The term Bioinformatics was coined by Paulien Hogeweg in 1978 .It is the field of science in which biology, computer science, and information technology merge into a single discipline. Bioinformatics aim to solve unattended biological problems using computer science, information technology and statistics. In bioinformatics there are three important sub- disciplines .The first one is development of new algorithms and statistics with which help to assess relationships among members of large data sets. The second sub-discipline is analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures. The last sub-discipline is development and implementation of tools that enable efficient access and management of different types of information.
The bioinformatics is defined as the creation and development of advanced information and computational technologies for problems in biology, most commonly molecular biology (but increasingly in other areas of biology). As such, it deals with methods for storing, retrieving and analyzing biological data, such as deoxyribonucleic acid/ribonucleic acid (nucleic acid) and protein sequences, structures, functions, pathways and genetic interactions.Â Â Some people construe bioinformatics more narrowly, and include only those issues dealing with the management of genome project sequencing data. Others construe bioinformatics more broadly and include all areas of computational biology, including population modeling and numerical simulations.
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In another words bioinformatics describes as, using of computers to handle biological information. In practice the definition used by most people is narrower; bioinformatics to them is a synonym for "computational molecular biology" - the use of computers to characterize the molecular components of living things
In bioinformatics field Computers are used to gather for analyze, store and integrate biological and genetic information. This information can be applied to gene-based drug discovery and development. The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project. The goal of Human Genome Project is determination of the sequence of the entire human genome (approximately three billion base pairs). The another filed of Bioinformatics is molecular modelling, which is the melding of molecular biology with computer science to mimic the behavior of molecules.This is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery. In recognition of this, many universities, government institutions and pharmaceutical firms have formed bioinformatics groups, consisting of computational biologists and bioinformatics computer scientists. Such groups will be key to unraveling the mass of information generated by large scale sequencing efforts underway in laboratories around the world.
Molecular diagnostics provided to understand the mechanisms of disease at molecular levels .Hence it is a rapidly advancing field in which insights into disease mechanisms are being explained by use of new gene-based biomarkers. Recently, diagnostic and prognostic assessment of diseased causing tissues and tumor cells depend up on indirect indicators that not permitted account the alterations in individual gene expression, and only permit general classifications into broad histologic or morphologic subtypes. Global expression analysis of microarrays technology provides the expression of thousands of genes in a high-throughput fashion. The microarray technology offers unprecedented opportunities to obtain molecular signatures of the state of activity of diseased cells and patient samples. Microarray analysis may provide useful information about disease pathology, progression, resistance to treatment, and response to cellular microenvironments. This technique ultimately may lead to improved early diagnosis and give therapeutic approaches for cancer.
In the beginning Microarray technology used to study differential gene expression using complex populations of RNA. After the improvement of methods now permit the analysis of copy number imbalances and gene amplification of DNA. This method recently been applied to the systematic analysis of expression at the protein level.
Expression Profiling Applied to Cancer Biology
The Cancer grows out of normal cells in the body. Usually normal cells multiply when the body needs them. These cells die when the body doesn't need them. Cancer appears to occur when the growth of cells in the body is out of control and cells divide too quickly. Cancer is caused by the accumulation of genetic and heritable changes in gene expression or cellular phenotype changes resulting from the change of sequence or expression of cancer-related genes. these genes are called oncogene or tumor suppressor genes. These genes involved in cell cycle control, apoptosis, adhesion, DNA repair, and angiogenesis. The gene expression profiles provide a snapshot of cell functions and processes at the time of sample preparation. The gene expression profile makes allow using comprehensive combinatorial analysis of the gene expression patterns of thousands of genes in tumor cells. This method provides comparison to the expression profile obtained with healthy cells that provide information about consistent changes in gene expression that are associated with tumor cellular dysfunction. In the past 3 years Microarray technology has been used to investigate tumor classification, cancer progression, and chemotherapy resistance and sensitivity.
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The Cancer grows out of normal cells in the body. Usually normal cells multiply when the body needs them. These cells die when the body doesn't need them. Cancer appears to occur when the growth of cells in the body is out of control and cells divide too quickly. There are many different kinds of cancer. Cancer can develop in almost any organ or tissue, such as the lung, colon, breast, skin, bones, or nerve tissue. Breast cancer is a cancer that starts in the tissues of the breast. There are two main types of breast cancer: Ductal carcinoma starts in the tubes (ducts), which move milk from the breast to the nipple. Most breast cancers are of this type. Lobular carcinoma starts in the parts of the breast, called lobules, which produce milk. In rare cases, breast cancer can start in other areas of the breast. Breast cancer may be invasive or noninvasive. Invasive means it has spread from the milk duct or lobule to other tissues in the breast. Noninvasive means it has not yet invaded other breast tissue. Noninvasive breast cancer is called "in situ."
