EDUCATIONAL PEARL: Biological sequence similarity

2005 ◽  
Vol 16 (1) ◽  
pp. 1-12
Author(s):  
DAVID WAKELING
Keyword(s):  
Functional Programming ◽  
Sequence Similarity ◽  
Functional Languages ◽  
Biological Sequence ◽  
What Matters

Functional languages provide an excellent framework for formulating biological algorithms in a naive form and then transforming them into an efficient form. This helps biologists understand what matters about programming and brings functional programming into the realm of the practical. In this column, we present an example from our MSc course on bioinformatics and report on our experiences teaching functional programming in this context.

A New Approach for DNA Sequence Similarity Analysis based on Triplets of Nucleic Acid Bases

2010 ◽  
Vol 2 (4) ◽  
pp. 1-11
Author(s):  
Dan Wei ◽  
Qingshan Jiang ◽  
Sheng Li
Keyword(s):  
Nucleic Acid ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Sequence Similarity ◽  
Similarity Analysis ◽  
Biological Sequence ◽  
Nucleic Acid Bases ◽  
New Approach ◽  
Characteristic Distribution ◽  
Sequence Similarity Analysis

Similarity analysis of DNA sequences is a fundamental research area in Bioinformatics. The characteristic distribution of L-tuple, which is the tuple of length L, reflects the valuable information contained in a biological sequence and thus may be used in DNA sequence similarity analysis. However, similarity analysis based on characteristic distribution of L-tuple is not effective for the comparison of highly conservative sequences. In this paper, a new similarity measurement approach based on Triplets of Nucleic Acid Bases (TNAB) is introduced for DNA sequence similarity analysis. The new approach characterizes both the content feature and position feature of a DNA sequence using the frequency and position of occurrence of TNAB in the sequence. The experimental results show that the approach based on TNAB is effective for analysing DNA sequence similarity.

CALL FOR PAPERS Journal of Functional Programming Special Issue on Theorem Provers and Functional Programming

1997 ◽  
Vol 7 (1) ◽  
pp. 125-126
Author(s):  
Tom Melham
Keyword(s):  
Interface Design ◽  
Functional Programming ◽  
Type Systems ◽  
Theory And Practice ◽  
Theorem Prover ◽  
Functional Languages ◽  
Special Issue ◽  
Theorem Provers ◽  
Problems And Solutions ◽  
Submission Deadline

A special issue of the Journal of Functional Programming will be devoted to the use of functional programming in theorem proving. The submission deadline is 31 August 1997.The histories of theorem provers and functional languages have been deeply intertwined since the advent of Lisp. A notable example is the ML family of languages, which are named for the meta language devised for the LCF theorem prover, and which provide both the implementation platform and interaction facilities for numerous later systems (such as Coq, HOL, Isabelle, NuPrl). Other examples include Lisp (as used for ACL2, PVS, Nqthm) and Haskell (as used for Veritas).This special issue is devoted to the theory and practice of using functional languages to implement theorem provers and using theorem provers to reason about functional languages. Topics of interest include, but are not limited to:– architecture of theorem prover implementations– interface design in the functional context– limits of the LCF methodology– impact of host language features– type systems– lazy vs strict languages– imperative (impure) features– performance problems and solutions– problems of scale– special implementation techniques– term representations (e.g. de Bruijn vs name carrying vs BDDs)– limitations of current functional languages– mechanised theories of functional programming

scholarly journals EDITORIAL

1998 ◽  
Vol 8 (4) ◽  
pp. 319-321 ◽  
Author(s):  
SIMON PEYTON JONES ◽  
PHIL WADLER
Keyword(s):  
Functional Programming ◽  
Lambda Calculus ◽  
Language Design ◽  
Functional Languages ◽  
Functional Language ◽  
The Past ◽  
Semantic Models ◽  
Compiled Code ◽  
Design Speed ◽  
Academic Content

In the end, research on functional languages does little good unless they are used to write something other than compilers for functional languages. However, if one scans a typical functional programming conference or journal, one mainly sees papers on twists in language design, speed-ups in compiled code, clever new analyses, or refinements to semantic models. It much less common to see a paper that considers a functional language as a tool to some other practical end. We would like to see this change.The Journal of Functional Programming carries, and will continue to carry, articles on all aspects of functional programming from lambda calculus theory to language design to implementation. But we have specially sought, and will continue to seek, papers on functional programming practice and experience.Research and papers on practice and experience sometimes receive less attention because they are perceived as possessing less academic content. So we want to remind potential authors that we have published a number of papers on this topic in the past, and to spell out the criteria we apply to such papers.

scholarly journals EnTrance: Exploration of Entropy Scaling Ball Cover Search in Protein Sequences

2021 ◽  
Author(s):  
Yoonjin Kim ◽  
Zhen Guo ◽  
Jeffrey A. Robertson ◽  
Benjamin Reidys ◽  
Ziyan Zhang ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Sequence Similarity ◽  
Protein Sequences ◽  
Biological Data ◽  
Biological Databases ◽  
Biological Sequence ◽  
Absolute Size ◽  
Sequence Identification ◽  
Build Time ◽  
Search Approach

