Molecular visualization tools bioinformatics

Reactome gives quite a different picture of the pathway, embedding it within its neighbors. This extended visualization can be very useful in providing a more complete picture of differential expression events. It, however, can also be distracting, if the pathway map gets too large, and therefore, too crowded. The input format is simple, and KEGG Mapper accepts several identifiers; Reactome even does identifier conversion on the fly.

Reac-tome moreover offers a variety of additional information on genes, such as the expression of a gene in different tissues. Both tools are easy to use for beginners and thus represent a good starting point for rendering expression data on pathways.

While the learning curves for the latter programs are steeper, both tools offer useful functionalities, such as looping over conditions KEGGViewer or mapping several conditions on the same pathway map PathVisio. In general, each pathway visualization tool has advantages and disadvantages. While we give examples only for a very small collection of available pathway mapping tools, we recommend to try out several methods and to regularly look for novel developments in the field of pathway visualization.

It is noteworthy to mention that many software packages for in silico metabolic modeling also allow users to visualize and analyze molecular pathways and networks with -omics data. Depending on the complexity of the research question, however, such tools might offer more functionality.

Other than pathways, which contain information on directionality of reactions, as well as on small compounds, protein interaction networks are a collection of nodes proteins connected to each other via edges. Biological interaction networks are typically created for one organism and contain information on all known biological interactions within the cell, whereby genetic and physical interactions are collected separately.

Biological networks, though being less detailed in information than pathways, are nevertheless useful for analyzing and visualizing -omics data. The most popular tools for network analysis are Cytoscape 36 and Gephi. Cytoscape and Gephi are able to handle diverse datasets, which can be biological or not. However, Cytoscape tends to be biased toward biological data analysis. Additionally, Cytoscape is extendible via community developed plugins 42 that range from Gene Ontology enrichment 43 to clustering to pathway visualization and analysis.

Similarly, the Reactome FI Functional Interaction plugin 47 finds pathways and network patterns related to cancer and other diseases using Reactome data. Its handling is very easy and comparable to the web-based visualization application of Reactome. CluePedia 48 is a search tool for new markers potentially linked to known pathways. It is one of the tools that offer the potential to extend our existing pathway definitions.

Gephi, on the other hand, tends to focus on network visualization. It is therefore able to generate powerful visualizations.

Like Cytoscape, Gephi can be extended via plugins. To illustrate the visualization capabilities of both, Cytoscape and Gephi, we collected molecular interactions from VirHostNet 2. We created a network of 7, nodes and , edges composed of human and influenza A virus proteins.

We further created a subnetwork with influenza A interactors and their first neighbors, which resulted in a network of 3, nodes and 19, edges. We applied several force-directed layout algorithms to those networks Figure 2. We chose force-directed layouts as they rely on the structure of the network and thus do not require domain-specific information. These layout types furthermore are visually more appealing as they reduce edge crossing and reveal symmetries within the graph. To visualize a large network is generally problematic.

We found the Fruchterman Reingold layout as the most appealing, as it provides some form of structure for very large networks. All chosen layout options are separating the small network in several, more tightly connected clusters, with the Spring layout performing best with respect to visual separation of subclusters. Notes: The entire human and influenza A interactomes are shown, as well as the first neighbors of the influenza A proteins.

Influenza A proteins are colored in orange, and human nodes are shown in green. The size of the nodes indicates the connectivity of the individual proteins.

While Cytoscape and Gephi are popular tools, there is a broad spectrum of alternatives available. For example, sigma. Additionally, software packages such as iGraph 52 and NetworkX 53 further support network analysis and visualization.

Pathway resources are annotated and curated separately. It is due to this fact that heterogeneous data models, formats, and application programming interfaces APIs are used. This makes the development of pathway visualization and analysis tools as well as the aggregation of pathway data an arduous task. Fortunately, the community using the pathway resources came together and launched the Biological Pathway Exchange BioPAX project 54 with the aim to define a standard for sharing pathway information.

It encapsulates the semantics related to pathways by using controlled vocabularies such as the Gene Ontology 55 and the Proteomics Standards Initiative Molecular Interaction 56 vocabularies, as well as its own community-defined constructs. Several tools have been developed to read and process BioPAX files. These include libraries like paxtools, 57 rBiopaxParser, 58 and BioPAX-pattern, 59 and visualization and analysis tools like Cytoscape and cyPath2, 60 among others.

