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Saturday, March 21, 2015


From Wikipedia, the free encyclopedia

Cheminformatics (also known as chemoinformatics, chemioinformatics and chemical informatics) is the use of computer and informational techniques applied to a range of problems in the field of chemistry. These in silico techniques are used in, for example, pharmaceutical companies in the process of drug discovery. These methods can also be used in chemical and allied industries in various other forms.


The term chemoinformatics was defined by F.K. Brown [1][2] in 1998:
Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.
Since then, both spellings have been used, and some have evolved to be established as Cheminformatics,[3] while European Academia settled in 2006 for Chemoinformatics.[4] The recent establishment of the Journal of Cheminformatics is a strong push towards the shorter variant.


Cheminformatics combines the scientific working fields of chemistry, computer science and information science for example in the areas of topology, chemical graph theory, information retrieval and data mining in the chemical space.[5][6][7][8] Cheminformatics can also be applied to data analysis for various industries like paper and pulp, dyes and such allied industries.


Storage and retrieval

The primary application of cheminformatics is in the storage, indexing and search of information relating to compounds. The efficient search of such stored information includes topics that are dealt with in computer science as data mining, information retrieval, information extraction and machine learning. Related research topics include:

File formats

The in silico representation of chemical structures uses specialized formats such as the XML-based Chemical Markup Language or SMILES. These representations are often used for storage in large chemical databases. While some formats are suited for visual representations in 2 or 3 dimensions, others are more suited for studying physical interactions, modeling and docking studies.

Virtual libraries

Chemical data can pertain to real or virtual molecules. Virtual libraries of compounds may be generated in various ways to explore chemical space and hypothesize novel compounds with desired properties.

Virtual libraries of classes of compounds (drugs, natural products, diversity-oriented synthetic products) were recently generated using the FOG (fragment optimized growth) algorithm. [9] This was done by using cheminformatic tools to train transition probabilities of a Markov chain on authentic classes of compounds, and then using the Markov chain to generate novel compounds that were similar to the training database.

Virtual screening

In contrast to high-throughput screening, virtual screening involves computationally screening in silico libraries of compounds, by means of various methods such as docking, to identify members likely to possess desired properties such as biological activity against a given target. In some cases, combinatorial chemistry is used in the development of the library to increase the efficiency in mining the chemical space. More commonly, a diverse library of small molecules or natural products is screened.

Quantitative structure-activity relationship (QSAR)

This is the calculation of quantitative structure-activity relationship and quantitative structure property relationship values, used to predict the activity of compounds from their structures. In this context there is also a strong relationship to Chemometrics. Chemical expert systems are also relevant, since they represent parts of chemical knowledge as an in silico representation. There is a relatively new concept of Matched molecular pair analysis or Predcition driven MMPA which is coupled with QSAR model in order to identify activity cliff.[10]


From Wikipedia, the free encyclopedia

Map of the human X chromosome (from the NCBI website). Assembly of the human genome is one of the greatest achievements of bioinformatics.

Bioinformatics Listeni/ˌb.ˌɪnfərˈmætɪks/ is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to study and process biological data.

Bioinformatics is both an umbrella term for the body of biological studies that use computer programming as part of their methodology, as well as a reference to specific analysis "pipelines" that are repeatedly used, particularly in the fields of genetics and genomics. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences.


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 and genomics, 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 the comparison of 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, and protein structures as well as molecular interactions.


