The term Bioinformatics was coined by Paulien Hogeweg in 1978 .It is the branch of science in which biology, information technology and computer science 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 create 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 protein structures, amino acid sequences, protein domains and nucleotide. The last sub-discipline is development and implementation of tools that provide opportunity to efficient access and handling of different types of information.
The bioinformatics is defined as the creation and development of computational technologies and advanced information for solving problems in biology, most commonly molecular biology (but increasingly in other areas of biology). Such as the term bioinformatics deals with methods for storing, mining and annotate biological data, such as deoxyribonucleic acid/ribonucleic acid (nucleic acid) and protein sequences, structures, functions, pathways and genetic interactions. 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.
In another words bioinformatics describes as, using of computer techniques to handle large set of biological data. Bioinformatics is used to developing computer database and algorithms. In human genome project bioinformatics play an important role. In this project complete human sequence in human genome that about 3 billion pairs using to determine the genomic information. This information helps to understand about the disease and also help to identify new biological targets. This will help in drug discovery.
In bioinformatics field Computers are used to gather for analyze, store and integrate biological and genetic information. This information helps to gene-based drug discovery and development. The bioinformatics play a vital role in Human Genome Project, determine complete sequence of the entire human genome that approximately about three billion base pair. The author 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 understanding human disease using genomic information and it help in 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 solve unattended biological problems .
Microarray Technology
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
In human body Cancer grows out of normal cells. Usually cells multiply when the body needs them and 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.
Breast cancer
In human body Cancer grows out of normal cells. Usually normal cells multiply when the body needs them and cells die when the body doesn't need them. Cancer starts when the growth of cells in the body is out of control and cells divide very quickly. There are many different kinds of cancer present in human body. Cancer can develop in almost any organ or tissue in human body such as the breast, bones, colon, lung, skin or nerve tissue. Breast cancer is a type of cancer that originates from the tissues of the breast. In normal case two type of breast cancer present in human body they are ductal carcinoma and Lobular carcinoma. In breast milk move to the nipple through ducts .Ductal carcinoma usually begins in ducts. Most reporting breast cancer is of this type. Lobular carcinoma begins in lobules. Lobules are the part of breast which produces milk. In rare cases breast cancer start other regions of the breast. Breast cancer may be invasive or noninvasive. If cancer spread from milk or lobule to other tissues in the breast called as invasive. Noninvasive means it has not yet enter other breast tissue. This type of breast cancer also called "in situ."
In breast cancer there are five stages are involved .These stages are based on the results of testing that is done on the tumor and lymph nodes removed during surgery. Stages of breast cancer involved several factor. Including the size of tumor, if any lymph nodes are involved or if the cancer is invasive or noninvasive .In the first stage is known as Stage 0 (carcinoma in situ) .this stage is considered as noninvasive breast cancer. In this stage there is no evidence that cancer cell has spread into neighboring breast tissue. The cancer cells are at this stage present in ducts or lobules .3 types of breast carcinoma in situ. The Ductal carcinoma in situ (DCIS) is a noninvasive condition of breast cancer. In this stage abnormal cells are found in the breast duct. In this type abnormal cells have not spread outside to duct. In rarer condition abnormal cells are spread out side of duct. Lobular carcinoma in situ (LCIS) is a condition in this condition abnormal cells are present in the lobules. The presence of LCIS increases probability of developing breast cancer. The last type is Paget disease of the nipple. In this condition abnormal cells are present in the nipple of breast.
In stage I is consider as early stage of invasive breast cancer. In this stage tumor is not more 2 cm in diameter and also there is no evidence that cancer cell has spread beyond breast. The Stage I is subcatgorarized into stages IA and IB. In stage IA, the tumor cell is 2 centimeters in diameter. In this stage Cancer has not spread outside the breast. In stage IB, small clusters of breast cancer cells are present in the lymph nodes. These cancer cells are larger than 0.2 millimeters in diameter.
Stage II is divided into subcategory IIA and IIB. In stage IIA is an invasive breast cancer. In this case tumor is maximum 2 cm in diameter. Tumor is spread lymph node under the arm. In another case the tumor cell is between 2-5 cm in diameter and that has not spread any lymph nodes. The stage IIB is little difference, it has diameter of 2-5 cm and found under the lymph nodes. In another study tumor is larger than 5 cm in diameter that has not spread under the lymph nodes.
Stage III is considered as locally advance cancer and also subcategorized into stages IIIA, IIIB and IIIC.There are two scenarios that can occur in stage IIIA breast cancer. One the tumor is not larger than 5 cm in diameter. But it spread under the lymph node. Those are growing each other forming clump. The cancer also spread lymph node near the breast bone. The second scenario in IIIA is tumor cell larger than 5 cm in diameter and those are not growing each other. Unlike other stages in IIIB tumor may be any size and that has spread skin of breast or chest wall. This stage also indicates some symptoms of breast cancer.
In stage IIIC, tumor also any size but it has spread lymph node area above or below of the clavicle chest wall and also the skin of the breast. some In stage IV, cancer has spread to other organs in parts of body.
Oncomine database
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. The oncomine is a cancer microarray database. Oncomine provides web-based data-mining platform that help to find discover from genome-wide expression analyses. The oncomine 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.
