Mapping Biomolecular Sequences: Graphical Representations - their Origins, Applications and Future Prospects

2021 ◽  
Vol 24 ◽  
Author(s):  
Ashesh Nandy
Keyword(s):  
Dna Sequences ◽  
Graphical Representation ◽  
Sequence Data ◽  
Basic Unit ◽  
Graphical Representations ◽  
Biological Sequences ◽  
Biological Sequence ◽  
New Approach ◽  
3D Space ◽  
2D And 3D

The exponential growth in the depositories of biological sequence data have generated an urgent need to store, retrieve and analyse the data efficiently and effectively for which the standard practice of using alignment procedures are not adequate due to high demand on computing resources and time. Graphical representation of sequences has become one of the most popular alignment-free strategies to analyse the biological sequences where each basic unit of the sequences – the bases adenine, cytosine, guanine and thymine for DNA/RNA, and the 20 amino acids for proteins – are plotted on a multi-dimensional grid. The resulting curve in 2D and 3D space and the implied graph in higher dimensions provide a perception of the underlying information of the sequences through visual inspection; numerical analyses, in geometrical or matrix terms, of the plots provide a measure of comparison between sequences and thus enable study of sequence hierarchies. The new approach has also enabled studies of comparisons of DNA sequences over many thousands of bases and provided new insights into the structure of the base compositions of DNA sequences In this article we review in brief the origins and applications of graphical representations and highlight the future perspectives in this field.

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.

scholarly journals Generative probabilistic biological sequence models that account for mutational variability

2020 ◽  
Author(s):  
Eli N. Weinstein ◽  
Debora S. Marks
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Disordered Proteins ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Multiple Sequence ◽  
Continuous Space ◽  
Future Evolution ◽  
Disordered Protein ◽  
Latent Representations

AbstractLarge-scale sequencing has revealed extraordinary diversity among biological sequences, produced over the course of evolution and within the lifetime of individual organisms. Existing methods for building statistical models of sequences often pre-process the data using multiple sequence alignment, an unreliable approach for many genetic elements (antibodies, disordered proteins, etc.) that is subject to fundamental statistical pathologies. Here we introduce a structured emission distribution (the MuE distribution) that accounts for mutational variability (substitutions and indels) and use it to construct generative and predictive hierarchical Bayesian models (H-MuE models). Our framework enables the application of arbitrary continuous-space vector models (e.g. linear regression, factor models, image neural-networks) to unaligned sequence data. Theoretically, we show that the MuE generalizes classic probabilistic alignment models. Empirically, we show that H-MuE models can infer latent representations and features for immune repertoires, predict functional unobserved members of disordered protein families, and forecast the future evolution of pathogens.

scholarly journals Estimation of Similarity between DNA Sequences and Its Graphical Representation

2020 ◽  
Vol 9 (1) ◽  
pp. 43-51
Keyword(s):  
Sequence Analysis ◽  
Molecular Biology ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Graphical Representation ◽  
Similarity Analysis ◽  
Biological Sequence ◽  
Field Of Study ◽  
Biological Sequence Analysis ◽  
Wide Range

Bioinformatics, which is now a well known field of study, originated in the context of biological sequence analysis. Recently graphical representation takes place for the research on DNA sequence. Research in biological sequence is mainly based on the function and its structure. Bioinformatics finds wide range of applications specifically in the domain of molecular biology which focuses on the analysis of molecules viz. DNA, RNA, Protein etc. In this review, we mainly deal with the similarity analysis between sequences and graphical representation of DNA sequence.

scholarly journals Genetic Similarity Analysis Based on Positive and Negative Sequence Patterns of DNA

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2090
Author(s):  
Yue Lu ◽  
Long Zhao ◽  
Zhao Li ◽  
Xiangjun Dong
Keyword(s):  
Dna Sequences ◽  
Sequence Similarity ◽  
Sequential Patterns ◽  
Similarity Analysis ◽  
Frequent Patterns ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Genetic Characteristics ◽  
Missing Gene ◽  
Sequence Similarity Analysis

