An Efficient Tool for Searching Maximal and Super Maximal Repeats in Large DNA/Protein Sequences via Induced-Enhanced Suffix Array

2019 ◽  
Vol 12 (2) ◽  
pp. 128-134
Author(s):  
Sanjeev Kumar ◽  
Suneeta Agarwal ◽  
Ranvijay
Keyword(s):  
Protein Sequences ◽  
Input Sequence ◽  
Suffix Array ◽  
Secondary Memory ◽  
Time And Space ◽  
Efficient Tool ◽  
Frequency Distributions ◽  
Alignment Algorithms ◽  
Common Prefix ◽  
Art Works

Background: DNA and Protein sequences of an organism contain a variety of repeated structures of various types. These repeated structures play an important role in Molecular biology as they are related to genetic backgrounds of inherited diseases. They also serve as a marker for DNA mapping and DNA fingerprinting. Efficient searching of maximal and super maximal repeats in DNA/Protein sequences can lead to many other applications in the area of genomics. Moreover, these repeats can also be used for identification of critical diseases by finding the similarity between frequency distributions of repeats in viruses and genomes (without using alignment algorithms). Objective: The study aims to develop an efficient tool for searching maximal and super maximal repeats in large DNA/Protein sequences. Methods: The proposed tool uses a newly introduced data structure Induced Enhanced Suffix Array (IESA). IESA is an extension of enhanced suffix array. It uses induced suffix array instead of classical suffix array. IESA consists of Induced Suffix Array (ISA) and an additional array-Longest Common Prefix (LCP) array. ISA is an array of all sorted suffixes of the input sequence while LCP array stores the lengths of the longest common prefixes between all pairs of consecutive suffixes in an induced suffix array. IESA is known to be efficient w.r.t. both time and space. It facilitates the use of secondary memory for constructing the large suffix-array. Results: An open source standalone tool named MSR-IESA for searching maximal and super maximal repeats in DNA/Protein sequences is provided at https://github.com/sanjeevalg/MSRIESA. Experimental results show that the proposed algorithm outperforms other state of the art works w.r.t. to both time and space. Conclusion: The proposed tool MSR-IESA is remarkably efficient for the analysis of DNA/Protein sequences, having maximal and super maximal repeats of any length. It can be used for identification of well-known diseases.

scholarly journals Engineering Augmented Suffix Sorting Algorithms

2018 ◽  
Author(s):  
Felipe A. Louza ◽  
Guilherme P. Telles ◽  
Simon Gog
Keyword(s):  
Computer Science ◽  
Full Text ◽  
Suffix Array ◽  
Optimal Time ◽  
Time And Space ◽  
Sorting Algorithms ◽  
Constant Size ◽  
Common Prefix ◽  
Efficient Processing ◽  
Burrows Wheeler Transform

Strings are prevalent in Computer Science and algorithms for their efficient processing are fundamental in various applications. The results introduced in this work contribute with theoretical improvements and practical advances in building full-text indexes. Our first contribution is an in-place algorithm that computes the Burrows-Wheeler transform and the longest common prefix (LCP) array. Our second contribution is the construction of the suffix array augmented with the LCP array in optimal time and space for strings from constant size alphabets. Our third contribution is a set of algorithms to construct full-text indexes for string collections in optimal theoretical bounds. This work is an extended abstract of the Ph.D. thesis of the first author.

scholarly journals Fast activation maximization for molecular sequence design

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Johannes Linder ◽  
Georg Seelig
Keyword(s):  
Deep Learning ◽  
Molecular Design ◽  
Protein Sequences ◽  
Input Sequence ◽  
General Purpose ◽  
Design Tool ◽  
Learning Models ◽  
Molecular Sequence ◽  
Regularization Techniques ◽  
Fast Activation

Abstract Background Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Results Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp’s capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. Conclusions Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.

scholarly journals A general framework to learn tertiary structure for protein sequence annotation

2021 ◽  
Author(s):  
Mu Gao ◽  
Jeffrey Skolnick
Keyword(s):  
Deep Learning ◽  
Protein Sequence ◽  
General Framework ◽  
Tertiary Structure ◽  
Learning Algorithms ◽  
Protein Structures ◽  
Protein Sequences ◽  
Input Sequence ◽  
Sequence Annotation ◽  
Deep Learning Neural Network

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

scholarly journals Problems of time and space in the philosophy of Shūzō Kuki (1888 – 1941)

2021 ◽  
pp. 1-12
Author(s):  
Lyubov' Borisovna Karelova
Keyword(s):  
Time Correlation ◽  
Edo Period ◽  
Time And Space ◽  
Space And Time ◽  
Art Works ◽  
Temporal Aspect ◽  
The Subject ◽  
The Aesthetic ◽  
Different Characteristics ◽  
Philosophical Writings

