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- Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis
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As an interdisciplinary field of science, bioinformatics combines biology , computer science , information engineering , mathematics and statistics to analyze and interpret the biological data.
Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics 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.
Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms SNPs. Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties esp. In a less formal way, bioinformatics also tries to understand the organizational principles within nucleic acid and protein sequences, called proteomics.
Bioinformatics has become an important part of many areas of biology. In experimental molecular biology , bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations.
It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology.
At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology.
In structural biology , it aids in the simulation and modeling of DNA,  RNA,   proteins  as well as biomolecular interactions. Historically, the term bioinformatics did not mean what it means today. Paulien Hogeweg and Ben Hesper coined it in to refer to the study of information processes in biotic systems. Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early s.
Comparing multiple sequences manually turned out to be impractical. A pioneer in the field was Margaret Oakley Dayhoff. Kabat , who pioneered biological sequence analysis in with his comprehensive volumes of antibody sequences released with Tai Te Wu between and These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were thus proof of the concept that bioinformatics would be insightful.
To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences , protein domains , and protein structures. Important sub-disciplines within bioinformatics and computational biology include:.
The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition , data mining , machine learning algorithms, and visualization. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.
Over the past few decades, rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes.
Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
Bioinformatics is a science field that is similar to but distinct from biological computation , while it is often considered synonymous to computational biology. Biological computation uses bioengineering and biology to build biological computers , whereas bioinformatics uses computation to better understand biology. Bioinformatics and computational biology involve the analysis of biological data, particularly DNA, RNA, and protein sequences.
The field of bioinformatics experienced explosive growth starting in the mids, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology. Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory , artificial intelligence , soft computing , data mining , image processing , and computer simulation.
The algorithms in turn depend on theoretical foundations such as discrete mathematics , control theory , system theory , information theory , and statistics. This sequence information is analyzed to determine genes that encode proteins , RNA genes, regulatory sequences, structural motifs, and repetitive sequences.
A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species the use of molecular systematics to construct phylogenetic trees. With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Computer programs such as BLAST are used routinely to search sequences—as of , from more than , organisms, containing over billion nucleotides.
Before sequences can be analyzed they have to be obtained from the data storage bank example the Genbank. DNA sequencing is still a non-trivial problem as the raw data may be noisy or afflicted by weak signals.
Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing. Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences.
The so-called shotgun sequencing technique which was used, for example, by The Institute for Genomic Research TIGR to sequence the first bacterial genome, Haemophilus influenzae  generates the sequences of many thousands of small DNA fragments ranging from 35 to nucleotides long, depending on the sequencing technology.
The ends of these fragments overlap and, when aligned properly by a genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. For a genome as large as the human genome , it may take many days of CPU time on large-memory, multiprocessor computers to assemble the fragments, and the resulting assembly usually contains numerous gaps that must be filled in later.
Shotgun sequencing is the method of choice for virtually all genomes sequenced today [ when? In the context of genomics , annotation is the process of marking the genes and other biological features in a DNA sequence.
This process needs to be automated because most genomes are too large to annotate by hand, not to mention the desire to annotate as many genomes as possible, as the rate of sequencing has ceased to pose a bottleneck. Annotation is made possible by the fact that genes have recognisable start and stop regions, although the exact sequence found in these regions can vary between genes.
The first description of a comprehensive genome annotation system was published in  by the team at The Institute for Genomic Research that performed the first complete sequencing and analysis of the genome of a free-living organism, the bacterium Haemophilus influenzae. Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA, such as the GeneMark program trained and used to find protein-coding genes in Haemophilus influenzae , are constantly changing and improving.
Following the goals that the Human Genome Project left to achieve after its closure in , a new project developed by the National Human Genome Research Institute in the U. S appeared. The so-called ENCODE project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy base call error and fidelity assembly error.
Evolutionary biology is the study of the origin and descent of species , as well as their change over time. Informatics has assisted evolutionary biologists by enabling researchers to:. Future work endeavours to reconstruct the now more complex tree of life. The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related.
The core of comparative genome analysis is the establishment of the correspondence between genes orthology analysis or other genomic features in different organisms.
It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution.
At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics , fixed parameter and approximation algorithms for problems based on parsimony models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.
Many of these studies are based on the detection of sequence homology to assign sequences to protein families. Pan genomics is a concept introduced in by Tettelin and Medini which eventually took root in bioinformatics. Pan genome is the complete gene repertoire of a particular taxonomic group: although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum etc.
With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases infertility ,  breast cancer  or Alzheimer's disease.
Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis. In cancer , the genomes of affected cells are rearranged in complex or even unpredictable ways.
Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms.
New physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses called comparative genomic hybridization , and single-nucleotide polymorphism arrays to detect known point mutations.
These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment.
Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or noise , and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes. Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome.
First, cancer is a disease of accumulated somatic mutations in genes. Second cancer contains driver mutations which need to be distinguished from passengers. With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably.
This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.
Protein microarrays and high throughput HT mass spectrometry MS can provide a snapshot of the proteins present in a biological sample. Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein are detected.
Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays. Gene regulation is the complex orchestration of events by which a signal, potentially an extracellular signal such as a hormone , eventually leads to an increase or decrease in the activity of one or more proteins.
Bioinformatics techniques have been applied to explore various steps in this process. For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions.
These interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments.
Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis
As an interdisciplinary field of science, bioinformatics combines biology , computer science , information engineering , mathematics and statistics to analyze and interpret the biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics 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. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms SNPs. Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties esp. In a less formal way, bioinformatics also tries to understand the organizational principles within nucleic acid and protein sequences, called proteomics.
Motivation: An important problem in synthetic biology is to design a nucleotide sequence of an mRNA that confers a desirable expression level of a target protein. The secondary structure of protein-coding sequences CDSs is one potential factor that could have both positive and negative effects on protein production. To elucidate the role of secondary structure in CDSs, algorithms for manipulating secondary structure should be developed. Results: We developed an algorithm for designing a CDS with the most stable secondary structure among all possible ones translated into the same protein, and implemented it as the program CDSfold. The algorithm runs the Zuker algorithm under the constraint of a given amino acid sequence. Although our algorithm is slower than the original Zuker algorithm, it could design a relatively long 2. Contacts: terai-goro aist.
Part II: STRUCTURE ANALYSIS 8. Structures and Structure Descriptions. Units of Structure Descriptions. Coordinates. Distance Matrices. Torsion.
NCBI Bookshelf. Boston: Kluwer Academic; Genetic evidence for Near-Eastern origins of European cattle.
Proteins have evolved subject to energetic selection pressure for stability and flexibility. Structural similarity between proteins which have gone through conformational changes can be captured effectively if flexibility is considered. Topologically unrelated proteins that preserve secondary structure packing interactions can be detected if both flexibility and sequence permutations are considered. We propose the FlexSnap algorithm for flexible non-topological protein structural alignment. The effectiveness of FlexSnap is demonstrated by measuring the agreement of its alignments with manually curated non-sequential structural alignments.
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