The breast cancer stage is based on the results of testing that is done on the tumor and lymph nodes removed during surgery and other tests.Stage 0 (carcinoma in situ) ,in this stage There are 3 types of breast carcinoma in situ .Ductal carcinoma in situ (DCIS) is a noninvasive condition in which abnormal cells are found in the lining of a breast duct. The abnormal cells have not spread outside the duct to other tissues in the breast. In some cases, DCIS may become invasive cancer and spread to other tissues. At this time, there is no way to know which lesions could become invasive. Lobular carcinoma in situ (LCIS) is a condition in which abnormal cells are found in the lobules of the breast. This condition seldom becomes invasive cancer. However, having LCIS in one breast increases the risk of developing breast cancer in either breast. Paget disease of the nipple is a condition in which abnormal cells are found in the nipple only.
In stage I, cancer has formed. Stage I is divided into stages IA and IB. In stage IA, the tumor is 2 centimeters or smaller. Cancer has not spread outside the breast.In stage IB, small clusters of breast cancer cells (larger than 0.2 millimeter but not larger than 2 millimeters) are found in the lymph nodes and either no tumor is found in the breast or the tumor is 2 centimeters or smaller.
Stage II is divided into stages IIA and IIB. In stage IIA no tumor is found in the breast or the tumor is 2 centimeters or smaller. Cancer (larger than 2 millimeters) is found in 1 to 3 axillary lymph nodes or in the lymph nodes near the breastbone or the tumor is larger than 2 centimeters but not larger than 5 centimeters. Cancer has not spread to the lymph nodes. In stage IIB, the tumor is larger than 2 centimeters but not larger than 5 centimeters. Small clusters of breast cancer cells (larger than 0.2 millimeter but not larger than 2 millimeters) are found in the lymph nodes or larger than 2 centimeters but not larger than 5 centimeters. Cancer has spread to 1 to 3 axillary lymph nodes or to the lymph nodes near the breastbone or larger than 5 centimeters. Cancer has not spread to the lymph nodes.
Stage III is divided into stages IIIA ,IIIB and IIIC.In stage IIIA no tumor is found in the breast or the tumor may be any size. Cancer is found in 4 to 9 axillary lymph nodes or in the lymph nodes near the breastbone or the tumor is larger than 5 centimeters. Small clusters of breast cancer cells (larger than 0.2 millimeter but not larger than 2 millimeters) are found in the lymph nodes; or the tumor is larger than 5 centimeters. Cancer has spread to 1 to 3 axillary lymph nodes or to the lymph nodes near the breastbone In stage IIIB, the tumor may be any size and cancer has spread to the chest wall and/or to the skin of the breast and caused swelling or an ulcer. Also, cancer may have spread up to 9 axillary lymph nodes or the lymph nodes near the breastbone.
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In stage IIIC, no tumor is found in the breast or the tumor may be any size. Cancer may have spread to the skin of the breast and caused swelling or an ulcer and/or has spread to the chest wall. Also, cancer has spread to 10 or more axillary lymph nodes or lymph nodes above or below the collarbone or axillary lymph nodes and lymph nodes near the breastbone. For treatment, stage IIIC breast cancer is divided into operable and inoperable stage IIIC.
In stage IV, cancer has spread to other organs of the body, most often the bones, lungs, liver, or brain.
Oncogenomic is the study of the relationship between genome of an individual and cancer .Dna microarray technology has led to oncogenomic analyses, generating a large amount of data and discovers the complex gene expression patterns of cancer. TheÂ oncomine is a cancer microarray database. Oncomine provides web-based data-mining platform that help to find discover from genome-wide expression analyses. The oncomineÂ contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Oncomine provide differential expression analyses comparing most major types of cancer with respective normal tissues. For the selected gene Data can be queried and visualized across all analyses or for multiple genes in a selected analysis. More over gene sets can be limited to clinically important annotations including secreted, kinas, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets. This is the main feature of oncomine database.
For the study and analysis of cancer transcriptome gene expression profiling with DNA microarrays has to use. In oncomine database more than 100 published studies have presented analyses of human cancer samples, identifying gene expression signatures for most major cancer types and subtypes, and uncovering gene expression patterns that correlate with various characteristics of tumors including tumor grade or differentiation state, metastatic potential, and patient survival . Using genome-wide screens novel tissue and potential therapeutic targets have been identified. These discoveries lead to remarkable impact on cancer research. Most microarray studies, these include thousands of gene measurements across tens or hundreds of cancer specimens, the authors have presented one interpretation of their data and have reported on only a subset of genes that demonstrate their particular hypothesis. The complete microarray datasets are sometimes made available as supplementary data, but even if that is the case, the datasets often sit as cryptic text files, stored and processed in an unsystematic manner, and thus only useful to those with computational expertise. Although standards have now been set for recording and exchanging microarray data, and authors have been urged to provide their complete datasets upon publication, the full potential of cancer microarray data will only be reached when it is unified, logically analyzed, and made easily accessible to the cancer research community.