<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/1535? https://bioinformaticsonline.com/pages/view/1537/virtual-bioinformatics-online-tutorial Sat, 10 Aug 2013 08:01:55 -0500 https://bioinformaticsonline.com/pages/view/1537/virtual-bioinformatics-online-tutorial <![CDATA[Virtual Bioinformatics Online Tutorial]]> There are several vitual online bioinformatics training centres. Here I provide some of them:

 

]]> Jitendra Narayan https://bioinformaticsonline.com/pages/view/1533/introduction-to-bioinformatics Sat, 10 Aug 2013 07:12:54 -0500 https://bioinformaticsonline.com/pages/view/1533/introduction-to-bioinformatics <![CDATA[Introduction to Bioinformatics]]>
  • Bioinformatics (Genomics)
  • Biocomputing in a Nutshell.
  • Biologist's Guide to Internet Resources
  • Computational Molecular Biology Course
  • Course on Bioinformatics
  • EMBNet Biocomputing Tutorials
  • Finding the genes in the genomic sequences
  • The Genetic Programming Tutorial
  • Jose R. Valverde's training course documents
  • Principles of Computational Biology, Steven Salzberg.
  • Principles of Protein Structure Using the Internet
  • Practical Course "Bioinformatics: Computer Methods in Molecular Biology"
  • Sequence analysis course (José R, Valverde, EMBNet/CNB)
  • Bioinformatics -An excellent review on genetic code and information processing
  • Molecular Sequence Analysis -Introductory sequence analysis by Andrew S Louka
  • Homology Modelling -Protein and homology modelling for beginers
  • B iocompanion -Tutorial for sequence analysis
  • Bioinformatics and Genomic Analysis -Link to graduate student course at the university of Arizona
  • EMBnet Biocomputing Tutorials - Introduction
  • Integrative Bioinformatics: Practical Kinetic Modeling of Biological Systems
    • ]]>     Jitendra Narayan     https://bioinformaticsonline.com/pages/view/1536/useful-links-for-bioinformaticians Sat, 10 Aug 2013 07:56:06 -0500 https://bioinformaticsonline.com/pages/view/1536/useful-links-for-bioinformaticians <![CDATA[Useful Links for Bioinformaticians]]> Bioinformatics demands knowledge of various inter-linked subjects. This page provide links of some the good computer, biology and related tutorials. 

    Introduction to computer science

    Introduction to Internet

    Basics of HTML

    Java Tutorial

    Perl Tutorials

    XML Tutorial

    SQL Tutorial

    Oracle Tutorial

    C and C++ Tutorial

    CGI Tutorial

    Unix Tutorial

    Basics of Biology

    Basics of Chemistry

    Introduction to BioChemistry

    Understand DNA

    About RNA

    About Genome

     Similarity Search Tutorials

    Feel free to add more useful tutorial links for bioinformaticians in comment section. 

    ]]>     Jitendra Narayan     https://bioinformaticsonline.com/bookmarks/view/968/online-bioinformatics-courses Wed, 17 Jul 2013 12:59:59 -0500 https://bioinformaticsonline.com/bookmarks/view/968/online-bioinformatics-courses <![CDATA[Online Bioinformatics Courses]]> Bioinformatics is developing very rapidly. With new tools and software it is, now a days, easy to analyse the large scale genomics datasets. But we need to understand the background of software/tool to better explain the data computationally. 

    I found this online bioinformatics courses really interesting where they are covering the common algorithms underlying the following fundamental topics in bioinformatics: assembling genomes, comparing DNA and protein sequences, predicting genes, finding regulatory motifs, analyzing gene expression, constructing evolutionary trees, analyzing genome rearrangements, and identifying proteins.

    Please paste other useful online bioinformatics courses links below if you know any others.

    Address of the bookmark: https://www.coursera.org/course/bioinformatics

    ]]>     Jitendra Narayan     https://bioinformaticsonline.com/videolist/watch/2276/daniel-reda-an-introduction-to-biotechnology-and-bioinformatics Tue, 13 Aug 2013 19:03:22 -0500 https://bioinformaticsonline.com/videolist/watch/2276/daniel-reda-an-introduction-to-biotechnology-and-bioinformatics <![CDATA[Daniel Reda - An Introduction to Biotechnology and Bioinformatics]]> Daniel Reda, co-Chair of the Biotechnology and Bioinformatics track at Singularity University, introduces the key concepts and breakthroughs in biotechnology and bioinformatics. Filmed during the November 2009 Executive Program at Singularity University.]]> https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools Sat, 08 Aug 2020 08:25:21 -0500 https://bioinformaticsonline.com/news/view/42023/encode3-a-collection-of-research-articles-and-related-content-describing-the-encyclopedia-of-dna-elements-its-datasets-and-tools <![CDATA[ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.]]> How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

