Biosequence Analysis Research Articles

A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics. Numerous solving algorithms are used for these problems, and complex similarities and differences exist among these algorithms for the same problem, causing difficulty for researchers to select the appropriate one. To address this situation, combined with the formal partition-and-recur method, component technology, domain engineering, and generic programming, the paper presents a method for the development of a family of biological sequence analysis algorithms. It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms. The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability, understandability, and development efficiency of particular algorithms.

A Time Efficient Approach for Quick Splitting and Finding Maximum Matches Sequences in Bio-sequence Analysis

A Time Efficient Approach for Quick Splitting and Finding Maximum Matches Sequences in Bio-sequence Analysis

A Simple Linear Space Algorithm for Computing Nonoverlapping Inversion and Transposition Distance in Quadratic Average Time.

In the sequence alignment problem, it is important to compare DNA sequences to retrieve relevant information and align these sequences. An inversion and a translocation are important operations in comparing DNA sequences in biosequence analysis. The alignment problem with nonoverlapping inversions and translocations is to find an alignment with nonoverlapping inversions and translocations for the given two strings X and Y. This problem has interesting application for finding a common sequence from two mutated sequences. A linear space and quadratic average time algorithm to compute the mutation distance between two strings of the same length under nonoverlapping inversions and transpositions is presented in this article. The recursive formula for this purpose is novel to the best of our knowledge. The space costs of the algorithms to solve the same problem are typically quadratic, and thus, our original algorithm is the first linear space algorithm to solve the mutation distance problem.

Integrating Pareto Optimization into Dynamic Programming

Pareto optimization combines independent objectives by computing the Pareto front of the search space, yielding a set of optima where none scores better on all objectives than any other. Recently, it was shown that Pareto optimization seamlessly integrates with algebraic dynamic programming: when scoring schemes A and B can correctly evaluate the search space via dynamic programming, then so can Pareto optimization with respect to A and B. However, the integration of Pareto optimization into dynamic programming opens a wide range of algorithmic alternatives, which we study in substantial detail in this article, using real-world applications in biosequence analysis, a field where dynamic programming is ubiquitous. Our results are two-fold: (1) We introduce the operation of a “Pareto algebra product” in the dynamic programming framework of Bellman’s GAP. Users of this framework can now ask for Pareto optimization with a single keystroke. Careful evaluation of the implementation alternatives by means of an extended Bellman’s GAP compiler demonstrates the dependence of the best implementation choice on the application at hand. (2) We extract from our experiments several pieces of advice to programmers who do not use a system such as Bellman’s GAP, but who choose to hand-craft their dynamic programming recurrences, incorporating Pareto optimization from scratch.

Alignment with non-overlapping inversions and translocations on two strings

An inversion and a translocation are important in bio sequence analysis and motivate researchers to consider the sequence alignment problem using these operations. Based on inversion and translocation, we introduce a new alignment problem with non-overlapping inversions and translocations—given two strings x and y, find an alignment with non-overlapping inversions and translocations for x and y. This problem has interesting application for finding a common sequence from two mutated sequences. We, in particular, consider the alignment problem when non-overlapping inversions and translocations are allowed for both x and y. We design an efficient algorithm that determines the existence of such an alignment and retrieves an alignment, if exists.

Modeling Dynamic Programming Problems over Sequences and Trees with Inverse Coupled Rewrite Systems

Dynamic programming is a classical algorithmic paradigm, which often allows the evaluation of a search space of exponential size in polynomial time. Recursive problem decomposition, tabulation of intermediate results for re-use, and Bellman’s Principle of Optimality are its well-understood ingredients. However, algorithms often lack abstraction and are difficult to implement, tedious to debug, and delicate to modify. The present article proposes a generic framework for specifying dynamic programming problems. This framework can handle all kinds of sequential inputs, as well as tree-structured data. Biosequence analysis, document processing, molecular structure analysis, comparison of objects assembled in a hierarchic fashion, and generally, all domains come under consideration where strings and ordered, rooted trees serve as natural data representations. The new approach introduces inverse coupled rewrite systems. They describe the solutions of combinatorial optimization problems as the inverse image of a term rewrite relation that reduces problem solutions to problem inputs. This specification leads to concise yet translucent specifications of dynamic programming algorithms. Their actual implementation may be challenging, but eventually, as we hope, it can be produced automatically. The present article demonstrates the scope of this new approach by describing a diverse set of dynamic programming problems which arise in the domain of computational biology, with examples in biosequence and molecular structure analysis.