Paulien Hogeweg and Ben Hesper coined the term bioinformatics in 1970 to refer to the study of information processes in biotic systems.[1][2][3] This definition placed bioinformatics as a field parallel to biophysics (the study of physical processes in biological systems) or biochemistry (the study of chemical processes in biological systems).[1]


Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early 1950s. Comparing multiple sequences manually turned out to be impractical. A pioneer in the field was Margaret Oakley Dayhoff, who has been hailed by David Lipman, director of the National Center for Biotechnology Information, as the "mother and father of bioinformatics."[4] Dayhoff compiled one of the first protein sequence databases, initially published as books[5] and pioneered methods of sequence alignment and molecular evolution.[6] Another early contributor to bioinformatics was Elvin A. Kabat, who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released with Tai Te Wu between 1980 and 1991.[7]


As whole genome sequences became available, again with the pioneering work of Frederick Sanger,[8] it became evident that computer-assisted analysis would be insightful. The first analysis of this type, which had important input from cryptologists at the National Security Agency, was applied to the nucleotide sequences of the bacteriophages MS2 and PhiX174. As a proof of principle, this work showed that standard methods of cryptology could reveal intrinsic features of the genetic code such as the codon length and the reading frame. This work seems to have been ahead of its time—it was rejected for publication by numerous standard journals and finally found a home in the Journal of Theoretical Biology.[9] The term bioinformatics was re-discovered and used to refer to the creation of databases such as GenBank in 1982. With public availability of data, tools for their analysis were quickly developed and described in journals, such as Nucleic Acids Research, which published specialized issues on bioinformatics tools as early as 1982.


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.[10] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include:
  • Development and implementation of computer programs that enable efficient access to, use and management of, various types of information
  • Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. 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 sequences.
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. 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, and the modeling of evolution.

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.

There are two fundamental ways of modelling a Biological system (e.g., living cell) both coming under Bioinformatic approaches.
  • Static
    • Sequences – Proteins, Nucleic acids and Peptides
    • Interaction data among the above entities including microarray data and Networks of proteins, metabolites
  • Dynamic
    • Structures – Proteins, Nucleic acids, Ligands (including metabolites and drugs) and Peptides (structures studied with bioinformatics tools are not considered static anymore and their dynamics is often the core of the structural studies)
    • Systems Biology comes under this category including reaction fluxes and variable concentrations of metabolites
    • Multi-Agent Based modelling approaches capturing cellular events such as signalling, transcription and reaction dynamics
A broad sub-category under bioinformatics is structural bioinformatics.

Relation to other fields

Bioinformatics is a science field that is similar to but distinct from biological computation and computational biology. Biological computation uses bioengineering and biology to build biological computers, whereas bioinformatics uses computation to better understand biology. Bioinformatics and computational biology have similar aims and approaches, but they differ in scale: bioinformatics organizes and analyzes basic biological data, whereas computational biology builds theoretical models of biological systems, just as mathematical biology does with mathematical models.

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.

Sequence analysis

The sequences of different genes or proteins may be aligned side-by-side to measure their similarity. This alignment compares protein sequences containing WPP domains.

Since the Phage Φ-X174 was sequenced in 1977,[11] the DNA sequences of thousands of organisms have been decoded and stored in databases. 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. Today, computer programs such as BLAST are used daily to search sequences from more than 260 000 organisms, containing over 190 billion nucleotides.[12] These programs can compensate for mutations (exchanged, deleted or inserted bases) in the DNA sequence, to identify sequences that are related, but not identical. A variant of this sequence alignment is used in the sequencing process itself. The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research to sequence the first bacterial genome, Haemophilus influenzae)[13] does not produce entire chromosomes. Instead it generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 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, and genome assembly algorithms are a critical area of bioinformatics research.

Another aspect of bioinformatics in sequence analysis is annotation. This involves computational gene finding to search for protein-coding genes, RNA genes, and other functional sequences within a genome. Not all of the nucleotides within a genome are part of genes. Within the genomes of higher organisms, large parts of the DNA do not serve any obvious purpose. This so-called junk DNA may, however, contain unrecognized functional elements. Bioinformatics helps to bridge the gap between genome and proteome projects — for example, in the use of DNA sequences for protein identification.

Genome annotation

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 genome annotation software system was designed in 1995 by Owen White, who was part of the team at The Institute for Genomic Research that sequenced and analyzed the first genome of a free-living organism to be decoded, the bacterium Haemophilus influenzae. White built a software system to find the genes (fragments of genomic sequence that encode proteins), the transfer RNAs, and to make initial assignments of function to those genes. 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.

