Skip to main content

Introduction to Bioinformatics


           Bioinformatics is branch of biological and biomedical sciences and it is an application for the management of the vast biological information. Technically, Bioinformatics is the combination of biological sciences and information technology. In this field, the computers, softwares and online databases are used to gather, store, analyze and integrate biological information and genetic information. These information can then be applied to gene-based drug discovery, protein engineering and development. With the help of this, an investigator can store or analyze the genetic information in understanding the human diseases  and in the identification of new potential molecular targets and drug discovery. Bioinformatics can also be applied to study the fundamental biomedical problems. 

Biological Databases:

Huge amount of data of nucleotides, proteins and structures can not be written in a book.. Its gonna take a lot of time and effort to write those data.. But, technology has evolved in a such a way that we can practically store vast amount of data in an online databases and these databases are publically available. 


Data generated are deposited in online databases which are publically accessible. These data include nucleotide sequences of genes or amino acid sequences of proteins. and there are some databases which have the data of function, structure, localization, mutations, and also the similarities of biological sequences from species to species. (Localization includes both chromosomal and cellular). 

There are mainly three types of databases: 

1) Nucleotide Sequence Database
2) Protein Sequence Database
3) Molecular Structure Database. 

Examples of Databases: 

EMBL = European Molecular Biology Laboratory. 

GenBank = This is nucleotide database and it is maintained by NCBI (National Center for Biotechnology Information). 

NCBI is itself a database which has contains lot of biological information and it is a part of NIH. 

DDBJ = DNA Data Bank of Japan. 

Unigen = Non-redundant database of gene-oriented clusters. 

EBI Genomes = Complete Genome Statistics. 

Ensembl = Software to produce and maintain automatic annotations of eukaryotic genomes. 

SWISS-PROT = Manually Curated biological database of protein sequences. 

PIR = Protein Information Resource, to support genomic and proteomic research. 

UniProt = Universal Protein Database, a central repository of protein data. 

PROSITEIt =  Protein Families and Domains Database. 

Pfam = Protein families database of alignments and Hidden Markov Models. 

PDB = Protein Data Bank, 3-D structural data of proteins and nucleic acids. 

SCOP = Structural Classification of Proteins, it classifies the protein 3-D structures in hierarchy. 

CATH = Class Architecture Topology Homologous Super Family, hierarchical classification of protein domain structures. 

KEGG = Kyoto Encyclopedia of Gene and Genomes, collection of online databases of genomes, biological chemicals and enzymatic pathways. 

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

Nanodrop

Thermofisher, Nanodrop 1000    Nanodrop is a spectrophotometer which is designed to measure the concentration of nucleic acids at 260nm and Proteins at 280nm present in a very small minute volume. It is also capable of measuring the concentrations of dsDNA, RNA, ssRNA and purified protein. Required sample size is usually 1µL. Nanodrop instruments are usually full spectrum spectrophotometer, i.e., Ultra violet and visible range wavelengths (250nm to 750nm).  Nanodrop is commonly used for the quantification of the DNA concentration and RNA concentration in a given sample. However there are many other applications which can be performed on this instrument.  It works on the principle of Spectrophotometer or Sample Retention System....  Principle of Nanodrop: "The Nanodrop works on the principle of sample retention system.. When the small volume of sample is introduced, the arm and pedestal will work together and make the sample in the form of colum...
Post a Comment
....CONTINUE READING

Statistics in Flow Cytometry Data and "MFI" values

                                 The speed of the flow cytometry offers wide range of data points and data plots. Due to its sensitivity and versatility, it has been used extensively. Flow cytometer can detect up to 1000s of cells per second.. But, it should be noted that the flow cytometry also involves the statistics, its significance, calculating the fluorescence intensity..                Statistics in flow cytometry involves, total number of data points acquired, percentage population, most importantly mean and median fluorescence intensity, and others. Most of us get confused about the term "MFI", whether it means mean fluorescence intensity or median fluorescence intensity.. Truth is, both.. It means it can be described as either mean or median fluorescence intensity. Some researchers use Mean for MFI and some use Median for MFI values.  (Keep...
1 comment
....CONTINUE READING

Compensation in Flow Cytometry (Fluorescence Spill Over Correction).

                      The term "Compensation" in flow cytometers refers to the process of correcting fluorescence spill over. The compensation matrix ensures that the fluorescence emitted is detected in a particular detector which is from the fluorescence being measured. As we all know that most of the fluorescence molecules have broad emission range. if more than one one fluorescence is selected, their emission spectra may overlap. To correct this spectral overlap in cytometry data, a mathematical matrix called compensation is used. The Intention of this article is to teach you the compensation from basic theory to practical. This example for FITC and PE fluorescence molecules shows that, Some of the FITC fluorescence is being detected in PE Detector. and Some of the PE fluorescence is being detected in FITC Detectors. Task here is to make sure that in cytometry data, FITC is in FITC detector only and PE is in PE Detector only....
1 comment
....CONTINUE READING