A Hardware Viewpoint on Biosequence Analysis

Biosequence alignment recently received an increasing support from both commodity and dedicated hardware platforms. Processing capabilities are constantly rising, but still not satisfying the limitless requirements of this application. We give an insight on the contribution to this need that can possibly be expected from emerging technology devices and architectures, focusing as an example on nanofabrics based on silicon nanowires. By varying a few parameters we explore the solution space, and demonstrate with proper figures of merit how this family of beyond CMOS structures could be considered as the effective disruptive technology for biosequence analysis applications.

Efficient tabling of structured data with enhanced hash-consing

AbstractCurrent tabling systems suffer from an increase in space complexity, time complexity or both when dealing with sequences due to the use of data structures for tabled subgoals and answers and the need to copy terms into and from the table area. This symptom can be seen in not only B-Prolog, which uses hash tables, but also systems that use tries such as XSB and YAP. In this paper, we apply hash-consing to tabling structured data in B-Prolog. While hash-consing can reduce the space consumption when sharing is effective, it does not change the time complexity. We enhance hash-consing with two techniques, called input sharing and hash code memoization, for reducing the time complexity by avoiding computing hash codes for certain terms. The improved system is able to eliminate the extra linear factor in the old system for processing sequences, thus significantly enhancing the scalability of applications such as language parsing and bio-sequence analysis applications. We confirm this improvement with experimental results.

Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications

BackgroundDiscriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied.ResultsWe introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) as an extension of Hidden Conditional Random Fields (HCRFs). GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows regular grammar rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins.ConclusionWe show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications.AvailabilityGRHCRF software is available under GPLv3 licence at the website

Normalized Lempel-Ziv complexity and its application in bio-sequence analysis

In this article, we propose a new method to measure DNA similarity based on a normalized Lempel-Ziv complexity scheme. The new method can weaken the effect of sequence length on complexity measurement and save computation time. Firstly, a DNA sequence is transformed into three (0,1)-sequences based on a scheme, which considers “A” and “non-A” , “G” and “non-G”, “C” and “non-C” bases respectively. Then, the normalized Lempel-Ziv complexity of the three (0,1)-sequences constitute a 3D vector. Finally, by the 3D vector, one may characterize DNA sequences and compute similarity matrix for them. The examination of similarities of two sets of DNA sequences illustrates the utility of the method in local and global similarity analysis.

Incremental discovery of the irredundant motif bases for all suffixes of a string in [formula omitted] time

Compact bases formed by motifs called “irredundant” and capable of generating all other motifs in a sequence have been proposed in recent years and successfully tested in tasks of biosequence analysis and classification. Given a sequence s of n characters drawn from an alphabet Σ , the problem of extracting such a base from s had been previously solved in time O ( n 2 log n log ∣ Σ ∣ ) and O ( ∣ Σ ∣ n 2 log 2 n log log n ) , respectively, using the FFT-based string searching by Fischer and Paterson. More recently, a solution on binary strings taking time O ( n 2 ) without resorting to the FFT was also proposed. In the present paper, we considered the problem of incrementally extracting the bases of all suffixes of a string. This problem was solved in a previous work in time O ( n 3 ) . A much faster incremental algorithm is described here, which takes time O ( n 2 log n ) for binary strings. Although this algorithm does not make use of the FFT, its performance is comparable to the one exhibited by the previous FFT-based algorithms involving the computation of only one base. The implicit representation of a single base requires O ( n ) space, whence for finite alphabets the proposed solution is within a log n factor from optimality.

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics

BackgroundA large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/ouput methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications.ResultsWe present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus.ConclusionImplementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website .

Beyond Mfold: Recent advances in RNA bioinformatics

Computational analysis of RNA secondary structure is a classical field of biosequence analysis, which has recently gained momentum due to the manyfold regulatory functions of RNA that have become apparent. We present five recent computational approaches that address the problems of synoptic folding space analysis, pseudoknot prediction, structure alignment, comparative structure prediction, and miRNA target prediction. All these programs are in current use and are available via the Bielefeld Bioinformatics Server at http://bibiserv.techfak.uni-bielefeld.de.

