10.2 Visualizing biological sequences, structures, and networks
5 min read•august 14, 2024
Visualizing biological data is crucial for understanding complex systems. This section covers techniques for representing sequences, structures, and networks. From to 3D protein models, these tools help researchers analyze and interpret diverse biological information.
Interactive visualizations and multi-layered approaches are key for exploring large datasets. By integrating different data types, scientists can gain deeper insights into biological processes, from molecular interactions to system-wide behaviors. These techniques are essential for modern computational biology research.
Sequence Visualization Techniques
Sequence Logos and Alignment Plots
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- Sequence logos graphically represent the consensus sequence and diversity of aligned sequences
- Height of each letter corresponds to its relative frequency at that position
- Can be generated using tools like WebLogo or the Logomaker Python package
- Take a set of aligned sequences as input and output the corresponding sequence logo
- display pairwise alignments between sequences
- Highlight regions of similarity, gaps, and mismatches
- Can be created using libraries like Matplotlib in Python or ggplot2 in R
- Input is a set of aligned sequences
- Output is a graphical representation of the alignments
- are another common visualization for sequence alignments
- Each cell in the matrix represents the similarity score between a pair of sequences
- Color intensity indicates the level of similarity
Heatmaps and Other Sequence Visualizations
- Heatmaps visually represent the similarity between sequences in an alignment
- Each cell in the matrix corresponds to a pairwise comparison between sequences
- Color intensity of the cell indicates the degree of similarity (darker = more similar)
- Can be created using libraries like Seaborn in Python or pheatmap in R
- Input is a similarity matrix calculated from the aligned sequences
- are a simple way to visualize sequence similarity and identify repeats
- Two sequences are plotted on the x and y axes
- Dots are placed at positions where the sequences match
- Diagonal lines indicate regions of continuous similarity (conserved regions)
- Dots scattered across the plot suggest repetitive elements or low complexity regions
- , such as Circos plots, can display multiple sequence features and relationships
- Sequences are arranged in a circular layout
- Connections between regions indicate sequence similarity, synteny, or other relationships
- Additional tracks can display features like GC content, gene density, or functional annotations
Protein and Nucleic Acid Structure Visualization
PyMOL and Chimera for 3D Structure Visualization
- and are popular software tools for visualizing and analyzing 3D structures of proteins and nucleic acids
- Can load structure files in various formats (PDB, mmCIF)
- Contain atomic coordinates and other structural information
- Provide a wide range of visualization options
- Ribbon diagrams, surface representations, atom-level displays
- Can be customized to highlight specific features of interest
- Support scripting and automation using languages like Python
- Allows for creating complex visualizations and performing structural analyses programmatically
- Can load structure files in various formats (PDB, mmCIF)
- PyMOL and Chimera enable simultaneous visualization and comparison of multiple structures
- Identify conserved regions, structural differences, and potential binding sites
- Useful for studying protein families, evolutionary relationships, and structure-function relationships
Structural Motifs and Interaction Interfaces
- are recurrent patterns in protein and nucleic acid structures
- Examples include alpha helices, beta sheets, and loops in proteins; hairpins and junctions in RNA
- Can be visualized using secondary structure representations in PyMOL or Chimera
- Cartoon or ribbon diagrams highlight the overall fold and secondary structure elements
- Motif-specific coloring or selections can emphasize the occurrence and distribution of motifs
- are regions where proteins or nucleic acids make contact with other molecules
- Can be visualized using surface representations in PyMOL or Chimera
- Coloring by properties like hydrophobicity, electrostatic potential, or sequence conservation
- Identifying interaction interfaces is crucial for understanding molecular recognition and function
- Protein-protein interactions, protein-ligand binding, protein-DNA/RNA interactions
- Interface residues can be highlighted using selections or custom coloring schemes
- Helps pinpoint key residues involved in binding or catalysis
- Can be visualized using surface representations in PyMOL or Chimera
Biological Network Visualization
Graph-Based Representations of Networks
- and pathways can be represented as graphs
- Nodes represent biological entities (genes, proteins, metabolites)
- Edges represent interactions or relationships between entities
- Graph-based visualizations can be created using libraries like in Python or in R
- Provide functions for constructing, manipulating, and analyzing graph objects
- Common graph layouts for biological networks
- (Fruchterman-Reingold): simulate repulsive and attractive forces between nodes
- Circular layouts: arrange nodes in a circle, useful for visualizing cyclic processes or hierarchical relationships
- : organize nodes in layers based on their properties or relationships
- Node and edge attributes can encode additional information
- Color, size, shape of nodes: expression levels, functional annotations, molecular type