Similarity analysis of DNA sequences can clarify the homology between sequences and predict the structure of, and relationship between, them. At the same time, the frequent patterns of biological sequences explain not only the genetic characteristics of the organism, but they also serve as relevant markers for certain events of biological sequences. However, most of the aforementioned biological sequence similarity analysis methods are targeted at the entire sequential pattern, which ignores the missing gene fragment that may induce potential disease. The similarity analysis of such sequences containing a missing gene item is a blank. Consequently, some sequences with missing bases are ignored or not effectively analyzed. Thus, this paper presents a new method for DNA sequence similarity analysis. Using this method, we first mined not only positive sequential patterns, but also sequential patterns that were missing some of the base terms (collectively referred to as negative sequential patterns). Subsequently, we used these frequent patterns for similarity analysis on a two-dimensional plane. Several experiments were conducted in order to verify the effectiveness of this algorithm. The experimental results demonstrated that the algorithm can obtain various results through the selection of frequent sequential patterns and that accuracy and time efficiency was improved.

scholarly journals Frequent Patterns Algorithm of Biological Sequences based on Pattern Prefix-tree

2019 ◽  
Vol 14 (4) ◽  
pp. 574-589
Author(s):  
Linyan Xue ◽  
Xiaoke Zhang ◽  
Fei Xie ◽  
Shuang Liu ◽  
Peng Lin
Keyword(s):  
Pattern Mining ◽  
Sequence Data ◽  
Biological Significance ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Protein Database ◽  
Sequence Pattern ◽  
Multiple Sequences

In the application of bioinformatics, the existing algorithms cannot be directly and efficiently implement sequence pattern mining. Two fast and efficient biological sequence pattern mining algorithms for biological single sequence and multiple sequences are proposed in this paper. The concept of the basic pattern is proposed, and on the basis of mining frequent basic patterns, the frequent pattern is excavated by constructing prefix trees for frequent basic patterns. The proposed algorithms implement rapid mining of frequent patterns of biological sequences based on pattern prefix trees. In experiment the family sequence data in the pfam protein database is used to verify the performance of the proposed algorithm. The prediction results confirm that the proposed algorithms can’t only obtain the mining results with effective biological significance, but also improve the running time efficiency of the biological sequence pattern mining.

scholarly journals SeqRepo: A system for managing local collections of biological sequences

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0239883
Author(s):  
Reece K. Hart ◽  
Andreas Prlić
Keyword(s):  
Programming Languages ◽  
High Performance ◽  
Sequence Data ◽  
Random Access ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Public And Private ◽  
Human Sequence ◽  
Local Sequence ◽  
Sequence Identifier

Motivation Access to biological sequence data, such as genome, transcript, or protein sequence, is at the core of many bioinformatics analysis workflows. The National Center for Biotechnology Information (NCBI), Ensembl, and other sequence database maintainers provide methods to access sequences through network connections. For many users, the convenience and currency of remotely managed data are compelling, and the network latency is non-consequential. However, for high-throughput and clinical applications, local sequence collections are essential for performance, stability, privacy, and reproducibility. Results Here we describe SeqRepo, a novel system for building a local, high-performance, non-redundant collection of biological sequences. SeqRepo enables clients to use primary database identifiers and several digests to identify sequences and sequence alises. SeqRepo provides a native Python interface and a REST interface, which can run locally and enables access from other programming languages. SeqRepo also provides an alternative REST interface based on the GA4GH refget protocol. SeqRepo provides fast random access to sequence slices. We provide results that demonstrate that a local SeqRepo sequence collection yields significant performance benefits of up to 1300-fold over remote sequence collections. In our use case for a variant validation and normalization pipeline, SeqRepo improved throughput 50-fold relative to use with remote sequences. SeqRepo may be used with any species or sequence type. Regular snapshots of Human sequence collections are available. It is often convenient or necessary to use a computed digest as a sequence identifier. For example, a digest-based identifier may be used to refer to proprietary reference genomes or segments of a graph genome, for which conventional identifiers will not be available. Here we also introduce a convention for the application of the SHA-512 hashing algorithm with Base64 encoding to generate URL-safe identifiers. This convention, sha512t24u, combines a fast digest mechanism with a space-efficient representation that can be used for any object. Our report includes an analysis of timing and collision probabilities for sha512t24u. SeqRepo enables clients to use sha512t24u as identifiers, thereby seamlessly integrating public and private sequence sets. Availability SeqRepo is released under the Apache License 2.0 and is available on github and PyPi. Docker images and database snapshots are also available. See https://github.com/biocommons/biocommons.seqrepo.