Computational evolutionary biology

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:
  • trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
  • more recently, 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 models of populations to predict the outcome of the system over time[14]
  • 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.

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.

Comparative genomics

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. Ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectra 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 homology detection and protein families computation.

Genetics of disease

With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases such as infertility,[15] breast cancer [16] or Alzheimer's Disease.[17] Genome-wide association studies are essential to pinpoint the mutations for such complex diseases.[18]

Analysis of mutations in cancer

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.
Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

Gene and protein expression

Analysis of gene expression

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. 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. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

Analysis of protein expression

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.

Analysis of regulation

Regulation is the complex orchestration of events starting with an extracellular signal such as a hormone and leading 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, 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. 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. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). One can then apply clustering algorithms to that expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering methods such as the Bi-CoPaM. The later, namely Bi-CoPaM, has been actually proposed to address various issues specific to gene discovery problems such as consistent co-expression of genes over multiple microarray datasets.[19][20]

Structural bioinformatics

Protein structure prediction is another important application of bioinformatics. The amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. In the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (Of course, there are exceptions, such as the bovine spongiform encephalopathy – a.k.a. Mad Cow Diseaseprion.) Knowledge of this structure is vital in understanding the function of the protein. Structural information is usually classified as one of secondary, tertiary and quaternary structure. A viable general solution to such predictions remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.
One of the key ideas in bioinformatics is the notion of homology. 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. 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. 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 currently remains the only way to predict protein structures reliably.

One example of this is the similar protein homology between hemoglobin in humans and the hemoglobin in legumes (leghemoglobin). Both serve the same purpose of transporting oxygen in the organism. Though both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes.

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

Network and systems biology

Network analysis seeks to understand the relationships within biological networks such as metabolic or protein-protein interaction networks. 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.

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes that comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

Molecular interaction networks

Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein-protein interactions from Treponema pallidum, the causative agent of syphilis and other diseases.

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. 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.

Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions.


Literature analysis

The growth in the number of published literature makes it virtually impossible to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. 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.

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 biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research.
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. the Sulston score

High-throughput single cell data analysis

Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.

Biodiversity informatics

Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases, or microbiome data. Examples of such analyses include phylogenetics, niche modelling, species richness mapping, or species identification tools.


Databases are essential for bioinformatics research and applications. There is a huge number of available databases covering almost everything from DNA and protein sequences, molecular structures, to phenotypes and biodiversity. Databases generally fall into one of three types. Some contain data resulting directly from empirical methods such as gene knockouts. Others consist of predicted data, and most contain data from both sources. There are meta-databases that incorporate data compiled from multiple other databases. Some others are specialized, such as those specific to an organism. These databases vary in their format, way of accession and whether they are public or not. Some of the most commonly used databases are listed below. For a more comprehensive list, please check the link at the beginning of the subsection. Please keep in mind that this is a quick sampling and generally most computation data is supported by wet lab data as well.

Software and tools

Software tools for bioinformatics range from simple command-line tools, to more complex graphical programs and standalone web-services available from various bioinformatics companies or public institutions.

Open-source bioinformatics software

Many free and open-source software tools have existed and continued to grow since the 1980s.[21] The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have helped to create opportunities for all research groups to contribute to both bioinformatics and the range of open-source software available, regardless of their funding arrangements. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications. They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration.

The range of open-source software packages includes titles such as Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Apache Taverna, and UGENE. To maintain this tradition and create further opportunities, the non-profit Open Bioinformatics Foundation[21] have supported the annual Bioinformatics Open Source Conference (BOSC) since 2000.[22]

Web services in bioinformatics

SOAP- and REST-based interfaces have been developed for a wide variety of bioinformatics applications allowing an application running on one computer in one part of the world to use algorithms, data and computing resources on servers in other parts of the world. The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads.

Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis).[23] The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single, standalone or web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.