- Edge width, style, color: interaction types, confidence scores, directionality
Interactive Exploration of Biological Networks
- Interactive graph visualizations allow users to explore and navigate large biological networks
- Created using libraries like or
- Enable zooming in on specific regions of interest
- Provide access to additional information through tooltips or linked views
- Network exploration techniques
- Panning and zooming: navigate the network by moving the viewport and changing the zoom level
- Node and edge selection: highlight specific nodes or edges of interest, display their properties
- Filtering and searching: focus on subsets of the network based on attributes or search criteria
- Network analysis and visualization can be combined to identify key features
- (degree, betweenness): highlight important nodes or hubs
- : identify densely connected modules or functional groups
- : find the most direct routes between nodes of interest
Integrating Data for Biological Systems Visualization
Multi-Layered Visualizations of Biological Systems
- Integrating multiple data types provides a comprehensive understanding of biological systems
- Sequence data, structural data, network data
- Tools like Cytoscape and enable the creation of
- Combine different data types and represent them in a unified visual framework
- Examples of multi-layered visualizations
- Protein-protein interaction network overlaid with gene expression data
- Identify highly expressed hub proteins
- Protein-protein interaction network overlaid with structural data
- Map interaction interfaces and potential drug targets
- Protein-protein interaction network overlaid with gene expression data
- Pathway databases (, ) provide curated collections of biological pathways
- Integrate multiple data types (metabolic, signaling, regulatory pathways)
- Visualize the interconnectivity and cross-talk between different biological processes
Visual Analytics for Integrated Biological Data
- approaches enable interactive exploration and querying of integrated biological datasets
- : highlight relationships and patterns across multiple data dimensions
- Selecting a subset of data in one view updates the corresponding elements in other views
- : display different aspects of the data simultaneously
- Example: scatter plot of gene expression, linked to a network view and a heatmap of functional annotations
- : highlight relationships and patterns across multiple data dimensions
- Effective visual representations of integrated biological data require careful consideration
- and normalization: ensure comparability across different data types and scales
- Visual encoding choices: select appropriate visual variables (color, size, position) to represent data attributes
- Interpretability and clarity: avoid visual clutter, provide clear legends and annotations
- Visual analytics tools for biological data
- : integrates multiple omics data types (gene expression, copy number variation, DNA methylation)
- : visualizes and compares mutation patterns across cancer samples and genes
- : summarizes and visualizes multi-dimensional data from biological experiments
Key Terms to Review (33)
3D Structure Visualization: 3D structure visualization refers to the techniques and tools used to create three-dimensional representations of biological macromolecules, such as proteins and nucleic acids. These visualizations allow researchers to understand the spatial arrangement of atoms, secondary structures, and overall shape, which are crucial for studying molecular interactions, functions, and dynamics. By providing a clearer view of complex biological entities, 3D structure visualization facilitates insights into how these molecules operate within biological systems.
Alignment plots: Alignment plots are graphical representations used to visualize the similarities and differences between biological sequences, such as DNA, RNA, or protein sequences. These plots allow researchers to see how closely related different sequences are by displaying them in a way that highlights conserved regions, gaps, and mutations. They play a crucial role in comparative genomics, evolutionary studies, and protein structure analysis.
Biological networks: Biological networks refer to complex systems of interactions among various biological entities, such as genes, proteins, and metabolites, that are critical for understanding cellular functions and biological processes. These networks can represent various relationships, including regulatory interactions, metabolic pathways, and protein-protein interactions, and are often visualized to analyze and interpret the underlying biological information. By studying these networks, researchers can gain insights into the organization of biological systems and their responses to different conditions.
Brushing and Linking: Brushing and linking is an interactive data visualization technique used to highlight relationships between different data elements in a visual representation. This method allows users to select or 'brush' a specific subset of data points, which then 'links' to other visualizations, updating them to reflect the selections made, thus providing a cohesive understanding of the biological data being analyzed.
Caleydo: Caleydo is a visualization tool designed to help researchers analyze and interpret complex biological data through interactive visual representations. It integrates various types of biological information, such as genomic sequences, protein structures, and molecular networks, allowing users to explore relationships and patterns within the data effectively. By enhancing the visual understanding of biological systems, Caleydo aids in hypothesis generation and data-driven decision-making.
