Semiotic thoughts on biological sequence representations

2021 ◽  
Vol 24 ◽  
Author(s):  
Guillermo Restrepo
Keyword(s):  
Research Area ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Mathematical Chemistry ◽  
Dna And Rna

: The deluge of biological sequences ranging from those of proteins, DNA and RNA to genomes has increased the models for their representation, which are further used to contrast those sequences. Here we present a brief bibliometric description of the research area devoted to representation of biological sequences and highlight the semiotic reaches of this process. Finally, we argue that this research area needs further research according to the evolution of mathematical chemistry and its drawbacks are required to be overcome.

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.

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.

scholarly journals A deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences

2020 ◽  
Author(s):  
Chao Wei ◽  
Junying Zhang ◽  
Xiguo Yuan ◽  
Zongzhen He ◽  
Guojun Liu
Keyword(s):  
Deep Learning ◽  
Noncoding Rna ◽  
Order Information ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Coding Region ◽  
Protein Coding ◽  
Learning Framework ◽  
Coding Regions ◽  
Local Sequence

ABSTRACTProtein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifier capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping kmer, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. In fact, kmer features that count the occurrence frequency of trinucleotides only reflect the local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. In viewing of the point, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploiting global sequence order information, non-overlapping kmer features and statistical dependencies among coding labels. Evaluated on genomic and transcript sequences, our proposed method significantly outperforms existing state-of-the-art methods.

scholarly journals Protein Family Classification using Deep Learning

2018 ◽  
Author(s):  
K S Naveenkumar ◽  
Babu R Mohammed Harun ◽  
R Vinayakumar ◽  
KP Soman
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Protein Classification ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Deep Learning Algorithm ◽  
N Gram ◽  
Family Classification ◽  
Selection Of

AbstractProtein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

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.

Multiple sequence alignment using enhanced bird swarm align algorithm

2021 ◽  
pp. 1-18
Author(s):  
Hafiz Asadul Rehman ◽  
Kashif Zafar ◽  
Ayesha Khan ◽  
Abdullah Imtiaz
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Research Area ◽  
Evolutionary Information ◽  
Optimal Alignment ◽  
Biological Sequences ◽  
Multiple Sequence ◽  
Alignment Problem ◽  
Computationally Expensive ◽  
Bird Swarm Algorithm

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

scholarly journals MathFeature: Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

2020 ◽  
Author(s):  
Robson P. Bonidia ◽  
Danilo S. Sanches ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Feature Extraction ◽  
Mathematical Expression ◽  
Predictive Performance ◽  
Relevant Information ◽  
Machine Learning Algorithms ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Genomic Signal Processing ◽  
Link Type ◽  
Main Challenge

AbstractMachine learning algorithms have been very successfully applied to extract new and relevant knowledge from biological sequences. However, the predictive performance of these algorithms is largely affected by how the sequences are represented. Thereby, the main challenge is how to numerically represent a biological sequence in a numeric vector with an efficient mathematical expression. Several feature extraction techniques have been proposed for biological sequences, where most of them are available in feature extraction packages. However, there are relevant approaches that are not available in existing packages, techniques based on mathematical descriptors, e.g., Fourier, entropy, and graphs. Therefore, this paper presents a new package, named MathFeature, which implements mathematical descriptors able to extract relevant information from biological sequences. MathFeature provides 20 approaches based on several studies found in the literature, e.g., multiple numeric mappings, genomic signal processing, chaos game theory, entropy, and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages.Availability and implementationMathFeature is freely available at https://bonidia.github.io/MathFeature/ or https://github.com/Bonidia/[email protected], [email protected]

scholarly journals PolyA: a tool for adjudicating competing annotations of biological sequences

2021 ◽  
Author(s):  
Kaitlin M. Carey ◽  
Robert Hubley ◽  
George T. Lesica ◽  
Daniel Olson ◽  
Jack W. Roddy ◽  
...  
Keyword(s):  
Large Scale ◽  
Software Tool ◽  
Alignment Score ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Sequence Elements

AbstractAnnotation of a biological sequence is usually performed by aligning that sequence to a database of known sequence elements. When that database contains elements that are highly similar to each other, the proper annotation may be ambiguous, because several entries in the database produce high-scoring alignments. Typical annotation methods work by assigning a label based on the candidate annotation with the highest alignment score; this can overstate annotation certainty, mislabel boundaries, and fails to identify large scale rearrangements or insertions within the annotated sequence. Here, we present a new software tool, PolyA, that adjudicates between competing alignment-based annotations by computing estimates of annotation confidence, identifying a trace with maximal confidence, and recursively splicing/stitching inserted elements. PolyA communicates annotation certainty, identifies large scale rearrangements, and detects boundaries between neighboring elements.

scholarly journals Detection of New Motifs Properties in Biodata

2015 ◽  
Vol 1 (4) ◽  
pp. 404
Author(s):  
Nooruldeen Nasih Qader ◽  
Hussein K. Al-Khafaji
Keyword(s):  
Molecular Biology ◽  
Real Data ◽  
Motif Finding ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Data Set ◽  
Background Frequency ◽  
Motif Model ◽  
Naturalistic Methodology ◽  
Strong Existence

Biodata are rich of information. Knowing the properties of biological sequence can be valuable in analyzing data and making appropriate conclusions. This research applied naturalistic methodology to investigate the structural properties of biological sequences (i.e., DNA). The research implemented in the field of motif finding. Two new motifs properties were discovered named identical neighbors and adjacent neighbors.  The analysis is done in different situations of background frequency and motif model, using distinctive real data set of varied data size. The analysis demonstrated the strong existence of the properties. Exploiting of these properties considers significant steps towards developing powerful algorithms in molecular biology.

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.

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