scholarly journals Adaptive dating and fast proposals: revisiting the phylogenetic relaxed clock model

2020 ◽  
Author(s):  
Jordan Douglas ◽  
Rong Zhang ◽  
Remco Bouckaert
Keyword(s):  
Sequence Data ◽  
Extreme Case ◽  
Evolutionary Divergence ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Clock Model ◽  
Phylogenetic Framework ◽  
Bayesian Phylogenetic Inference ◽  
Clock Models ◽  
Relaxed Clock

AbstractUncorrelated relaxed clock models enable estimation of molecular substitution rates across lineages and are widely used in phylogenetics for dating evolutionary divergence times. In this article we delved into the internal complexities of the relaxed clock model in order to develop efficient MCMC operators for Bayesian phylogenetic inference. We compared three substitution rate parameterisations, introduced an adaptive operator which learns the weights of other operators during MCMC, and we explored how relaxed clock model estimation can benefit from two cutting-edge proposal kernels: the AVMVN and Bactrian kernels. This work has produced an operator scheme that is up to 65 times more efficient at exploring continuous relaxed clock parameters compared with previous setups, depending on the dataset. Finally, we explored variants of the standard narrow exchange operator which are specifically designed for the relaxed clock model. In the most extreme case, this new operator traversed tree space 40% more efficiently than narrow exchange. The methodologies introduced are adaptive and highly effective on short as well as long alignments. The results are available via the open source optimised relaxed clock (ORC) package for BEAST 2 under a GNU licence (https://github.com/jordandouglas/ORC).Author summaryBiological sequences, such as DNA, accumulate mutations over generations. By comparing such sequences in a phylogenetic framework, the evolutionary tree of lifeforms can be inferred. With the overwhelming availability of biological sequence data, and the increasing affordability of collecting new data, the development of fast and efficient phylogenetic algorithms is more important than ever. In this article we focus on the relaxed clock model, which is very popular in phylogenetics. We explored how a range of optimisations can improve the statistical inference of the relaxed clock. This work has produced a phylogenetic setup which can infer parameters related to the relaxed clock up to 65 times faster than previous setups, depending on the dataset. The methods introduced adapt to the dataset during computation and are highly efficient when processing long biological sequences.

DATA MINING TOOLS FOR BIOLOGICAL SEQUENCES

2003 ◽  
Vol 01 (01) ◽  
pp. 139-167 ◽  
Author(s):  
HUIQING LIU ◽  
LIMSOON WONG
Keyword(s):  
Data Mining ◽  
Translation Initiation ◽  
Dna Sequences ◽  
Sequence Data ◽  
Feature Selection Method ◽  
Biological Sequences ◽  
Related Data ◽  
Entropy Measures ◽  
Correlation Based Feature Selection ◽  
Mining Tools