Biological sequence alignment using computational power has received increasing attention as technology develops. It is important to predict if a novel DNA sequence is potentially dangerous by determining its taxonomic identity and functional characteristics through sequence identification. This task can be facilitated by the rapidly increasing amounts of biological data in DNA and protein databases thanks to the corresponding increase in computational and storage costs. Unfortunately, the growth in biological databases has caused difficulty in exploiting this information. EnTrance presents an approach that can expedite the analysis of this large database by employing entropy scaling. This allows scaling with the amount of entropy in the database instead of scaling with the absolute size of the database. Since DNA and protein sequences are biologically meaningful, the space of biological sequences demonstrates the structure exploited by entropy scaling. As biological sequence databases grow, taking advantage of this structure can be extremely beneficial for reducing query times. EnTrance, the entropy scaling search algorithm introduced here, accelerates the biological sequence search exemplified by tools such as BLAST. EnTrance does this by utilizing a two step search approach. In this fashion, EnTrance quickly reduces the number of potential matches before more exhaustively searching the remaining sequences. Tests of EnTrance show that this approach can lead to improved query times. However, constructing the required entropy scaling indices beforehand can be challenging. To improve performance, EnTrance investigates several ideas for accelerating index build time that supports entropy scaling searches. In particular, EnTrance makes full use of the concurrency features of Go language greatly reducing the index build time. Our results identify key tradeoffs and demonstrate that there is potential in using these techniques for sequence similarity searches. Finally, EnTrance returns more matches and higher percentage identity matches when compared with existing tools.

scholarly journals Default rules for Curry

2016 ◽  
Vol 17 (2) ◽  
pp. 121-147 ◽  
Author(s):  
SERGIO ANTOY ◽  
MICHAEL HANUS
Keyword(s):  
Logic Programming ◽  
Basic Concept ◽  
Functional Programming ◽  
Functional Languages ◽  
Logic Programs ◽  
The Core ◽  
Functional Logic Programming ◽  
Functional Logic ◽  
Default Rules

AbstractIn functional logic programs, rules are applicable independently of textual order, i.e., any rule can potentially be used to evaluate an expression. This is similar to logic languages and contrary to functional languages, e.g., Haskell enforces a strict sequential interpretation of rules. However, in some situations it is convenient to express alternatives by means of compact default rules. Although default rules are often used in functional programs, the non-deterministic nature of functional logic programs does not allow to directly transfer this concept from functional to functional logic languages in a meaningful way. In this paper, we propose a new concept of default rules for Curry that supports a programming style similar to functional programming while preserving the core properties of functional logic programming, i.e., completeness, non-determinism, and logic-oriented use of functions. We discuss the basic concept and propose an implementation which exploits advanced features of functional logic languages.

scholarly journals Editorial for the Special Issue on Parallel and Concurrent Functional Programming

2018 ◽  
Vol 28 ◽  
Author(s):  
Gabriele Keller ◽  
Fritz Henglein
Keyword(s):  
Programming Languages ◽  
Functional Programming ◽  
Programming Model ◽  
State Of The Art ◽  
Functional Languages ◽  
Functional Language ◽  
Special Issue ◽  
Practical Applications ◽  
Wide Range ◽  
Concurrent Computing

Functional languages are uniquely suited to providing programmers with a programming model for parallel and concurrent computing. This is reflected in the wide range of work that is currently underway, both on parallel and concurrent functional languages, as well as on bringing functional language features to other programming languages. This has resulted in a rapidly growing number of practical applications. The Journal of Functional Programming decided to dedicate a special issue to this field to showcase the state of the art in how functional languages and functional concepts currently assist programmers with the task of managing the challenges of creating parallel and concurrent systems.

Evolving Fisher Kernels for Biological Sequence Classification

2013 ◽  
Vol 21 (1) ◽  
pp. 83-105 ◽  
Author(s):  
K.-J. Won ◽  
C. Saunders ◽  
A. Prügel-Bennett
Keyword(s):  
Sequence Similarity ◽  
Generative Models ◽  
Complex Model ◽  
Generative Model ◽  
Support Vector ◽  
Homologous Sequence ◽  
Sequence Information ◽  
Biological Sequence ◽  
Domain Specific ◽  
Fisher Kernel

Fisher kernels have been successfully applied to many problems in bioinformatics. However, their success depends on the quality of the generative model upon which they are built. For Fisher kernel techniques to be used on novel problems, a mechanism for creating accurate generative models is required. A novel framework is presented for automatically creating domain-specific generative models that can be used to produce Fisher kernels for support vector machines (SVMs) and other kernel methods. The framework enables the capture of prior knowledge and addresses the issue of domain-specific kernels, both of which are current areas that are lacking in many kernel-based methods. To obtain the generative model, genetic algorithms are used to evolve the structure of hidden Markov models (HMMs). A Fisher kernel is subsequently created from the HMM, and used in conjunction with an SVM, to improve the discriminative power. This paper investigates the effectiveness of the proposed method, named GA-SVM. We show that its performance is comparable if not better than other state of the art methods in classifying secretory protein sequences of malaria. More interestingly, it showed better results than the sequence-similarity-based approach, without the need for additional homologous sequence information in protein enzyme family classification. The experiments clearly demonstrate that the GA-SVM is a novel way to find features with good performance from biological sequences, that does not require extensive tuning of a complex model.

scholarly journals A Short Historical Survey of Functional Hardware Languages

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Gang Chen
Keyword(s):  
Programming Languages ◽  
Functional Programming ◽  
Hardware Design ◽  
Functional Languages ◽  
Historical Survey ◽  
Functional Programming Languages ◽  
High Degree

Functional programming languages offer a high degree of abstractions and clean semantics, which are desirable for hardware descriptions. This short historical survey is about functional languages specifically created for hardware design and verification. It also includes those hardware languages or formalisms which are strongly influenced by functional programming style.

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