Bioinformatics workflow management systems

A Bioinformatics workflow management system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a Bioinformatics application. Such systems are designed to
  • provide an easy-to-use environment for individual application scientists themselves to create their own workflows
  • provide interactive tools for the scientists enabling them to execute their workflows and view their results in real-time
  • simplify the process of sharing and reusing workflows between the scientists.
  • enable scientists to track the provenance of the workflow execution results and the workflow creation steps.
Some of the platforms giving this service: Galaxy, Kepler, Taverna, UGENE, Anduril.

Education platforms

Software platforms designed to teach bioinformatics concepts and methods include Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. The Canadian Bioinformatics Workshops provides videos and slides from training workshops on their website under a Creative Commons license.


There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), Research in Computational Molecular Biology (RECOMB) and American Society of Mass Spectrometry (ASMS).


From Wikipedia, the free encyclopedia

In biology, phylogenetics /flɵɪˈnɛtɪks/ is the study of evolutionary relationships among groups of organisms (e.g. species, populations), which are discovered through molecular sequencing data and morphological data matrices. The term phylogenetics derives from the Greek terms phylé (φυλή) and phylon (φῦλον), denoting "tribe", "clan", "race"[1] and the adjectival form, genetikós (γενετικός), of the word genesis (γένεσις) "origin", "source", "birth".

In fact, phylogenesis is the process, phylogeny is science on this process, and phylogenetics is phylogeny based on analysis of sequences of biological macromolecules (DNA, RNA and proteins, in the first).[2] The result of phylogenetic studies is a hypothesis about the evolutionary history of taxonomic groups: their phylogeny.[3]

Evolution is a process whereby populations are altered over time and may split into separate branches, hybridize together, or terminate by extinction. The evolutionary branching process may be depicted as a phylogenetic tree, and the place of each of the various organisms on the tree is based on a hypothesis about the sequence in which evolutionary branching events occurred. In historical linguistics, similar concepts are used with respect to relationships between languages; and in textual criticism with stemmatics.

Phylogenetic analyses have become essential to research on the evolutionary tree of life. For example, the RedToL aims at reconstructing the Red Algal Tree of Life. The National Science Foundation sponsors a project called the Assembling the Tree of Life (AToL) activity. The goal of this project is to determine evolutionary relationships across large groups of organisms throughout the history of life. The research on this project often involves large teams working across institutions and disciplines, and typically provides support to investigators working on computational phylogenetics and phyloinformatics tasks, including data acquisition, analysis, and algorithm development and dissemination.

Taxonomy—the classification, identification and naming of organisms—is usually richly informed by phylogenetics, but remains a methodologically and logically distinct discipline.[4] The degree to which taxonomies depend on phylogenies differs depending on the school of taxonomy: phenetics ignores phylogeny altogether, trying to represent the similarity between organisms instead; cladistics (phylogenetic systematics) tries to reproduce phylogeny in its classification without loss of information; evolutionary taxonomy tries to find a compromise between them in order to represent stages of evolution.

Construction of a phylogenetic tree

The scientific methods of phylogenetics are often grouped under the term cladistics. The most common ones are parsimony, maximum likelihood (ML), and MCMC-based Bayesian inference. All methods depend upon an implicit or explicit mathematical model describing the evolution of characters observed in the species included; all can be, and are, used for molecular data, wherein the characters are aligned nucleotide or amino acid sequences, and all but maximum likelihood (see below) can be, and are, used for phenotypic (morphological, chemical, and physiological) data (also called classical or traditional data).

Phenetics, popular in the mid-20th century but now largely obsolete, uses distance matrix-based methods to construct trees based on overall similarity in morphology or other observable traits (i.e. in the phenotype, not the DNA), which was often assumed to approximate phylogenetic relationships.