We describe a methodology, as well as some related data mining tools, for analyzing sequence data. The methodology comprises three steps: (a) generating candidate features from the sequences, (b) selecting relevant features from the candidates, and (c) integrating the selected features to build a system to recognize specific properties in sequence data. We also give relevant techniques for each of these three steps. For generating candidate features, we present various types of features based on the idea of k-grams. For selecting relevant features, we discuss signal-to-noise, t-statistics, and entropy measures, as well as a correlation-based feature selection method. For integrating selected features, we use machine learning methods, including C4.5, SVM, and Naive Bayes. We illustrate this methodology on the problem of recognizing translation initiation sites. We discuss how to generate and select features that are useful for understanding the distinction between ATG sites that are translation initiation sites and those that are not. We also discuss how to use such features to build reliable systems for recognizing translation initiation sites in DNA sequences.

scholarly journals SeqRepo: A system for managing local collections biological sequences

2020 ◽  
Author(s):  
Reece K. Hart ◽  
Andreas Prlić
Keyword(s):  
Programming Languages ◽  
High Performance ◽  
Sequence Data ◽  
Random Access ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Public And Private ◽  
Human Sequence ◽  
Local Sequence ◽  
Sequence Identifier

AbstractMotivationAccess to biological sequence data, such as genome, transcript, or protein sequence, is at the core of many bioinformatics analysis workflows. The National Center for Biotechnology Information (NCBI), Ensembl, and other sequence database maintainers provide methods to access sequences through network connections. For many users, the convenience and currency of remotely managed data are compelling, and the network latency is non-consequential. However, for high-throughput and clinical applications, local sequence collections are essential for performance, stability, privacy, and reproducibility.ResultsHere we describe SeqRepo, a novel system for building a local, high-performance, non-redundant collection of biological sequences. SeqRepo enables clients to use primary database identifiers and several digests to identify sequences and sequence alises. SeqRepo provides a native Python interface and a REST interface, which can run locally and enables access from other programming languages. SeqRepo also provides an alternative REST interface based on the GA4GH refget protocol.SeqRepo provides fast random access to sequence slices. We provide results that demonstrate that a local SeqRepo sequence collection yields significant performance benefits of up to 1300-fold over remote sequence collections. In our use case for a variant validation and normalization pipeline, SeqRepo improved throughput 50-fold relative to use with remote sequences. SeqRepo may be used with any species or sequence type. Regular snapshots of Human sequence collections are available.It is often convenient or necessary to use a computed digest as a sequence identifier. For example, a digest-based identifier may be used to refer to proprietary reference genomes or segments of a graph genome, for which conventional identifiers will not be available. Here we also introduce a convention for the application of the SHA-512 hashing algorithm with Base64 encoding to generate URL-safe identifiers. This convention, sha512t24u, combines a fast digest mechanism with a space-efficient representation that can be used for any object. Our report includes an analysis of timing and collision probabilities for sha512t24u. SeqRepo enables clients to use sha512t24u as identifiers, thereby seamlessly integrating public and private sequence sets.AvailabilitySeqRepo is released under the Apache License 2.0 and is available on github and PyPi. Docker images and database snapshots are also available. See https://github.com/biocommons/biocommons.seqrepo.

Proof Theory of the Cut Rule

2018 ◽  
Author(s):  
J. R. B. Cockett ◽  
R. A. G. Seely
Keyword(s):  
Proof Theory ◽  
Graphical Representation ◽  
Basic Component ◽  
Graphical Representations ◽  
Mathematical Language ◽  
Basic Logic ◽  
Cut Rule ◽  
Structural Rules ◽  
The One ◽  
Categorical Semantics

This chapter describes the categorical proof theory of the cut rule, a very basic component of any sequent-style presentation of a logic, assuming a minimum of structural rules and connectives, in fact, starting with none. It is shown how logical features can be added to this basic logic in a modular fashion, at each stage showing the appropriate corresponding categorical semantics of the proof theory, starting with multicategories, and moving to linearly distributive categories and *-autonomous categories. A key tool is the use of graphical representations of proofs (“proof circuits”) to represent formal derivations in these logics. This is a powerful symbolism, which on the one hand is a formal mathematical language, but crucially, at the same time, has an intuitive graphical representation.

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