    During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

    ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

    The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

    More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

    ]]>     Shruti Paniwala     https://bioinformaticsonline.com/bookmarks/view/6300/list-of-bioinformatics-vacancy-jobs-opportunity-websites Tue, 12 Nov 2013 20:04:28 -0600 https://bioinformaticsonline.com/bookmarks/view/6300/list-of-bioinformatics-vacancy-jobs-opportunity-websites <![CDATA[List of Bioinformatics Vacancy, Jobs, Opportunity websites]]> Bioinformatics cover wide area of biology, and indulge in almost all sort of science related work. Bioinformatician give strong emphasis on open access to biological information as well as Free and Open Source software!!

    There are several jobs opening in bioinformatics all around the world, but many of them do not get proper attention due to lack of advertisements, or social connectivity. This bookmark is created for an academic, non-academic, scientists and budding researchers to help and updates the bioinformatics/computational biology jobs links of all know websites around the world.

    I also love to stream the live bioinformatics or Computational biology jobs updates using Twitter https://twitter.com/search?q=bioinformatics%20jobs&src=typd

    Find out here about exciting job opportunities in the life sciences.

    Please add well known bioinformatics jobs websites below in comment section.

    Address of the bookmark: http://www.nature.com/naturejobs/science/jobs?utf8=%E2%9C%93&q=bioinformatics&where=&commit=Find+Jobs

    ]]>     Jitendra Narayan     https://bioinformaticsonline.com/pages/view/901/bioinformatics-definitions Mon, 15 Jul 2013 03:01:07 -0500 https://bioinformaticsonline.com/pages/view/901/bioinformatics-definitions <![CDATA[Bioinformatics Definitions]]> "Bioinformatics is a science of biological predictions and analysis" -- Jitendra Narayan

    "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information."

    "The collection, organization and analysis of large amounts of biological data, using networks of computers and databases." - from the glossary for ABC Science Online's feature: The State of the Genome 2001.

    "It is defined here as an interdisciplinary research area that applies computer and information science to solve biological problems. However, this is not the only definition. The field is being defined (and redefined) at present, and there are probably as many definitions as there are bioinformaticians (bioinformaticists?).

    The following references are a snapshot of the moving target named bioinformatics. ... " - from the University of Minnesota Graduate Program in Bioinformatics' page: What is Bioinformatics,

    "The application of computer technology to the management of biological information.Bioinformatics uses computers to solve problems in the life sciences, such as determination of DNA and protein sequences, investigation of protein functions, development of pharmaceuticals. It involves the creation of extensive electronic databases on genomes and protein sequences, and techniques such as the three-dimensional modeling of biomolecules and biologic systems. ..." - from the Bioinformatics Glossary edited by Charles E. Kahn, Jr., Medical College of Wisconsin.

    "Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned." - from the National Center for Biotechnology Information's Bioinformatics Factsheet.

    "Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data." - NIH Bioinformatics Web site

    "The use of computers, laboratory robots and software to create, manage and interpret massive sets of complex biological data." - from the glossary for the University of Michigan Health System's Symphony of Life: Genetics & Medicine Web site.

    "The field of science in which biology, computer science, and information technology merge into a single discipline.There are three important sub-disciplines within bioinformatics: (1) the development of new algorithms and statistics with which to assess relationships among members of large data sets; (2) the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and (3) the development and implementation of tools that enable efficient access and management of different types of information." - U.S. Environmental Protection Agency's ComputationalToxicology Research Glossary.

    What is Bioinformatics? "One idea for a definition: (Molecular) Bio - informatics = is conceptualizing biology in terms of molecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied math, CS, and statistics) to understand and organize the information associated with these molecules, on a large-scale." - By Mark Gerstein, Gerstein Group - Yale Bioinformatics.

    Bioinformatics

    Definition:

    Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine.