Efficient q-Gram Filters for Finding All ε-Matches over a Given Length

Fast and exact comparison of large genomic sequences remains a challenging task in biosequence analysis. We consider the problem of finding all epsilon-matches between two sequences, i.e., all local alignments over a given length with an error rate of at most epsilon. We study this problem theoretically, giving an efficient q-gram filter for solving it. Two applications of the filter are also discussed, in particular genomic sequence assembly and BLAST-like sequence comparison. Our results show that the method is 25 times faster than BLAST, while not being heuristic.

Effective ambiguity checking in biosequence analysis

BackgroundAmbiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity. As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking.ResultsWe explain frequently observed sources of ambiguity, and show how to avoid them. We suggest four testing procedures that may help to detect ambiguity when present, including a just-in-time test that permits to work safely with a potentially ambiguous grammar. We introduce, for the special case of stochastic context free grammars and RNA structure modeling, an automated partial procedure for proving non-ambiguity. It is used to demonstrate non-ambiguity for several relevant grammars.ConclusionOur mechanical proof procedure and our testing methods provide a powerful arsenal of methods to ensure non-ambiguity.

A discipline of dynamic programming over sequence data

Dynamic programming is a classical programming technique, applicable in a wide variety of domains such as stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing of ambiguous languages, and biosequence analysis. Little methodology has hitherto been available to guide the design of such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and, in nontrivial applications, almost impossible to debug completely. This article introduces a discipline designed to alleviate this problem. We describe an algebraic style of dynamic programming over sequence data. We define its formal framework, based on a combination of grammars and algebras, and including a formalization of Bellman's Principle. We suggest a language used for algorithm design on a convenient level of abstraction. We outline three ways of implementing this language, including an embedding in a lazy functional language. The workings of the new method are illustrated by a series of examples drawn from diverse areas of computer science.

A discipline of dynamic programming over sequence data

Dynamic programming is a classical programming technique, applicable in a wide variety of domains such as stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing of ambiguous languages, and biosequence analysis. Little methodology has hitherto been available to guide the design of such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and, in nontrivial applications, almost impossible to debug completely. This article introduces a discipline designed to alleviate this problem. We describe an algebraic style of dynamic programming over sequence data. We define its formal framework, based on a combination of grammars and algebras, and including a formalization of Bellman's Principle. We suggest a language used for algorithm design on a convenient level of abstraction. We outline three ways of implementing this language, including an embedding in a lazy functional language. The workings of the new method are illustrated by a series of examples drawn from diverse areas of computer science.

A systematic approach to dynamic programming in bioinformatics.

Dynamic programming is probably the most popular programming method in bioinformatics. Sequence comparison, gene recognition, RNA structure prediction and hundreds of other problems are solved by ever new variants of dynamic programming. Currently, the development of a successful dynamic programming algorithm is a matter of experience, talent and luck. The typical matrix recurrence relations that make up a dynamic programming algorithm are intricate to construct, and difficult to implement reliably. No general problem independent guidance is available. This article introduces a systematic method for constructing dynamic programming solutions to problems in biosequence analysis. By a conceptual splitting of the algorithm into a recognition and an evaluation phase, algorithm development is simplified considerably, and correct recurrences can be derived systematically. Without additional effort, the method produces an early, executable prototype expressed in a functional programming language. The method is quite generally applicable, and, while programming effort decreases, no overhead in terms of ultimate program efficiency is incurred.

A Discriminative Framework for Detecting Remote Protein Homologies

A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a generative statistical model for a protein family, in this case a hidden Markov model. This general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.

Information resources for the bioinformatician.

The collaborative computing project in biosequence and structure analysis (CCPII) was established to foster bioinformatics in the broad community and the UK research community in particular. A World-Wide Web site called 'The Bioinformatics Resource' has been created containing a comprehensive set of information resources of use to the bioinformatician. The activities of CCPII are complementary to other providers of molecular biology information such as the BIOSCI electronic communication forum, which was established to facilitate communication between professionals in the biological sciences.