complete genomes or large sequence databases is a common, but computationally expensive task. RESULTS: We present a new non-heuristic algorithm, called ESAsearch, to efficiently find matches of PSSMs in large databases. Our approach preprocesses the search space, e.g., a complete genome or a set of protein sequences, and builds an enhanced suffix array that is stored on file. This allows the searching of a database with a PSSM in sublinear expected time. Since ESAsearch benefits from small alphabets, we present a variant operating on sequences recoded according to a reduced alphabet. We also address the problem of non-comparable PSSM-scores by developing a method which allows the efficient computation of a matrix similarity threshold for a PSSM, given an E-value or a p-value. Our method is based on dynamic programming and, in contrast to other methods, it employs lazy evaluation of the dynamic programming matrix. We evaluated algorithm ESAsearch with nucleotide PSSMs and with amino acid PSSMs. Compared to the best previous methods, ESAsearch shows speedups of a factor between 17 and 275 for nucleotide PSSMs, and speedups up to factor 1.8 for amino acid PSSMs. Comparisons with the most widely used programs even show speedups by a factor of at least 3.8. Alphabet reduction yields an additional speedup factor of 2 on amino acid sequences compared to results achieved with the 20 symbol standard alphabet. The lazy evaluation method is also much faster than previous methods, with speedups of a factor between 3 and 330. CONCLUSION: Our analysis of ESAsearch reveals sublinear runtime in the expected case, and linear runtime in the worst case for sequences not shorter than the absolute value of A(m) + m - 1, where m is the length of the PSSM and A a finite alphabet. In practice, ESAsearch shows superior performance over the most widely used programs, especially for DNA sequences. The new algorithm for accurate on-the-fly calculations of thresholds has the potential to replace formerly used approximation approaches. Beyond the algorithmic contributions, we provide a robust, well documented, and easy to use software package, implementing the ideas and algorithms presented in this manuscript.

Algorithms↗
PubMed1999

A linear time algorithm for finding all maximal scoring subsequences.

Given a sequence of real numbers ("scores"), we present a practical linear time algorithm to find those nonoverlapping, contiguous subsequences having greatest total scores. This improves on the best previously known algorithm, which requires quadratic time in the worst case. The problem arises in biological sequence analysis, where the high-scoring subsequences correspond to regions of unusual composition in a nucleic acid or protein sequence. For instance, Altschul, Karlin, and others have used this approach to identify transmembrane regions, DNA binding domains, and regions of high charge in proteins.

Algorithms↗
PubMed2006

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications.

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene-patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene-patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome.org/; the information is updated bimonthly.

Amino Acid Sequence↗
PubMed1996

Biological testing versus sequence analysis for determination of drug resistance of human immunodeficiency virus.

Resistance of HIV-1 to 3'-azido-3'-deoxythymidine (AZT) is associated with one or more of five mutations in the reverse transcriptase (RT) segment of the polymerase gene ("AZT-specific mutations"). Therefore, sequence analysis of the proviral DNA, derived directly from the blood, is considered to replace the biological test. Additional arguments are non-cultivatable viral strains, the universality of the sequence analysis in combination therapy, and the suspicion that the cultivated virus does not represent the predominant viral variant in the blood. In this investigation, 21 strains of human immunodeficiency virus type 1 (HIV-1) were comparatively analysed by molecular and biological testing. For 12 strains, the homology of the RT gene segment between the predominant provirus in the blood and the cultivated virus was ascertained (99.72% homology). 11 strains from untreated patients or patients treated no longer than 5 months were free from AZT-specific mutations and proved to be sensitive. 10 strains from patients treated for 17 to 57 months displayed 2-4 AZT-specific mutations. However, it was not possible to correlate the degree of sensitivity to the number or the pattern of the mutations. Suppression of AZT resistance by strain-specific sequences in other parts of the gene are discussed as the reason for that discrepancy. Remarkably, the productivity of resistant virus strains could be drastically enhanced by non-inhibiting concentrations of the drug.

DNA, Viral↗
PubMed1995

A statistical analytical approach to decipher information from biological sequences: application to murine splice-site analysis and prediction.

