Bioinformatics
Bioinformatics
Bioinformatics
Bioinformatics is a field that develops programs and software tools for understanding biological data. It combines biology, computer science, information engineering, mathematics, and statistics to analyze and interpret biological data. It includes biological studies that use computer programming as part of their methodology, as well as a specific analysis “pipelines” that are repeatedly used, particularly in the field of genomics.
Uses of bioinformatics include the identification of candidates’ genes and single nucleotide. in order to understand the genetic basis of disease, unique adaptations, desirable properties or differences between populations.
There are many platforms online that processes DNA to tell more about people like their personality, intelligence level, physical trait, food and nutrients, and so on. We have started our research in the field of Bioinformatics to analyze biological data more accurately.
Components of Bioinformatics
We are currently expanding our business to Bioinformatics, and for that, we are working towards understanding genetics and genomics that includes the study of DNA, RNA, protein, and other structures. As we are already working in the field of Deep Learning and Machine Learning, we are now stepping forward to understand biological sequences by applying various algorithms and techniques.
Technology
Algorithm
Analysis Tools
Database
Computing Power
Roles of Bioinformatics
Artificial Intelligence
Graph Theory
Graphy theory is applied to several fields of Bioinformatics, like comparing sequences for better understanding. It includes assembling fragments.
Algorithms
Analyzing and processing data is an important step in Bioinformatics, this includes computer science algorithms for sequencing and assembling the data.
Data Mining
Data mining is used to perform a comparison between sequences in different species by uncovering patterns, structures, and anomalies statistically.
Soft Computing
In order to experiment on large and complex bioinformatics data especially in proteomic, soft computing technique is used to analyze them.
Computer Simulation
Computer simulators are widely developed to stimulate DNA sequencing data, for understanding the behavior of biological process.
Image Processing
Algorithms are written for image processing that helps scientists and physicians in the diagnosis and evaluating and editing microscopic images.
Sequence Analysis
Sequence analysis is the process of subjecting a DNA, RNA, or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment and searches against biological databases.
The tasks that include in sequence analysis are often non-trivial to resolve and require the use of relatively complex approaches. Of the many types of methods used in practice, the most popular include: DNA patterns, Dynamic programming, Artificial Neural Network, Clustering, Regression Analysis, Sequence mining, and Alignment-free sequence analysis.
We are working towards developing software to automate sequencing and interpret large complex data, that will benefit biological fields and common people whoever want to know about it.
Protein Expression
Protein production is the biotechnological process of generating a specific protein. It is typically achieved by the manipulation of gene expression in an organism such that it expresses large amounts of a recombinant gene. This includes the transcription of the recombinant DNA to messenger RNA (mRNA), the translation of mRNA into polypeptide chains, which are ultimately folded into functional proteins and may be targeted to specific subcellular or extracellular locations.
There is various protein-expressing software in the market that are used to identify and produce data from large data set.
Analysis of Cellular Organization
Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. This is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems.
A gene ontology category, cellular compartment, has been devised to capture subcellular localization in many biological databases.
This includes Microscopy and image analysis, Protein localization, Nuclear organization of chromatin.
BioJS
BioJS is an open-source project for bioinformatics data on the web. Its goal is to develop an open-source library of JavaScript components to visualize biological data. BioJS develops and maintains small building blocks (components) that can be reused by others.
We are a JavaScript company and BioJS JavaScript framework for biological data visualization. In addition to programming, it also provides a registry for the discovery of available bioinformatics components. We as a JavaScript company trying to develop such components for biological applications.
BioPHP
BioPHP is a collection of open-source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools. We have done PHP since we started software development and we have more than 200+ PHP projects in our portfolio.
And now we are extending the scope of PHP to BioPHP so that we can create useful bioinformatics applications. It will help us to connect bioinformatics web-based applications to databases, in order to read biological data.
Apache Taverna
Apache Taverna is an open-source software tool for designing and executing workflows. It allows users to integrate many different software components, including WSDL SOAP or REST Web services for various biotechnology and bioinformatics institutes.
Some of the services for the use in Taverna workflows can be discovered through the BioCatalogue – a public, centralized and curated registry of Life Science Web services.
