Bioinformatics, a field of life sciences devoted to the interpretation and analysis of biological data using computational techniques and tools, has evolved massively in recent years due to the explosive growth of biological information generated by the scientific community. Biologists are stepping up their efforts in understanding biological processes by using a variety of experimental and bioinformatics methods.
Biological Data Analysis
All the advancements in the field of bioinformatics has resulted in a flood of biological and clinical data, which could be overwhelming for researchers to handle without appropriate data processing and analysis tools, especially when there is a lack of training or no knowledge of programming, statistics, and modeling. Thus, custom data analysis services have become increasingly important in biosciences and can certainly help accelerate the research cycle.
Factors affecting the rate of Biological Data Analysis
Following are the factors that may cause affect the time period to solve big biological data analysis problems:
Sequencing technologies to produce biological data are prone to errors. Thus high complexities will be introduced into algorithms in order to handle these errors and uncertainties.
Due to inherent algorithmic complexities, many biological data analysis problems are both data-intensive and compute-intensive. HPC may provide an efficient tool to solve these problems.
Big biological data analysis problems have very high computational requirements even the corresponding algorithms have polynomial time complexities.
Biological Data Visualization
It is a sub-branch of Bioinformatics that deals with the applications of computer graphics, information visualization, and scientific visualization of the life sciences.
The methods and tools for visualizing biological data have improved considerably over the last decades, but they are still inadequate for some high-throughput data sets. For most users, a key challenge is to benefit from the deluge of data without being overwhelmed by it. This challenge is still largely unfulfilled and will require the development of truly integrated and highly usable tools. This includes visualization of genomes, sequences, phylogenies, alignments, system biology, magnetic visualization imaging (MRI) data, microscopy, and molecular structures data. Many web-based and stand alone software systems and tools are available for biological data visualization.
Tools for molecular graphics visualization & analysis
Jmol - A free and open source stand alone java applet that supports advanced capabilities such as loading multiple molecules with independent movement, surfaces and molecular orbitals, cavity visualization, crystal symmetry.
PyMol - An open source Python application which provides publication quality images of the biological macromolecules.
Cn3D - A free, open source stand alone program used for the visualization and analysis of biological macromolecules.
Molecular Operating Environment (MOE) - A closed source program that is used to build, edit and visualise small molecules, macromolecules, protein-ligand complexes, crystal lattices, molecular and property surfaces. Platform for extensive collection of molecular modelling / drug discovery applications.
RasMol - An open source stand alone program that is available free for the public use and assists in visualizing and analyzing the biological macromolecules of interest.
UCSF Chimera - A closed source, non-commercial Python-based program that includes single/multiple sequence viewer, structure-based sequence alignment, automatic sequence-structure crosstalk for integrated analyses.
Phylogenetic tree visualization tools
ITOL - Interactive Tree Of Life - annotates the trees with various types of data and exports the data to various graphical formats; scriptable through a batch interface.
FigTree - A simple Java tree viewer that is able to read newick and nexus tree file formats and can be used to color the branches of the phylogenetic tree and produce vector artwork.
MEGA - A software for statistical analysis of molecular evolution. It includes different tree visualization and tree generating features. It provides multiple options to create phylogenetic trees based on various algorithms, i.e., UPGMA, ML, MP, etc.
Biological Data Analysis & Visualization
Biological data visualization & analysis includes:
Identification of Biomarkers - it involves a wide range of data types from ChIP-Seq, RNA-Seq, miRNA sequencing, 4C-Seq, microarray and mass spectroscopy experiments for the rapid identification and validation of biomarkers.
Image analysis - it involves the image analysis of biological systems on various scales, from the structure of biomolecules, up to cells and whole organs based on the microscopy techniques.
Structural biology - a huge amount of data can be generated from structural biology projects. Various bioinformatics tools help in analysis and model reconstruction of high resolution macromolecular structures (and their complexes) from X-ray crystallography, NMR, and EM data. Model quality assessment and refinement are also performed to evaluate and ensure reliability.
Statistical data analysis and programming - it involves the statistical analysis of biological data using various statistical methods such as testing, regression, clustering, classification, error rates, resampling, quality control, outlier detection, and programming are developed and applied to a wide range of topics in biological research, including data coding and management, comprehensive univariate and multivariate analyses, large healthcare data analytics, etc.
Biological modeling - it represents the systematic reconstruction and analysis of biological pathways and networks from observed data using methods such as the graph-theoretic approaches. Exploration of the behavior of networks, integration of prior knowledge, and differential analysis in the context of integrated experimental data.
Data visualization - it is an integral part of biological sciences. To meet the challenge of rapid increase in data volume and complexity, various state-of-the-art software tools are available for visualization of sequences, alignments, phylogenies, microarray, macromolecular structures, networks, and many others.
Libraries for Biological Data Visualization
Data visualization is an essential part of the process for being able to explore data and communicate results. To make data visualization more easier and powerful, many new Python data visualization libraries have been created in the past few years, such as:
Pandas - a data manipulation library - most of its functionality concentrates on sorting, filtering, cleaning and transforming data. Pandas is useful for all sorts of data manipulation tasks, from very simple things like dealing with missing data to tricky things like reshaping multidimensional data and resampling time series data. We can also use pandas to create simple charts, but that's not its main purpose.
Vispy - is targeted to visualise very large datasets. Vispy is designed to take advantage of modern graphics cards. A glance at the gallery page will illustrate how it is different from matplotlib; examples taken mainly from mathematics and physics with no conventional charts. To sum-up, this library is least likely to be useful in biology.
Matplotlib - a general purpose plotting library which is a fairly low level library - means that it gives a great deal of control over plots, and allows to make all sorts of exotic chart types, but requires a fair amount of code to do so. It provides various built-in functions for creating common chart types, but also allows drawing directly with primitive shapes (circles, lines etc.). It also works for making animations. Many other tools rely on matplotlib; for example, both pandas and seaborn actually use matplotlib internally to draw their charts.
Seaborn - provides “statistical data visualisation”, means that it's very good at looking at distributions of things and relationships between values. Seaborn provides good visualisation practise, i.e., styles are very clear and colour schemes are easy to interpret. It also has nice implementations of some slightly unusual chart types, i.e., heatmaps, violin plots and hexbin plots are often useful when dealing with genome-scale biological data. It also provides an amazing implementation of "factor plots" to observe how relationships differ between various categories (e.g. male vs female specimens, or normal vs. diseased cells).
Pygal - a high level library, having two specific aims:
a. To create charts in scalable vector graphics (SVG) format, and
b. To have a very simple interface.
SVG graphics have several nice properties: easy editing with other tools, scaling the object to any size without blur and adding cool interactive features like tooltips. As long as the interface is concerned, pygal is the opposite of matplotlib, i.e., it's a high level charting library - it makes it very easy to draw a plot, but makes it harder to have complete control over the way it looks or to make custom graphics.
Just like Python, R-language also provides various libraries to visualize and analyze the biological datasets in a more interactive and comprehensive manner. Here is a brief description about some the data visualization libraries provided by R:
ggplot2 - can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. by using high-level API. It is a visualization library of R, that allows us to add different types of data visualization components or layers in a single visualization.
Esquisse - allows you to create detailed data visualizations using the ggplot2 package. It helps in creating all sorts of scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, etc. and also export these graphs. Esquisse is such a famous and easily used data visualization tool because of its drag and drops ability that makes it popular even among beginners.
Bioinfolytics & Our Services
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