scispace - formally typeset
Search or ask a question

What is the geometric representation of DNA? 


Best insight from top research papers

The geometric representation of DNA involves transforming DNA sequences into numerical vectors and arranging them into a zigzag curve . Another approach involves transforming DNA sequences into vectors in 5-dimensional space, where nucleotides of the same type are on the same line . A novel 4D graphical representation method of DNA sequences has also been proposed, which avoids non-unique representations and overlapping lines . Additionally, a method based on chaos geometry in 4-dimensional space has been developed for graphical representation of DNA sequences . A new method for finding local similar subsequences among whole genomes is based on random walk visualization and reduces the search problem to finding parts of a geometric object within a small-scale space .

Answers from top 4 papers

More filters
Papers (4)Insight
The geometric representation of DNA in the paper is the Chaos 4-dimensional Representation (C4DR) in 4-dimensional space.
The paper describes a novel 4D graphical representation method of DNA sequences, which involves using a set of N-point coordinates to represent an arbitrary DNA sequence. The geometric center of the DNA sequences is calculated using a formula.
The geometric representation of DNA in this paper is a collection of vectors in a 5-dimensional space, where nucleotides of the same type are on the same line.
The paper introduces a graphical representation of DNA sequences that arranges each nucleotide into a numerical vector and concatenates them into a zigzag curve.

Related Questions

What are the various DNA helix parameters?5 answersDNA helix parameters include roll, twist, slide, rise, inclination, tilt, propeller twist, base pair buckle, base pair roll, and base stagger. These parameters play crucial roles in determining the conformational preferences and structural characteristics of DNA, influencing interactions with proteins like transcription factors and nucleosomal proteins. Studies have shown that DNA sequences rich in specific base pairs exhibit unique local equilibrium structures and increased conformational plasticity, facilitating interactions with regulatory proteins. Additionally, the deformation of helical parameters during double proton transfer processes affects the stability and geometry of DNA structures, complicating interactions within microhelices. Understanding these helical parameters is essential for elucidating DNA-protein interactions and structural dynamics in biological systems.
How to predict 2D and 3D structure of RNA molecules?5 answersRNA structure prediction involves the prediction of both 2D and 3D structures of RNA molecules. Machine learning-based methods have been developed to predict RNA secondary and tertiary structures. These methods incorporate thermodynamic principles and modeling strategies to overcome the challenges posed by the dynamic nature of RNA structures. Physics-based prediction models, such as Vfold2D-MC and VfoldMCPX, have been developed to predict RNA secondary structures and folding stabilities. Additionally, three-site DCA methods have been developed to enhance the prediction accuracy for RNA base pairs and tertiary contacts in 2D structures. For predicting RNA 3D structures, including multiway junction structures, nucleotide- and helix-level coarse-grained models, such as RNAJP, have been developed. These models consider non-canonical base pairing, base stacking interactions, and long-range loop-loop interactions to improve structure predictions. Furthermore, deep neural networks and atomic force microscopy (AFM) images have been used to determine the 3D topological structures of conformationally heterogeneous RNA molecules.
What does dna actually look like?5 answersDNA is a complex molecule that has a double helix structure, but it can also adopt other conformations such as curved, triple-stranded, quadruple-stranded, and left-handed forms. While a single DNA molecule is too small to be seen by the naked eye, it can be visualized using special equipment like X-rays, electron microscopes, or atomic force microscopes. In most cells, DNA is compressed into a tangled package, but it still allows for meticulous gene-regulatory control. Each piece of DNA has a unique silhouette due to its curved structure at almost every base pair. DNA's physical existence is not as aesthetically elegant as the idealized double helix, but its local structural variations and nanoscale motions are critical for its functions, such as packaging, recognition, and transcription.
How can representation learning be used for dna analysis?5 answersRepresentation learning can be used for DNA analysis by converting DNA sequences into numerical vectors that capture their features. This transformation allows for the application of various machine learning techniques and models to analyze and interpret DNA data. Representation learning methods, such as natural language processing (NLP) techniques, treat DNA sequences as sentences and use embedding to convert nucleic acids or k-mers into vectors. These vectorized DNA sequences can then be used for function and structure estimation, as well as inputs for other probabilistic models. Several studies have utilized representation learning for tasks like splice site classification and gene regulation inference, demonstrating the effectiveness of this approach in DNA analysis.
How can NLP techniques be used for DNA analysis?5 answersNLP techniques can be used for DNA analysis in various ways. One approach is to use signal processing tools to process DNA data in a 1-D space, which can provide similar results to those obtained in higher dimensional spaces with lower complexity. Another approach is to apply statistical methods for gene direction analysis, genome and chromosome base composition analysis, RNA polyadenylation site analysis, allele comparison for protein domains, and protein network analysis. Additionally, digital signal processing can be used to predict gene locations and identify defect regions in DNA sequences, with the choice of numerical representation method affecting the classification performance. Furthermore, signal processing techniques, such as the Resonant Recognition Model (RRM), can be used for the analysis of biological sequences, including DNA, RNA, and proteins, with promising results in promoter recognition.
What is the explainable machine learning for classification of DNA data?5 answersExplainable machine learning techniques have been applied to classify DNA data in bioinformatics research. These techniques aim to provide insights into the decision-making process of the classification models. One approach involves using machine learning algorithms to identify classes of DNA sequences based on the sequence of nucleotides. Another approach utilizes deep learning architectures to automatically extract meaningful features from DNA sequences for classification tasks. Additionally, state-of-the-art algorithms such as Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic Models have been used in combination with feature extraction methods to achieve high accuracies in DNA classification. These methods have been applied to various datasets, including those related to viral diseases like Covid-19, AIDS, Influenza, and Hepatitis C.