The subject of this research is the philosophy of Shūzō Kuki, which is usually associated with his original concept, built around the concept of iki that simultaneously denotes taste, wealth, sensibility, dignity, reserve, and spontaneity, as well as embodies the aesthetic ideal formed in urban culture of the Edo period (1603 – 1868). The Japanese philosopher is also notable for a number of other intellectual insights. For depicting a holistic image on the philosophical views of Shūzō Kuki, a more extensive array of his works is introduced into the scientific discourse. A significant part of these work have not been translated into the Russian or other foreign languages. This article explores the problems of time and space, which are cross-cutting in the works of Shūzō Kuki  using examples of such philosophical writings as the “Theory of Time”, “What is Anthropology?”, “Problems of Time. Bergson and Heidegger”, “Metaphysical Time”, "Problems of Casualty”. The research employs the method of historical-philosophical reconstruction and sequential textual analysis of sources. Special attention is given to the problems of cyclical time, correlation between the infinite and the finite, and its reflection in the literary or art works, existential-anthropological landscape of space and time, spatial-temporal aspect of casualty and relevance. The conclusion is made on the contribution of Shūzō Kuki to elaboration of the problems of space and time, namely his cross-cultural approach that allows viewing the general philosophical problems from the perspective of both Western and Eastern thought, as well as a distinct  “interdisciplinary” approach towards analysis of the phenomena of space and time, which are viewed from different perspective and acquire different characteristics depending on the angle and aspect of reality of the corresponding context. Thus, there is a variety of concepts of time, which do not eliminate, but complement each other.

scholarly journals RECONSTRUCTING A SUFFIX ARRAY

2006 ◽  
Vol 17 (06) ◽  
pp. 1281-1295 ◽  
Author(s):  
FRANTISEK FRANEK ◽  
WILLIAM F. SMYTH
Keyword(s):  
Data Compression ◽  
Data Structures ◽  
Suffix Tree ◽  
Suffix Array ◽  
Efficient Algorithms ◽  
Lexicographical Order ◽  
Time And Space

For certain problems (for example, computing repetitions and repeats, data compression applications) it is not necessary that the suffixes of a string represented in a suffix tree or suffix array should occur in lexicographical order (lexorder). It thus becomes of interest to study possible alternate orderings of the suffixes in these data structures, that may be easier to construct or more efficient to use. In this paper we consider the "reconstruction" of a suffix array based on a given reordering of the alphabet, and we describe simple time- and space-efficient algorithms that accomplish it.

scholarly journals Ruby vs. Perl – the Languages of Bioinformatics

2013 ◽  
Vol 35 (1) ◽  
pp. 143-155
Author(s):  
Maciej Goliński ◽  
Agnieszka Kitlas Golińska
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Protein Sequences ◽  
Nucleotide Sequences ◽  
Regular Expressions ◽  
Fasta File ◽  
Efficient Tool ◽  
Specific Nucleotide

Abstract Ruby and Perl are programming languages used in many fields. In this paper we would like to present their usefulness with regard to basic bioinformatic problems. We concentrate on a comparison of widely used Perl and relatively rarely used Ruby to show that Ruby can be a very efficient tool in bioinformatics. Both Perl and Ruby have a built-in regular expressions (or regexp) engine, which is essential in solving many problems in bioinformatics. We present some selected examples: printing the file content, removing comments from a FASTA file, using hashes, printing nucleotides included in a sequence, searching for a specific nucleotide in sequence and translating nucleotide sequences into protein sequences obtained in GenBank format. It is our belief that Ruby’s popularity will rise because of its simple syntax and the richness of its methods. Programs in Ruby are very easy to read and therefore easier to maintain and debug, which are the most important characteristics for a programming language.

A new 2D graphical representation of protein sequence and its application

2015 ◽  
Vol 08 (05) ◽  
pp. 1550063
Author(s):  
Lei Wang ◽  
Hui Peng ◽  
Jinhua Zheng ◽  
Yanzi Qiu
Keyword(s):  
Protein Sequence ◽  
Visual Inspection ◽  
Visual Analysis ◽  
Graphical Representation ◽  
Protein Sequences ◽  
Storage Space ◽  
Efficient Tool ◽  
Characteristic Model ◽  
Representation Scheme

Graphical representation is a very efficient tool for visual analysis of protein sequences. In this paper, a novel 2D graphical representation scheme is proposed on the basis of a newly introduced concept, named characteristic model of the protein sequences. After obtaining the 2D graphics of protein sequences, two numerical characterizations of them is designed as descriptors to analyze the nine DN5 protein sequences, simulation and analysis results show that, comparing with existing methods, our method is not only visible, intuitional, and simple, but also has no circuit or degeneracy, and even more important, since the storage space required by our method is constant and has nothing to do with the length of protein sequences, then it can keep excellent visual inspection for long protein sequences.

Sign in / Sign up

Export Citation Format

Share Document