    Description:

    The history of computing in biology goes back to the 1920s when scientists were already thinking of establishing biological laws solely from data analysis by induction (e.g. A.J. Lotka, Elements of Physical Biology, 1925). However, only the development of powerful computers, and the availability of experimental data that can be readily treated by computation (for example, DNA or amino acid sequences and three–dimensional structures of proteins) launched bioinformatics as an independent field. Today, practical applications of bioinformatics are readily available through the world wide web, and are widely used in biological and medical research. As the field is rapidly evolving, the very definition of bioinformatics is still the matter of some debate.

    The relationship between computer science and biology is a natural one for several reasons. First, the phenomenal rate of biological data being produced provides challenges: massive amounts of data have to be stored, analysed, and made accessible. Second, the nature of the data is often such that a statistical method, and hence computation, is necessary. This applies in particular to the information on the building plans of proteins and of the temporal and spatial organisation of their expression in the cell encoded by the DNA. Third, there is a strong analogy between the DNA sequence and a computer program (it can be shown that the DNA represents a Turing Machine).

    Analyses in bioinformatics focus on three types of datasets: genome sequences, macromolecular structures, and functional genomics experiments (e.g. expression data, yeast two–hybrid screens). But bioinformatic analysis is also applied to various other data, e.g. taxonomy trees, relationship data from metabolic pathways, the text of scientific papers, and patient statistics. A large range of techniques are used, including primary sequence alignment, protein 3D structure alignment, phylogenetic tree construction, prediction and classification of protein structure, prediction of RNA structure, prediction of protein function, and expression data clustering. Algorithmic development is an important part of bioinformatics, and techniques and algorithms were specifically developed for the analysis of biological data (e.g., the dynamic programming algorithm for sequence alignment).

    Bioinformatics has a large impact on biological research. Giant research projects such as the human genome project [4] would be meaningless without the bioinformatics component. The goal of sequencing projects, for example, is not to corroborate or refute a hypothesis, but to provide raw data for later analysis. Once the raw data are available, hypotheses may be formulated and tested in silico. In this manner, computer experiments may answer biological questions which cannot be tackled by traditional approaches. This has led to the founding of dedicated bioinformatics research groups as well as to a different work practice in the average bioscience laboratory where the computer has become an essential research tool.

    Three key areas are the organisation of knowledge in databases, sequence analysis, and structural bioinformatics.

    Organizing biological knowledge in databases:

    Biological raw data are stored in public databanks (such as Genbank or EMBL for primary DNA sequences). The data can be submitted and accessed via the world wide web. Protein sequence databanks like trEMBL provide the most likely translation of all coding sequences in the EMBL databank. Sequence data are prominent, but also other data are stored, e. g. yeast two–hybrid screens, expression arrays, systematic gene–knock–out experiments, and metabolic pathways.

    The stored data need to be accessed in a meaningful way, and often contents of several databanks or databases have to be accessed simultaneously and correlated with each other. Special languages have been developed to facilitate this task (such as the Sequence Retrieval System (SRS) and the Entrez system). An unsolved problem is the optimal design of inter–operating database systems. Databases provide additional functionality such as access to sequence homology searches and links to other databases and analysis results. For example, SWISSPROT [1] contains verified protein sequences and more annotations describing the function of a protein. Protein 3D structures are stored in specific databases (for example, the Protein Data Bank [2], now primarily curated and developed by the Research Collaboratory for Structural Bioinformatics). Organism specific databases have been developed (such as ACEDB, the A C. Elegans DataBase for the C. elegans genome, FLYBASE for D. melanogaster etc). A major problem are errors in databanks and databases (mostly errors in annotation), in particular since errors propagate easily through links.

    Also databases of scientific literature (such as PUBMED, MEDLINE) provide additional functionality, e.g. they can search for similar articles based on word–usage analysis. Text recognition systems are being developed that extract automatically knowledge about protein function from the abstracts of scientific articles, notably on protein–protein interactions.

    Analysing sequence data:

    The primary data of sequencing projects are DNA sequences. These become only really valuable through their annotation. Several layers of analysis with bioinformatics tools are necessary to arrive from a raw DNA sequence at an annotated protein sequences:

    • establish the correct order of sequence contigs to obtain one continuous sequence;
    • find the tranlation and transcription initiation sites, find promoter sites, define open reading frames (ORF);
    • find splice sites, introns, exons;
    • translate the DNA sequence into a protein sequence, searching all six frames;
    • compare the DNA sequence to known protein sequences in order to verify exons etc with homologuous sequences.

    Some completely automated annotation systems have been developed (e.g., GENEQUIZ), which use a multitude of different programs and methods.