SBGN is a community-developed, standard graphical language for unambiguously representing diverse biochemical and cellular events. One of the key aspects of SBGN is to minimize the effort needed to understand a pathway. It does so by limiting the number of symbols and reusing them when possible. However, the current standards are still maturing and so not fully adopted. An API is a particular set of guidelines and specifications that software applications can follow to communicate with each other.

It provides an interface between different applications facilitating their interaction. The combination of different APIs allows developers to create new applications, visualizations, and services 9 , 44 , 46 , 62 with use cases beyond the scope of a given data type or data source.

Most pathway data sources 1 — 3 , 5 , 7 , 44 , 63 provide data access methods such as I return relevant information about a given pathway list of participants, list of complexes, etc , II search for a keyword or pathway in a given database, III convert identifiers from one format to another, and IV fetch pathway maps as plain bitmap images or, in some cases, as XML files.

Additionally, a few APIs provide methods for data analysis. For example, through the PANTHER pathway API, it is possible to run an overrepresentation test: by comparing a list of genes to a reference list, the statistical over- or underrepresentation of selected categories is determined eg, function, process, cellular location, protein class, or pathway.

Similarly, Reactome recently developed an API devoted solely to data analysis. The amount of data produced by the different -omics fields is rapidly growing, mainly due to the constant development of high-throughput methods. Given the fast rate at which vast amounts of data are produced, the need for new data visualization and analysis methods is undisputed.

There are, however, a series of challenges that need to be overcome. Standard formats allow biological data to be easily exchanged, manipulated, aggregated, and analyzed. Different communities, like the Proteomics Standard Initiative 67 and BioPAX, are addressing that need, for proteomics and biological pathways, respectively. However, data integration is still challenging. For instance, mapping between different identifiers such as protein, gene, transcript, or clone is perhaps the most common operation a bioinformatician has to perform.

Though there are several tools for mapping identifiers, 68 — 70 significant manual effort is still required. Further development of tools and models able to link and aggregate datasets from various sources and types is crucial to enable detailed analysis and rich visualizations. Traditionally, networks or graphs are visualized in node-link diagrams, where nodes represent entities and edges represent relationships.

Such a representation is intuitive and works well for small networks, but as soon as the network size increases, so does the visualization complexity. Different techniques have been developed to try and enhance such visualizations: node filtering, edge bundling, 71 , 72 different layout algorithms, 72 — 75 and edge lenses are currently available in the toolbox. However, the traditional node-link visualization is limited when applied to large networks.

Alternative network visualizations such as matrix diagrams 74 , 75 and 3D models 76 — 79 are exiting steps towards a hairball-free visualization and may lead to much more interesting visualization techniques than the traditional node-link diagrams.

Matrix diagrams 80 , 81 are based on the adjacency matrix of a given graph. The main advantage of this kind of visualization is that line crossings are impossible, which leads to a clear visualization. However, the effectiveness of a matrix diagram is heavily dependent on the order of rows and columns. Therefore, patterns may be hidden due to a nonoptimal clustering or ordering algorithm. Adding an additional dimension enhances the understanding of the topology of a large network.

Hence, with the conformation or structure predicted, the function of any unknown protein can also be predicted with similarity search techniques. The relationship between sequence and function is primarily concerned with understanding the 3-D folding of proteins and inferring protein function from these 3-D structures.

Molecular visualization helps the scientists to bioengineer the protein molecules. User-friendly graphic interface makes this area of Bioinformatics a full filled, scientific thrill to the bioscientists. Tools for molecular visualization:. This standalone software can be downloaded from the RasMol homepage: www. As mentioned above, several viewers for examining PBD files are available. The most popular one is RasMol. RasMol represents a breakthrough in software-driven three-dimensional graphics, and its source code is a recommended study material for anyone interested in high-performance three-dimensional graphpics.

RasMol treats PDB data with extreme caution and often recomputes information, making up for inconsistencies in the underlying database. It does not try to validate the chemical graph of sequences or structures encoded in PDB files.

RasMol does not perform internally either dictionary-based standard residue validations or alignment of explicit and implicit sequences. RasMol 2. For instance, it is functional in various formats of data automatically. Moreover, it can collect data sequentially from the web page.

It efficiently can handle millions of datasets in a reasonable time; moreover, it produces high-quality MSAs. In this Linux bioinformatics tool, there is a process where the user requires leaving the file sequence in the default mode. That gets aligned and clustered to generate a guide tree, and that ultimately allows forming a progressive alignment sequence. Clustal Omega. It can find relevant matches between nucleotide and protein sequences and show the statistical importance of it.

