bioinformatics
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What is bioinformatics?
Bioinformatics is a hybrid science using biological data and informatics techniques for data storage, distribution, and analysis, supporting scientific research and biomedicine. It uses high-throughput data-generating experiments, such as genomic sequence determinations and gene expression pattern measurements. Bioinformatics is used to identify relationships between gene sequences and diseases, predict protein structures from amino acid sequences, help design new drugs, and tailor treatments to individual patients based on their DNA sequences. Measuring sequence similarity, extending experimental data via predictions, and predicting interactions between proteins are important goals.
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How does bioinformatics combine biology and computers?
Bioinformatics merges biological data with computer techniques for data storage, analysis, and distribution, supporting scientific research and biomedicine. It uses high-throughput experiments to determine genomic sequences and measure gene expression patterns. Bioinformatics helps identify links between gene sequences and diseases, predict protein structures, and design new drugs.
Algorithms measure sequence similarity, like the Needleman-Wunsch algorithm, which divides large problems into smaller segments to construct a solution. Programs like BLAST retrieve information rapidly from large databases. Bioinformatics integrates data to understand organisms and diseases. Computer science and artificial intelligence contribute to modeling sequences, analyzing complex data, and codifying biological knowledge.
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What types of data do bioinformaticians work with?
Bioinformaticians work with a variety of data types, including DNA sequences of genes or full genomes, amino acid sequences of proteins, and 3D structures of proteins, nucleic acids, and protein-nucleic acid complexes.
Additional data includes transcriptomics (RNA synthesis patterns from DNA), proteomics (protein distribution in cells), interactomics (protein-protein and protein-nucleic acid interactions), and metabolomics (small molecule transformation patterns). They use automated biochemical assays, software, and databases for complex analyses, and also work with data from omics studies, which are stored in public databases.
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How is bioinformatics used to study DNA and genes?
Bioinformatics merges biological data with computing for storing, distributing, and analyzing data to support scientific research. It helps identify correlations between gene sequences and diseases, predict protein structures, design drugs, and customize treatments based on individual DNA (pharmacogenomics). Computer programs scan DNA sequences, locate genes, predict their functions based on similar genes, and compare DNA molecules for evolutionary insights. Comparative genomics can detect subtle differences in DNA, even between species that seem distantly related.
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What role does bioinformatics play in understanding diseases?
Bioinformatics merges biological data with computational techniques for data storage, distribution, and analysis, playing a crucial role in understanding diseases. It identifies correlations between gene sequences and diseases, predicts protein structures, aids in drug design, and customizes treatments based on individual DNA (pharmacogenomics).
Bioinformatics facilitates systems biology by analyzing genes and gene products as integrated systems and uses computer-centered statistical methods to handle the vast amounts of information from genome sequencing projects. It also helps scientists understand the natural history of complex human diseases and their responsiveness to treatment.
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How has bioinformatics helped make discoveries in medicine and genetics?
Bioinformatics merges biological data with computing techniques for data storage, analysis, and distribution, which supports scientific research and biomedicine. It uses data from experiments, such as genomic sequencing and gene expression measurements. Bioinformatics helps identify relationships between gene sequences and diseases, predict protein structures, and design drugs.
Bioinformatics uses computer-based statistics to process information from genome sequencing projects. Computer programs scan DNA for genes, predict their functions based on similar genes, and compare DNA molecules for evolutionary analysis. Bioinformatics has enabled systems biology, which analyzes genes and gene products of cells as integrated systems. Advances in bioinformatics, along with high-throughput biotechnology, have increased computational power, creating large databases and comprehensive data analyses.
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bioinformatics, a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine. Bioinformatics is fed by high-throughput data-generating experiments, including genomic sequence determinations and measurements of gene expression patterns. Database projects curate and annotate the data and then distribute it via the World Wide Web. Mining these data leads to scientific discoveries and to the identification of new clinical applications. In the field of medicine in particular, a number of important applications for bioinformatics have been discovered. For example, it is used to identify correlations between gene sequences and diseases, to predict protein structures from amino acid sequences, to aid in the design of novel drugs, and to tailor treatments to individual patients based on their DNA sequences (pharmacogenomics).
The data of bioinformatics
The classic data of bioinformatics include DNA sequences of genes or full genomes; amino acid sequences of proteins; and three-dimensional structures of proteins, nucleic acids and protein–nucleic acid complexes. Additional “-omics” data streams include: transcriptomics, the pattern of RNA synthesis from DNA; proteomics, the distribution of proteins in cells; interactomics, the patterns of protein-protein and protein–nucleic acid interactions; and metabolomics, the nature and traffic patterns of transformations of small molecules by the biochemical pathways active in cells. In each case there is interest in obtaining comprehensive, accurate data for particular cell types and in identifying patterns of variation within the data. For example, data may fluctuate depending on cell type, timing of data collection (during the cell cycle, or diurnal, seasonal, or annual variations), developmental stage, and various external conditions. Metagenomics and metaproteomics extend these measurements to a comprehensive description of the organisms in an environmental sample, such as in a bucket of ocean water or in a soil sample.
Bioinformatics has been driven by the great acceleration in data-generation processes in biology. Genome sequencing methods show perhaps the most dramatic effects. In 1999 the nucleic acid sequence archives contained a total of 3.5 billion nucleotides, slightly more than the length of a single human genome; a decade later they contained more than 283 billion nucleotides, the length of about 95 human genomes. The U.S. National Institutes of Health has challenged researchers by setting a goal to reduce the cost of sequencing a human genome to $1,000; this would make DNA sequencing a more affordable and practical tool for U.S. hospitals and clinics, enabling it to become a standard component of diagnosis.
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Storage and retrieval of data
In bioinformatics, data banks are used to store and organize data. Many of these entities collect DNA and RNA sequences from scientific papers and genome projects. Many databases are in the hands of international consortia. For example, an advisory committee made up of members of the European Molecular Biology Laboratory Nucleotide Sequence Database (EMBL-Bank) in the United Kingdom, the DNA Data Bank of Japan (DDBJ), and GenBank of the National Center for Biotechnology Information (NCBI) in the United States oversees the International Nucleotide Sequence Database Collaboration (INSDC). To ensure that sequence data are freely available, scientific journals require that new nucleotide sequences be deposited in a publicly accessible database as a condition for publication of an article. (Similar conditions apply to nucleic acid and protein structures.) There also exist genome browsers, databases that bring together all the available genomic and molecular information about a particular species.
The major database of biological macromolecular structure is the worldwide Protein Data Bank (wwPDB), a joint effort of the Research Collaboratory for Structural Bioinformatics (RCSB) in the United States, the Protein Data Bank Europe (PDBe) at the European Bioinformatics Institute in the United Kingdom, and the Protein Data Bank Japan at Ōsaka University. The homepages of the wwPDB partners contain links to the data files themselves, to expository and tutorial material (including news items), to facilities for deposition of new entries, and to specialized search software for retrieving structures.
Information retrieval from the data archives utilizes standard tools for identification of data items by keyword; for instance, one can type “aardvark myoglobin” into Google and retrieve the molecule’s amino acid sequence. Other algorithms search data banks to detect similarities between data items. For example, a standard problem is to probe a sequence database with a gene or protein sequence of interest in order to detect entities with similar sequences.