    The protein sequences are further analysed to predict function. The function can often be inferred if a sequence of a homologous protein with known function can be found. Homology searches are the predominant bioinformatics application, and very efficient search methods have been developed [3]. The often difficult distinction between orthologous sequences and paralogous sequences facilitates the functional annotation in the comparison of whole genomes. Several methods detect glycolysation, myristylation and other sites, and the prediction of signal peptides in the amino acid sequence give valuable information about the subcellular location of a protein.

    The ultimate goal of sequence annotation is to arrive at a complete functional description of all genes of an organism. However, function is an ill–defined concept. Thus, the simplified idea of “one gene – one protein – one structure – one function” cannot take into account proteins that have multiple functions depending on context (e.g., subcellar location and the presence of cofactors). Well-known cases of “moonlighting” proteins are lens crystalline and phosphoglucose isomerase. Currently, work on ontologies is under way to explicitly define a vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing.

    Families of similar sequences contain information on sequence evolution in the form of specific conservation patters at all sequence positions. Multiple sequence alignments are useful for

    • building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position. improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [3];
    • studying evolutionary aspects, by the construction of phylogenetic trees from the pairwise differences between sequences: for example, the classification with 70S, 30S RNAs established the separate kingdom of archeae;
    • determining active site residues, and residues specifc for subfamilies;
    • predicting protein–protein interactions;
    • analysing single nucleotide polymorphisms to hunt for genetic sources of deseases.
    • Many complete genomes of microorganisms and a few of eukaryotes are available [4]. By analysis of entire genome sequences a wealth of additional information can be obtained. The complete genomic sequence contains not only all protein sequences but also sequences regulating gene expression. A comparison of the genomes of genetically close organisms reveals genes responsible for specific properties of the organisms (e.g., infectivity). Protein interactions can be predicted from conservation of gene order or operon organisation in different genomes. Also the detection of gene fusion and gene fission (i.e, one protein is split into two in another genome) events helps to deduce protein interactions.

    Structural bioinformatics:

    This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the three–dimensional (3D) structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70 %.

    3D models can be obtained most easily if the 3D structure of a homologous protein is known (homology modelling, comparative modelling). A homology model can only be as good as the sequence alignment: whereas protein relationships can be detected at the 20% identity level and below, a correct sequence alignment becomes very difficult, and the homology model will be doubtful. From 40 to 50% identity the models are usually mostly correct; however, it is possible to have 50% identity between two carefully designed protein sequences with different topology (the so –called JANUS protein). Remote relationships that are undetectable by sequence comparisons may be detected by sequence–to–structure–fitness (or threading) approaches: the search sequence is systematically compared to all known protein structures. Ab initio predictions of protein 3D structure remains the major challenge; some progress has been made recently by combining statistical with force–field based approaches.

    Membrane proteins are interesting drug targets. It is estimated that membrane receptors form 50 % of all drug targets in pharmacological research. However, membrane proteins are underrepresented in the PDB structure database. Since membrane proteins are usually excluded from structural genomics initiatives due to technical problems, the prediction of transmembrane helices and solvent accessibility is very important. Modern methods can predict transmembrane helices with a reliability greater than 70 %.

    Understanding the 3D structure of a macromolecule is crucial for understanding its function. Many properties of the 3D structure cannot be deduced directly from the primary sequence. Obtaining better understanding of protein function is the driving force behind structural genomics efforts, which can be thus understood as part of functional genomics. Similar structure can imply similar function. General structure–to–function relationships can be obtained by statistical approaches, for example, by relating secondary structure to known protein function or surface properties to cell location.

    The increased speed of structure determination necessary for the structural genomics projects make an independent validation of the structures (by comparison to expected properties) particularly important. Structure validation helps to correct obvious errors (e.g., in the covalent structure) and leads to a more standardized representation of structural data, e.g., by agreeing on a common atom name nomenclature. The knowledge of the structure quality is a prerequisite for further use of the structure, e.g in molecular modelling or drug design.

    In order to make as much data on the structure and its determination available in the databases, approaches for automated data harvesting are being developed. Structure classification schemes, as implemented for example in the SCOP, CATH, and FSSP databases, elucidate the relationship between protein folds and function and shed light on the evolution of protein domains.

    Combined analysis of structural and genomic data will certainly get more important in the near future. Protein folds can be analysed for whole genomes. Protein–protein interactions predicted on the sequence level, can be studied in more detail on the structure level. Single Nucleotide Polymorphisms can be mapped on 3D structures of proteins in order to elucidate specific structural causes of disease.