Centrality Measures: Centrality measures are metrics used in network analysis to determine the relative importance or influence of nodes within a network. These measures help identify key players or critical points in biological networks, such as protein-protein interaction networks or metabolic pathways, enabling researchers to understand how information flows through the system and which components are most crucial for function.
Chimera: A chimera refers to an organism that contains cells with different genotypes, often resulting from the fusion of multiple zygotes or genetic manipulation. This concept is crucial in understanding various biological phenomena such as genetic diversity, tissue compatibility, and developmental biology, as it allows researchers to visualize and analyze how these differences manifest in biological sequences and structures.
Circular visualizations: Circular visualizations are graphical representations that arrange data in a circular format, allowing for a comprehensive view of complex relationships within biological data. This approach is particularly effective for displaying large datasets, such as genomic sequences or molecular structures, and it helps reveal patterns and connections that may not be immediately apparent in traditional linear formats.
Community detection algorithms: Community detection algorithms are computational methods used to identify groups or clusters within networks where nodes are more densely connected to each other than to the rest of the network. These algorithms are essential for analyzing complex biological networks, such as protein-protein interaction networks and gene co-expression networks, as they help reveal the underlying organization and functional modules within biological systems.
Coordinated multiple views: Coordinated multiple views is an approach in data visualization that allows users to analyze and compare different representations of the same data simultaneously. This method enhances understanding by linking visualizations, enabling users to see relationships and trends across various perspectives, such as biological sequences, structures, and networks.
Cytoscape.js: Cytoscape.js is an open-source JavaScript library designed for visualizing and analyzing complex networks and biological data. It allows researchers to create interactive graphs to represent biological sequences, structures, and networks, facilitating a better understanding of relationships within biological systems. With its robust functionality, Cytoscape.js can manage large datasets while providing features such as styling, layouts, and user interactions.
Data normalization: Data normalization is the process of adjusting and scaling data values to bring them into a common format, which can improve the accuracy and efficiency of various analyses. By transforming data to a standard range or distribution, it enhances the performance of algorithms used in supervised learning, ensures effective visualization in biological contexts, and aids in producing consistent and informative figures.
Data preprocessing: Data preprocessing is the process of cleaning, transforming, and organizing raw data into a format that is suitable for analysis and interpretation. This step is crucial in ensuring the quality and accuracy of data, as it helps to remove noise, fill in missing values, and standardize formats, ultimately improving the reliability of subsequent analyses, including the visualization of biological sequences, structures, and networks.
Dotplots: Dotplots are graphical representations used to visualize the similarities and differences between biological sequences, such as DNA, RNA, or proteins. They display dots at coordinates corresponding to matching elements in the sequences, making it easy to identify regions of alignment and divergence. This method is particularly useful for comparative analysis, allowing researchers to quickly assess the relationship between multiple biological sequences.
Force-directed layouts: Force-directed layouts are a type of graph drawing algorithm used to visualize complex networks by modeling them as physical systems, where nodes represent entities and edges represent relationships. This method uses forces, such as attraction and repulsion, to position nodes in a way that reflects their connections, making the structure of the data easier to understand. It is especially useful for visualizing biological networks, as it allows for an intuitive representation of interactions within biological sequences and structures.
Gephi: Gephi is an open-source network visualization software used for analyzing and visualizing complex networks and graphs. It allows researchers to create interactive representations of data, which can reveal hidden patterns, structures, and relationships in various biological contexts such as protein-protein interactions and biological sequences. Its user-friendly interface and powerful analytical tools make it a popular choice in computational biology for examining interconnected data.
Graph-based representations: Graph-based representations are mathematical structures used to model pairwise relationships between objects, represented as nodes (or vertices) and the connections between them as edges. These representations allow for efficient visualization and analysis of complex biological data, such as sequences, structures, and networks, facilitating a clearer understanding of relationships and interactions within biological systems.
Heatmaps: Heatmaps are graphical representations of data where individual values are represented by colors, allowing for quick visual interpretation of complex datasets. In biological research, heatmaps help visualize relationships between biological sequences, structures, and networks by displaying the intensity of different attributes, such as gene expression levels or protein interactions, across various conditions or samples.
Hierarchical layouts: Hierarchical layouts are visual representations that organize information in a tree-like structure, showing relationships and levels of importance among various elements. This style of visualization is particularly useful for depicting complex biological sequences, structures, and networks, as it allows for clear representation of the hierarchy and connections between different biological entities, like genes, proteins, or cellular components.
