Informatics has assisted evolutionary biologists by enabling researchers to: • trace the evolution of a large number of organisms by measuring changes in their DNA, rather
than through physical taxonomy or physiological observations alone, • compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important
in bacterial speciation, • build complex computational population genetics models to predict the outcome of the system over time • track and share information on an increasingly large number of species and organisms Future work endeavours
to reconstruct the now more complex tree of life.
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
Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved
in each state.
Although biological networks can be constructed from a single type of molecule or entity (such as genes), network biology often attempts to integrate many different data types,
such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both.
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.
Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure
prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis.
For example: • Abbreviation recognition – identify the long-form and abbreviation of biological terms • Named-entity recognition – recognizing biological terms such as gene
names • Protein–protein interaction – identify which proteins interact with which proteins from text The area of research draws from statistics and computational linguistics.
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.
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
Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data.
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 900 nucleotides long, depending on the sequencing technology).
All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing
statistical tools to separate signal from noise in high-throughput gene expression studies.
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).
Some examples are: • high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics) • morphometrics
• clinical image analysis and visualization • determining the real-time air-flow patterns in breathing lungs of living animals • quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury •
making behavioral observations from extended video recordings of laboratory animals • infrared measurements for metabolic activity determination • inferring clone overlaps in DNA mapping, e.g.
 Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.
Analysis of cellular organization Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells.
For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related
Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as specific analysis “pipelines” that are repeatedly used, particularly
in the field of genomics.
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
This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome.
Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome.
In a less formal way, bioinformatics also tries to understand the organizational principles within nucleic acid and protein sequences, called proteomics.
One can then apply clustering algorithms to that expression data to determine which genes are co-expressed.
It is divided in two parts- The Core genome: Set of genes common to all the genomes under study (These are often housekeeping genes vital for survival) and The Dispensable/Flexible
Genome: Set of genes not present in all but one or some genomes under study.
In fact, most gene function prediction methods focus on protein sequences as they are more informative and more feature-rich.
Another aspect of structural bioinformatics include the use of protein structures for Virtual Screening models such as Quantitative Structure-Activity Relationship models
and proteochemometric models (PCM).
For complex genomes, the most successful methods use a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms.
 Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases.
Goals 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.
At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology.
It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes.
 Nuclear organization of chromatin Main article: Nuclear organization Data from high-throughput chromosome conformation capture experiments, such as Hi-C (experiment)
and ChIA-PET, can provide information on the spatial proximity of DNA loci.
 Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early 1950s.
Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer.
 She compiled one of the first protein sequence databases, initially published as books and pioneered methods of sequence alignment and molecular evolution.
Nevertheless, half of the predicted proteins in a new genome sequence tend to have no obvious function.
 AlphaFold, during the 14th Critical Assessment of protein Structure Prediction (CASP14) computational protein structure prediction software competition, became the first
contender ever to deliver prediction submissions with accuracy competitive with experimental structures in a majority of cases and greatly outperforming all other prediction software methods up to that point.
 Genetics of disease Main article: Genome-wide association studies With the advent of next-generation sequencing we are obtaining enough sequence data to map the
genes of complex diseases including infertility, breast cancer or Alzheimer’s disease.
A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.
 Analysis of mutations in cancer Main article: Oncogenomics In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways.
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence
of gene B, whose function is unknown, one could infer that B may share A’s function.
Gene function prediction While genome annotation is primarily based on sequence similarity (and thus homology), other properties of sequences can be used to predict
the function of genes.
In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known.
This is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems.
These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably.
 Main articles: Protein–protein interaction prediction and interactome Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography
and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing
protein–protein interaction experiments.
Bioinformatics (/ˌbaɪ.oʊˌɪnfərˈmætɪks/) is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data
sets are large and complex.
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.
Many of these studies are based on the detection of sequence homology to assign sequences to protein families.
See also: sequence analysis, sequence mining, sequence profiling tool, and sequence motif Genome annotation Main article: Gene prediction In the context of genomics,
annotation is the process of marking the genes and other biological features in a DNA sequence.
In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins.
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.
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.
Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.
With the growing amount of data, it long ago became impractical to analyze DNA sequences manually.
 Analysis of regulation 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.
Often, such identification is made with the aim to better understand the genetic basis of disease, unique adaptations, desirable properties (esp.
Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.
High-throughput image analysis Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content
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.
For example, gene expression can be regulated by nearby elements in the genome.
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Photo credit: https://www.flickr.com/photos/zanastardust/173350276/’]