    More detailed aspects of protein function can be obtained also by force–field based approaches. Whereas protein function requires protein dynamics, no experimental technique can observe it directly on an atomic scale, and motions have to be simulated by molecular dynamics (MD) simulations. Also free energy differences (for example between binding energies of different protein ligands) can be characterized by MD simulations. Molecular mechanics or molecular dynamics based approaches are also necessary for homology modelling and for structure refinement in X–ray crystallography and NMR structure determination.

    Drug design exploits the knowledge of the 3D structure of the binding site (or the structure of the complex with a ligand) to construct potential drugs, for example inhibitors of viral proteins or RNA. In addition to the 3D structure, a force field is necessary to evaluate the interaction between the protein and a ligand (to predict binding energies). In virtual screening, a library of molecules is tested on the computer for their capacities to bind to the macromolecule.

    Pharmacological Relevance:

    Many aspects of bioinformatics are relevant for pharmacology. Drug targets in infectious organisms can be revealed by whole genome comparisons of infectious and non–infectious organisms. The analysis of single nucleotide polymorphisms reveals genes potentially responsible for genetic deseases. Prediction and analysis of protein 3D structure is used to develop drugs and understand drug resistance.

    Patient databases with genetic profiles, e.g. for cardiovascular diseases, diabetes, cancer, etc. may play an important role in the future for individual health care, by integrating personal genetic profile into diagnosis, despite obvious ethical problems. The goal is to analyse a patient’s individual genetic profile and compare it with a collection of reference profiles and other related information. This may improve individual diagnosis, prophylaxis, and therapy.

    References:

    Bairoch A, Apweiler R (2000) The SWISS–PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28:45–48
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res. 28:235–42
    Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI–BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389–3402
    Pearson WR (2000) Flexible sequence similarity searching with the FASTA3 program package. Methods Mol. Biol. 132:185–219
    The Genome International Sequencing Consortium (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921
    JC Venter et al. (2001) The sequence of the human genome. Science 291:1304–1351
    R.D. Fleischmann et al. (1995) Whole–genome random sequencing and assembly of haemophilus–influenzae. Science 269:496–51

    ]]>     Jitendra Narayan     https://bioinformaticsonline.com/pages/view/34146/phylogenetic-molecular-genetics-terms-and-definitions Tue, 08 Aug 2017 08:20:31 -0500 https://bioinformaticsonline.com/pages/view/34146/phylogenetic-molecular-genetics-terms-and-definitions <![CDATA[Phylogenetic & Molecular Genetics Terms and Definitions]]> analog -- A feature that appears similar in two taxa which have originated from two different ancestors.

    ancestor -- Any organism, population, or species from which some other organism, population, or species is descended by reproduction.

    apomorphy -- specialized (=derived) characters of an organism.

    basal group -- The earliest diverging group within a clade; for instance, to hypothesize that sponges are basal animals is to suggest that the lineage(s) leading to sponges diverged from the lineage that gave rise to all other animals.

    biological classification -- The orderly arrangement of organisms in hierarchical system that ideally reflects evolutionary history.

    cDNA -- Complementary DNA; DNA that is synthesized, by reverse transcriptase, from a Messenger RNA template ( Messenger RNA contains the coded information for protein synthesis).

    character -- Heritable trait possessed by an organism.

    character state -- characters are usually described in terms of their states, for example: "hair present" vs. "hair absent," where "hair" is the character, and "present" and "absent" are its states.

    clade -- A monophyletic taxon; a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. From the Greek word "klados", meaning branch or twig.

    cladogenesis -- The development of a new clade; the splitting of a single lineage into two distinct lineages; speciation.

    cladogram -- A diagram, resulting from a cladistic analysis, which depicts a hypothetical branching sequence of lineages leading to the taxa under consideration. The points of branching within a cladogram are called nodes. All taxa occur at the endpoints of the cladogram.

    convergence -- Similarities which have arisen independently in two or more organisms that are not closely related. Contrast with homology. 

    crown group -- All the taxa descended from a major cladogenesis event, recognized by possessing the clade's synapomorphy. See: stem group.

    derived -- Describes a character state that is present in one or more subclades, but not all, of a clade under consideration. A derived character state is inferred to be a modified version of the primitive condition of that character, and to have arisen later in the evolution of the clade. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

    diversity -- Term used to describe numbers of taxa, or variation in morphology. 