A simple statistical approach for the analysis of biological sequences, such as splice-sites, promoter regions, helices and extended structure forming regions or any other sequence dependent functional entities in proteins, is presented. The approach has been proved useful to develop a method for prediction of such entities in newly available sequences. We first search for invariant sequence features of each functional entity from the experimentally available sequences and identify a set of 'like' sequences with similar sequence features. In the next step, concrete features of sequence entities in terms of occurrences of smaller subsequences are identified at various positions which are used as a knowledge base to select potential functional entities from the identified 'like' sequences. The third step consists of refinement of this pattern learning, statistical improvements of the knowledge base weight matrices, and finally its application to predict functional entities in newly available sequences. Such an analysis is operationally described for murine splice-site predictions. Regions comprising -30 to +30 nucleotides from the splice-junction at the murine splice-sites (donors and acceptors), reported earlier, were analyzed. Invariant sequence-specific features in terms of monomer frequency average were used to identify splice-site-like sequences in the EMBL murine DNA sequence data base. The frequencies of occurrence of mono-, di-, tri- and tetranucleotides in the known splice-sites were studied in comparison with the splice-site-like sequences; the significant differences in their occurrences were extracted as statistical knowledge coded in weight matrices for computer to identify potential splice-sites. The algorithm was refined and a method was developed to predict potential splice-sites in a given murine DNA; the analysis was also extended to human DNA. The success rate of the method to predict correct splice-sites in these species is found to be 80% and 85%, respectively. The major strength of this method lies in reducing significantly the number of false positives which are normally picked up in such analysis.

Algorithms↗
PubMed1996

Characterization of Newcastle disease virus vaccines by biological properties and sequence analysis of the hemagglutinin-neuraminidase protein gene.

Six commercially available monovalent Newcastle disease virus (NDV) live-vaccines were examined for their biological and genomic stability in comparison to their stated parent virus. Thermostability of the hemagglutinin at 56 degrees C for 5 min was consistently observed among the majority of the vaccine viruses. One exception was a recently developed NDV vaccine isolated from turkeys that had a thermostability of 15 min. Neuraminidase activity, as measured by elution rate of agglutinated red blood cells, varied among vaccine viruses and correlated with that of the parent isolate. Virulence as measured by intracerebral pathogenicity index ranged from 0 to 0.39 among NDV vaccine-type viruses, well within the range of avirulent lentogens. Sequence of the fusion protein cleavage site from all the NDV vaccine isolates examined was consistent with that for lentogens. The entire hemagglutinin-neuraminidase gene sequence was 98% similar among all the NDV vaccine viruses examined and phylogenetic classification of commercial vaccine types correlated with their respective parent virus. Consequently, the commercially produced NDV vaccines reported here appear relatively stable when mass produced in avian embryonated eggs.

Amino Acid Sequence↗
PubMed2003

Atlantic salmon interferon genes: cloning, sequence analysis, expression, and biological activity.

In this work, we report cDNA cloning of two type I interferons (IFNs) from the head kidney of Atlantic salmon, called SasaIFN-alpha1 (829 bp) and SasaIFN-alpha2 (1290 bp). Both translate into 175 amino acid precursor molecules showing 95% amino acid sequence identity. The precursors have a putative 23 amino acid signal peptide, which suggests that the mature Atlantic salmon IFNs contain 152 amino acids (18.2 kDa). Salmon IFN appears to have five alpha-helices, similar to mammalian and avian type I IFNs, and showed 45% sequence identity with zebrafish IFN, up to 29% identity with mammalian IFN-alpha sequences, and 17%-18% sequence identity with mammalian IFN-beta and chicken type I IFNs. Human embryonic kidney 293 cells transfected with the SasaIFN-alpha1 cDNA gene produced high titers of acid-stable antiviral activity, which protected salmonid cells against infectious pancreatic necrosis virus (IPNV) and also induced Mx protein in the cells. Poly(I)-poly(C) induced two IFN transcripts in head kidney of Atlantic salmon. Genomic IFN sequences contained four introns and five exons, which is different from the intronless type I IFN genes of birds and mammals.

Amino Acid Sequence↗
PubMed2006

Functional annotation and analysis of Korean patented biological sequences using bioinformatics.

A recent report of the Korean Intellectual Property Office (KIPO) showed that the number of biological sequence-based patents is rapidly increasing in Korea. We present biological features of Korean patented sequences though bioinformatic analysis. The analysis is divided into two steps. The first is an annotation step in which the patented sequences were annotated with the Reference Sequence (RefSeq) database. The second is an association step in which the patented sequences were linked to genes, diseases, pathway, and biological functions. We used Entrez Gene, Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) databases. Through the association analysis, we found that nearly 2.6% of human genes were associated with Korean patenting, compared to 20% of human genes in the U.S. patent. The association between the biological functions and the patented sequences indicated that genes whose products act as hormones on defense responses in the extra-cellular environments were the most highly targeted for patenting. The analysis data are available at http://www.patome.net.

Base Sequence↗