A comprehensive step-by-step protocol on constructing phylogenetic tree, including DNA/Amino Acid contiguous sequence assembly, multiple sequence alignment, model-test (testing best-fitting substitution models) and phylogeny reconstruction using Maximum Likelihood and Bayesian Inference, is available at Nature Protocol[5]

Prior to 1990, phylogenetic inferences were generally presented as narrative scenarios. Such methods are legitimate, but often ambiguous and hard to test.[6][7][8]

Limitations and workarounds

Ultimately, there is no way to measure whether a particular phylogenetic hypothesis is accurate or not, unless the true relationships among the taxa being examined are already known (which may happen with bacteria or viruses under laboratory conditions). The best result an empirical phylogeneticist can hope to attain is a tree with branches that are well supported by the available evidence. Several potential pitfalls have been identified:


Certain characters are more likely to evolve convergently than others; logically, such characters should be given less weight in the reconstruction of a tree.[9] Weights in the form of a model of evolution can be inferred from sets of molecular data, so that maximum likelihood or Bayesian methods can be used to analyze them. For molecular sequences, this problem is exacerbated when the taxa under study have diverged substantially. As time since the divergence of two taxa increase, so does the probability of multiple substitutions on the same site, or back mutations, all of which result in homoplasies. For morphological data, unfortunately, the only objective way to determine convergence is by the construction of a tree – a somewhat circular method. Even so, weighting homoplasious characters[how?] does indeed lead to better-supported trees.[9] Further refinement can be brought by weighting changes in one direction higher than changes in another; for instance, the presence of thoracic wings almost guarantees placement among the pterygote insects because, although wings are often lost secondarily, there is no evidence that they have been gained more than once.[10]

Horizontal gene transfer

In general, organisms can inherit genes in two ways: vertical gene transfer and horizontal gene transfer. Vertical gene transfer is the passage of genes from parent to offspring, and horizontal (also called lateral) gene transfer occurs when genes jump between unrelated organisms, a common phenomenon especially in prokaryotes; a good example of this is the acquired antibiotic resistance as a result of gene exchange between various bacteria leading to multi-drug-resistant bacterial species. There have also been well-documented cases of horizontal gene transfer between eukaryotes.

Horizontal gene transfer has complicated the determination of phylogenies of organisms, and inconsistencies in phylogeny have been reported among specific groups of organisms depending on the genes used to construct evolutionary trees. The only way to determine which genes have been acquired vertically and which horizontally is to parsimoniously assume that the largest set of genes that have been inherited together have been inherited vertically; this requires analyzing a large number of genes.

Taxon sampling

Owing to the development of advanced sequencing techniques in molecular biology, it has become feasible to gather large amounts of data (DNA or amino acid sequences) to infer phylogenetic hypotheses. For example, it is not rare to find studies with character matrices based on whole mitochondrial genomes (~16,000 nucleotides, in many animals). However, simulations have shown that it is more important to increase the number of taxa in the matrix than to increase the number of characters, because the more taxa there are, the more accurate and more robust is the resulting phylogenetic tree.[11][12] This may be partly due to the breaking up of long branches.

Phylogenetic signal

Another important factor that affects the accuracy of tree reconstruction is whether the data analyzed actually contain a useful phylogenetic signal, a term that is used generally to denote whether a character evolves slowly enough to have the same state in closely related taxa as opposed to varying randomly. Tests for phylogenetic signal exist.[13]

Continuous characters

Morphological characters that sample a continuum may contain phylogenetic signal, but are hard to code as discrete characters. Several methods have been used, one of which is gap coding, and there are variations on gap coding.[14] In the original form of gap coding:[14]
group means for a character are first ordered by size. The pooled within-group standard deviation is calculated … and differences between adjacent means … are compared relative to this standard deviation. Any pair of adjacent means is considered different and given different integer scores … if the means are separated by a "gap" greater than the within-group standard deviation … times some arbitrary constant.
If more taxa are added to the analysis, the gaps between taxa may become so small that all information is lost. Generalized gap coding works around that problem by comparing individual pairs of taxa rather than considering one set that contains all of the taxa.[14]

Missing data

In general, the more data that are available when constructing a tree, the more accurate and reliable the resulting tree will be. Missing data are no more detrimental than simply having fewer data, although the impact is greatest when most of the missing data are in a small number of taxa. Concentrating the missing data across a small number of characters produces a more robust tree.[15]