What is more, this tool is largely cultivated thriving unknown genes in various animals, and it lets mapping out sequence-based datasets through qualitative analysis. Bedtool bioinformatics software is a Swiss army knife of tools used for far ranges of genomic analysis. Genomic arithmetic uses this tool very widely that implies it can find the set theory with it.

Get Bedtools. Bioclipse Linux bioinformatics tool that is defined with workbench for life science is a java based open-source software. It works on the visual platform that includes chemo and bioinformatics Eclipse Rich Client Platform. It is featured with a plugin architecture. That implies the state of the art plugin architecture moreover, functionality and visual interfaces from Eclipse, such as help system, software updates also included.

Get Bioclipse. Bioinformatics used extensively in the Linux platform is an open-source and free bioinformatics tool, coherently used in medical biology for high-throughput analysis. It mainly uses statistic R programming; nevertheless, it also contains another programming language as well. This software is designed by focusing on a couple of objectives; for instance, it aims to establish a collaborative development and to ensure of using innovative software immensely.

Get Bioconductor. More importantly, it works to create information between phylogenetic and met genetic datasets. Anduril is open source components-based bioinformatics software for Linux that works for creating a workflow framework regarding scientific data analysis. This bioinformatics tool for Linux is designed to enable efficient, flexible, and systematic data analysis, particularly in the biomedical research field.

Get Anduril. LabKey Server is a preferred choice for the scientists used in the laboratories to integrate research, analyze and share biomedical data. A secure data repository is used in this tool that facilitates web-based querying, reporting, and collaborating within a far range of databases.

Along with the given underlying platform, many more scientific instruments can be added in this application. And bioinformatics tools have come to the rescue here founder taking such genomic test. Genomic testing is performed to study mutations in a gene. Mutations indicate the presence of disorders and diseases, sometimes as deadly as cancer. Genomic sequencing tests identify differing levels of expression in a group of genes to understand their interactions.

Test genomic analysis is thus critical for understanding the activity of genes and making an accurate prognosis. Genomic tests are a kind of medical tests done to rule out the possibility of a person having or developing a medical condition.

These types of genomic testing are performed by geneticist or genetic counsellors:. Diagnostic testing is used for identifying genetic conditions that an individual may have. These are based on clinical presentations prepared by clinicians to confirm an initial diagnosis.

This kind of genomic test is done by checking an allele or a specific gene variant associated with a particular kind of disease.

Bioinformatics tools here help with sequencing the genomes for further analysis. Clinical predictive kind of genomic testing is done to see if or not an individual is susceptible to a certain disorder. Open source and free bioinformatics tools here help with examining the causative gene variant for targeted tests.

Open source and free bioinformatics tools for Linux help with pharmacogenomic testing by studying the genomic determinants of different drug responses.

This test genomic analysis helps assess whether a particular medicine would be effective. Tumour testing helps with sequencing of DNAs to study the mutations within them. Bioinformatics tools are deployed here to do tumour testing. This type of genomic testing is quite important when it comes to diagnosing cancers and tumours.

Genome-wide analysis and testing is critical for testing, monitoring and preventing diseases across populations. Next generation genome sequencing technology undertakes parallel sequencing of DNA fragments for an efficient genomic analysis. These tests are performed using samples of hair, blood, tissue, amniotic fluid or skin. Some of the most advanced methods today for genomic testing are:. Genomic testing has evolved newer advanced methods for analyzing the different health risks in new born.

This is one area where genomic testing and medicine has advanced well and let us have a look here how:. This kind of genomic testing studies gene mutations to identify defects that may emerge either after birth or during later stages in life.

People with increased risk of suffering from a genetic disorder are administered this type of genomic testing. Carrier testing assesses people who have more than one copy of genetic mutation. The process makes it possible to ensure whether the child would have any genetic disorder.

Chromosomal tests analyze long length DNAs and whole chromosomes to find out if there are massive genetic changes. Bioinformatics software solutions are ideal for genomic testing or next-generation sequencing. Next-generation sequencing technology is used to study mutations in genes for predicting the nature of a disease. If we speak about this technology in the Indian context, you would find that India has started genome sequencing of COVID using bioinformatics tools.

Covid has protein binding on its membrane and is primarily an RNA virus. It is because of these components that the virus has acquired the character it has and is fast mutating.