See what other people are reading

How reproducible mRNA studies are?
4 answers
What are the challenges of Gaussian process regression?
5 answers
Can STARR-seq be performed on synthetic oligo libraries?
4 answers
Can STARR-seq be performed on synthetic oligo libraries?
4 answers
What is the aim of a practical on multiply sequence alignment?
5 answers
How does Bayesian metabolic flux analysis compare to traditional methods for estimating metabolic rates in vivo?
5 answers
What is known about developmental homology of behavior using brain transcriptomics?
5 answers
Developmental homology of behavior using brain transcriptomics involves understanding how gene expression patterns in the brain change during development to influence behavior. Studies have shown that distinct behaviors are associated with specific neurogenomic states in the brain, regulated by transcription factors (TFs). Changes in gene expression during brain development control proteomic diversity, including alterations in whole genes, alternate exons, and RNA editing. Additionally, comparative sociogenomics research on social insects like wasps and honeybees has revealed common molecular roots for behaviors such as foraging and division of labor, indicating conserved genetic mechanisms across different species. These findings highlight the intricate relationship between gene expression, brain development, and behavioral outcomes.
What are the most commonly used pH level descriptors in scientific research?
5 answers
In scientific research, the most commonly used pH level descriptors include traditional methods like glass electrodes and emerging techniques such as ion-sensitive field transistors, pH imaging, conductometric, and spectroscopy. These methods have evolved from the early days of electrode-based measurements to non-invasive probes that can measure hydrogen ion concentration and electrical conductivity simultaneously, catering to various fields like water analysis, agriculture, and electrochemistry. Additionally, the repeatability of pH measurements using glass electrodes is excellent, making it a preferred choice for many laboratories. Furthermore, recent developments in spectroscopic methods and miniaturization efforts, particularly in biomedical applications for skin and bio-fluids, are enhancing pH measurement capabilities and expanding the scope of pH-related research.
What is the effectiveness of KNN in sentiment analysis compared to other machine learning algorithms?
5 answers
The effectiveness of K-Nearest Neighbor (KNN) in sentiment analysis has been extensively studied across various domains. In sentiment classification tasks, KNN has shown remarkable performance. Research has demonstrated that KNN, when applied to sentiment analysis tasks, achieved high accuracies ranging from 91% to 98.4%. In comparison to other machine learning algorithms such as Naive Bayes, SVM, Decision Tree, and Random Forest, KNN consistently outperformed them in terms of accuracy, with KNN achieving an accuracy of 98.4% and an AUC score of 98.8%. This highlights the robustness and effectiveness of KNN in sentiment analysis tasks, making it a competitive choice among various machine learning algorithms for sentiment classification.
How to use GANs to generate biologically functional DNA sequences from numbers?
5 answers
To generate biologically functional DNA sequences from numbers using Generative Adversarial Networks (GANs), one can convert handwritten numbers into DNA sequences and train GANs to generate new DNA sequences based on this artificial information. GANs have been successfully employed in biological sequence analysis to handle data imbalance issues by generating synthetic data that closely resembles real data, thus improving machine learning models' classification performance. Additionally, generative neural network methods have been proposed to generate DNA sequences with desired properties, such as creating synthetic DNA sequences using GANs and a DNA-based variant of activation maximization design method, which capture important structures of the data and can be applied to designing probes for protein binding microarrays. These approaches showcase the potential of GANs in generating biologically functional DNA sequences from artificial information and advancing genomics research.
How to use GANs to generate biologically functional RNA sequences from numbers?
5 answers
To generate biologically functional RNA sequences from numbers using Generative Adversarial Networks (GANs), one can follow a methodology similar to converting handwritten numbers into DNA sequences. By training GANs to convert numerical data into RNA sequences, synthetic RNA sequences can be generated that closely resemble real data. This approach leverages the power of GANs to create new sequences based on the input numerical information, showcasing the potential of neural networks in generating novel biological sequences. Additionally, utilizing GANs for data augmentation in transcriptomics has shown significant improvements in classification performance, indicating the effectiveness of GAN-based strategies in enhancing datasets for predictive modeling. By combining GANs with neural network architectures like SANDSTORM, one can predict and generate functional RNA sequences with improved performance and functionality, showcasing the versatility and power of these technologies in RNA sequence design.