    evolution -- Darwin's definition: descent with modification. The term has been variously used and abused since Darwin to include everything from the origin of man to the origin of life.

    evolutionary tree -- A diagram which depicts the hypothetical phylogeny of the taxa under consideration. The points at which lineages split represent ancestor taxa to the descendant taxa appearing at the terminal points of the cladogram.

    expressed sequence tag (EST) -- A partial coding sequence isolated at random from a cDNA library, used for identification and mapping of coding sequences, for discovery of new genes and (by reference to sequence data banks) for discovery of identities with other genes.

    extinction -- When all the members of a clade or taxon die, the group is said to be extinct.

    genetic marker -- A DNA sequence that can be recognized and thus used to characterize the larger DNA sequence and the chromosome in which it occurs. 

    homolog -- A feature that appears similar in two or more taxa with a common ancestor that also possessed that feature.

    homology -- Two structures are considered homologous when they are inherited from a common ancestor which possessed the structure. This may be difficult to determine when the structure has been modified through descent.

    hypothesis -- A concept or idea that can be falsified by various scientific methods.

    ingroup -- In a cladistic analysis, the set of taxa which are hypothesized to be more closely related to each other than any are to the outgroup.

    lineage -- Any continuous line of descent; any series of organisms connected by reproduction by parent of offspring.

    monophyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. A monophyletic group is called a clade.

    outgroup -- In a cladistic analysis, any taxon used to help resolve the polarity of characters, and which is hypothesized to be less closely related to each of the taxa under consideration than any are to each other.

    paraphyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members, but not all of the descendants of that most recent common ancestor.

    parsimony -- Refers to a rule used to choose among possible cladograms, which states that the cladogram implying the least number of changes in character states is the best.

    phylogenetics -- Field of biology that deals with the relationships between organisms. It includes the discovery of these relationships, and the study of the causes behind this pattern.

    phylogeny -- The evolutionary relationships among organisms; the patterns of lineage branching produced by the true evolutionary history of the organisms being considered.

    plesiomorphy -- A primitive character state for the taxa under consideration.

    polarity of characters -- The states of characters used in a cladistic analysis, either original or derived. Original characters are those acquired by an ancestor deeper in the phylogeny than the most recent common ancestor of the taxa under consideration. Derived characters are those acquired by the most recent common ancestor of the taxa under consideration.

    polyphyletic -- Term applied to a group of organisms which does not include the most recent common ancestor of those organisms; the ancestor does not possess the character shared by members of the group.

    primitive -- Describes a character state that is present in the common ancestor of a clade. A primitive character state is inferred to be the original condition of that character within the clade under consideration. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

    radiation -- Event of rapid cladogenesis, believed to occur under conditions where a new feature permits a lineage to move into a new niche or new habitat, and is then called an adaptive radiation.

    rank -- In traditional taxonomy, taxa are ranked according to their level of inclusiveness. Thus a genus contains one or more species, a family includes one or more genera, and so on.

    relatedness -- Two clades are more closely related when they share a more recent common ancestor between them than they do with any other clade.

    repetitive DNA -- Sequences of DNA that are found to be repeated, sometimes thousands of times over.  

    reticulation -- Joining of separate lineages on a phylogenetic tree, generally through hybridization or through lateral gene transfer. Fairly common in certain land plant clades; reticulation is thought to be rare among metazoans.

    selection -- Process which favors one feature of organisms in a population over another feature found in the population. This occurs through differential reproduction -- those with the favored feature produce more offspring than those with the other feature, such that they become a greater percentage of the population in the next generation.

    sister group -- The two clades resulting from the splitting of a single lineage.

    stem group -- All the taxa in a clade preceding a major cladogenesis event. They are often difficult to recognize because they may not possess synapomorpies found in the crown group.

    sympleisiomorphy – A ancestral character shared by the taxa under consideration

    synapomorphy -- A character which is derived, and because it is shared by the taxa under consideration, is used to infer common ancestry (shared derived state).

    synteny -- Portions of chromosomes in which gene order is conserved. 

    systematics -- Field of biology that deals with the diversity of life. Systematics is usually divided into the two areas of phylogenetics and taxonomy.

    taxon -- Any named group of organisms, not necessarily a clade

    taxonomy -- The science of naming and classifying organisms. 

    ]]>     Poonam Mahapatra