The role of fossils

Because many characters involve embryological, or soft-tissue or molecular characters that (at best) hardly ever fossilize, and the interpretation of fossils is more ambiguous than that of living taxa, extinct taxa almost invariably have higher proportions of missing data than living ones. However, despite these limitations, the inclusion of fossils is invaluable, as they can provide information in sparse areas of trees, breaking up long branches and constraining intermediate character states; thus, fossil taxa contribute as much to tree resolution as modern taxa.[16] Fossils can also constrain the age of lineages and thus demonstrate how consistent a tree is with the stratigraphic record;[17] stratocladistics incorporates age information into data matrices for phylogenetic analyses.


The term "phylogeny" derives from the German Phylogenie, introduced by Haeckel in 1866.[18]

Ernst Haeckel's recapitulation theory

During the late 19th century, Ernst Haeckel's recapitulation theory, or "biogenetic fundamental law", was widely accepted. It was often expressed as "ontogeny recapitulates phylogeny", i.e. the development of an organism successively mirrors the adult stages of successive ancestors of the species to which it belongs. This theory has long been rejected.[19][20] Instead, ontogeny evolves – the phylogenetic history of a species cannot be read directly from its ontogeny, as Haeckel thought would be possible, but characters from ontogeny can be (and have been) used as data for phylogenetic analyses; the more closely related two species are, the more apomorphies their embryos share.

Timeline of key events

Branching tree diagram from Heinrich Georg Bronn'swork,(1858)