Icomut: icomut is a computational tool designed to visualize and analyze mutations in biological sequences, particularly in the context of genomic data. It allows users to display mutation data in a clear and interactive manner, facilitating the understanding of how genetic variations relate to biological functions and phenotypes. By integrating diverse datasets, icomut helps researchers identify patterns of mutations across different samples and connect them to specific biological processes.
Igraph: igraph is a powerful software package and library used for creating and analyzing graphs and networks in various fields, including computational biology. It enables users to visualize complex biological structures, sequences, and interactions through graphical representations, making it easier to understand relationships and patterns in biological data.
Interaction interfaces: Interaction interfaces refer to the regions on biological molecules where molecular interactions occur, such as binding sites for proteins, nucleic acids, or small molecules. These interfaces play a crucial role in understanding how biological sequences and structures interact with each other, influencing various biological functions and processes.
KEGG: KEGG, or the Kyoto Encyclopedia of Genes and Genomes, is a comprehensive database resource that integrates genomic, chemical, and systemic functional information. It plays a crucial role in understanding biological functions and systems by providing a framework for analyzing gene functions and metabolic pathways.
Multeesum: Multeesum is a mathematical function used in computational biology to aggregate or summarize multiple values into a single score or metric, often applied in the context of biological sequences, structures, and networks. It helps in visualizing complex biological data by simplifying it into more interpretable forms. This concept can be crucial for understanding the overall behavior or characteristics of biological systems based on individual components.
Multi-layered visualizations: Multi-layered visualizations are graphical representations that integrate multiple datasets or dimensions of information, allowing for a comprehensive view of complex biological data. These visualizations enhance the understanding of relationships among biological sequences, structures, and networks by presenting various layers of information simultaneously, often using color coding, interactive features, and different graphical styles to convey intricate details and patterns.
Networkx: Networkx is a Python library used for the creation, manipulation, and study of complex networks and graphs. It provides tools to visualize and analyze the relationships between entities, making it a vital resource for exploring biological sequences, structures, and networks in computational biology. By enabling the representation of biological data as graphs, it allows researchers to uncover patterns and interactions within large datasets.
Omix: Omix refers to a collection of biological data types and their associated analysis methods, typically involving high-throughput technologies for studying various biological systems. This term is often used to describe fields such as genomics, transcriptomics, proteomics, and metabolomics, which analyze different biological molecules at scale. Omix serves as a foundational concept for understanding how these diverse data types can be integrated to visualize complex biological sequences, structures, and networks.
Pymol: PyMOL is a powerful molecular visualization system that allows users to create high-quality 3D images of biological macromolecules, such as proteins and nucleic acids. It provides tools for both the visualization and analysis of molecular structures, making it an essential resource for scientists in the fields of computational biology and structural biology. PyMOL supports various file formats, allowing seamless integration with other software and databases, which enhances its utility in understanding biological sequences and structures.
Reactome: Reactome is a curated, open-access database that provides detailed information about biological pathways and processes in human biology. It serves as a vital resource for researchers looking to understand how genes and proteins interact within complex networks and how these interactions contribute to cellular functions, disease mechanisms, and therapeutic interventions.
Sequence Logos: Sequence logos are graphical representations that depict the frequency and conservation of nucleotides or amino acids at specific positions within a sequence alignment. They effectively illustrate the importance of each position in a biological sequence, showing how often each character appears, which is crucial for identifying conserved motifs or functional elements in proteins and DNA. This visualization allows researchers to quickly understand patterns and variations, making them invaluable in both protein sequence analysis and the exploration of biological networks.
Shortest Path Analysis: Shortest path analysis is a computational method used to determine the shortest possible route between two points in a graph, which can represent various biological structures or networks. This technique helps identify the most efficient pathways in biological systems, such as the shortest distance between genes, proteins, or other entities within a network. By optimizing these connections, researchers can gain insights into the relationships and interactions that define biological processes.
Structural motifs: Structural motifs are specific arrangements or patterns of secondary structural elements in proteins, such as alpha helices and beta sheets, that contribute to the overall three-dimensional shape of a molecule. These motifs are essential for understanding how proteins fold and function, as they often play critical roles in the stability and interactions of biomolecules.
Visual analytics: Visual analytics is an interdisciplinary field that combines data analysis with interactive visualizations to help users gain insights from complex data sets. It emphasizes the importance of visual representations in understanding patterns, trends, and relationships within data, which is crucial for fields such as computational biology, where large and intricate biological sequences, structures, and networks are analyzed.