Phylogenetic tree suggested by Haeckel(1866)
  • 1300s, lex parsimoniae (parsimony principle), William of Ockam, English philosopher, theologian, and Franciscan monk, but the idea actually goes back to Aristotle, precursor concept
  • 1763, Bayesian probability, Rev. Thomas Bayes,[21] precursor concept
  • 1700s, Pierre Simon (Marquis de Laplace), perhaps 1st to use ML (maximum likelihood), precursor concept
  • 1809, evolutionary theory, Philosophie Zoologique, Jean-Baptiste de Lamarck, precursor concept, foreshadowed in the 1600s and 1700s by Voltaire, Descartes, and Leibniz, with Leibniz even proposing evolutionary changes to account for observed gaps suggesting that many species had become extinct, others transformed, and different species that share common traits may have at one time been a single race,[22] also foreshadowed by some early Greek philosophers such as Anaximander in the 6th century BC and the atomists of the 5th century BC, who proposed rudimentary theories of evolution[23]
  • 1837, Darwin's notebooks show an evolutionary tree[24]
  • 1843, distinction between homology and analogy (the latter now referred to as homoplasy), Richard Owen, precursor concept
  • 1858, Paleontologist Heinrich Georg Bronn (1800–1862) published a hypothetical tree to illustrating the paleontological "arrival" of new, similar species following the extinction of an older species. Bronn did not propose a mechanism responsible for such phenomena, precursor concept.[25]
  • 1858, elaboration of evolutionary theory, Darwin and Wallace,[26] also in Origin of Species by Darwin the following year, precursor concept
  • 1866, Ernst Haeckel, first publishes his phylogeny-based evolutionary tree, precursor concept
  • 1893, Dollo's Law of Character State Irreversibility,[27] precursor concept
  • 1912, ML recommended, analyzed, and popularized by Ronald Fisher, precursor concept
  • 1921, Tillyard uses term "phylogenetic" and distinguishes between archaic and specialized characters in his classification system[28]
  • 1940, term "clade" coined by Lucien Cuénot
  • 1949, jackknife, Maurice Quenouille (foreshadowed in '46 by Mahalanobis and extended in '58 by Tukey), precursor concept
  • 1950, Willi Hennig's classic formalization[29]
  • 1952, William Wagner's groundplan divergence method[30]
  • 1953, "cladogenesis" coined[31]
  • 1960, "cladistic" coined by Cain and Harrison[32]
  • 1963, 1st attempt to use ML (maximum likelihood) for phylogenetics, Edwards and Cavalli-Sforza[33]
  • 1965
    • Camin-Sokal parsimony, 1st parsimony (optimization) criterion and 1st computer program/algorithm for cladistic analysis both by Camin and Sokal[34]
    • character compatibility method, also called clique analysis, introduced independently by Camin and Sokal (loc. cit.) and E.O. Wilson[35]
  • 1966
    • English translation of Hennig[36]
    • "cladistics" and "cladogram" coined (Webster's, loc. cit.)
  • 1969
    • dynamic and successive weighting, James Farris[37]
    • Wagner parsimony, Kluge and Farris[38]
    • CI (consistency index), Kluge and Farris[38]
    • introduction of pairwise compatibility for clique analysis, Le Quesne[39]
  • 1970, Wagner parsimony generalized by Farris[40]
  • 1971
    • Fitch parsimony, Fitch[41]
    • NNI (nearest neighbour interchange), 1st branch-swapping search strategy, developed independently by Robinson[42] and Moore et al.
    • ME (minimum evolution), Kidd and Sgaramella-Zonta[43] (it is unclear if this is the pairwise distance method or related to ML as Edwards and Cavalli-Sforza call ML "minimum evolution".)
  • 1972, Adams consensus, Adams[44]
  • 1974, 1st successful application of ML to phylogenetics (for nucleotide sequences), Neyman[45]
  • 1976, prefix system for ranks, Farris[46]
  • 1977, Dollo parsimony, Farris[47]
  • 1979
    • Nelson consensus, Nelson[48]
    • MAST (maximum agreement subtree)((GAS)greatest agreement subtree), a consensus method, Gordon [49]
    • bootstrap, Bradley Efron, precursor concept[50]
  • 1980, PHYLIP, 1st software package for phylogenetic analysis, Felsenstein
  • 1981
    • majority consensus, Margush and MacMorris[51]
    • strict consensus, Sokal and Rohlf[52]
    • 1st computationally efficient ML algorithm, Felsenstein[53]
  • 1982
    • PHYSIS, Mikevich and Farris
    • branch and bound, Hendy and Penny[54]
  • 1985
    • 1st cladistic analysis of eukaryotes based on combined phenotypic and genotypic evidence Diana Lipscomb[55]
    • 1st issue of Cladistics
    • 1st phylogentic application of bootstrap, Felsenstein[56]
    • 1st phylogenetic application of jackknife, Scott Lanyon[57]
  • 1986, MacClade, Maddison and Maddison
  • 1987, neighbor-joining method Saitou and Nei[58]
  • 1988, Hennig86 (version 1.5), Farris
  • 1989
    • RI (retention index), RCI (rescaled consistency index), Farris[59]
    • HER (homoplasy excess ratio), Archie[60]
  • 1990
    • combinable components (semi-strict) consensus, Bremer[61]
    • SPR (subtree pruning and regrafting), TBR (tree bisection and reconnection), Swofford and Olsen[62]
  • 1991
    • DDI (data decisiveness index), Goloboff[63][64]
    • 1st cladistic analysis of eukaryotes based only on phenotypic evidence, Lipscomb
  • 1993, implied weighting Goloboff[65]
  • 1994, Bremer support (decay index), Bremer[66]
  • 1994, reduced consensus: RCC (reduced cladistic consensus) for rooted trees, Wilkinson[67]
  • 1995, reduced consensus RPC (reduced partition consensus) for unrooted trees, Wilkinson[68]
  • 1996, 1st working methods for BI (Bayesian Inference)independently developed by Li,[69] Mau,[70] and Rannalla and Yang[71] and all using MCMC (Markov chain-Monte Carlo)
  • 1998, TNT (Tree Analysis Using New Technology), Goloboff, Farris, and Nixon
  • 1999, Winclada, Nixon
  • 2003, symmetrical resampling, Goloboff[72]