Disease Informatics: Brush up the terms describing techniques and resources

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Disease Informatics Brush up the terms
describing techniques and resources

• R. P. Deolankar

Half knowledge is always dangerous
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Wet lab

• A laboratory allowing for hands-on scientific
  research and equipped with
• Appropriate plumbing
• Ventilation
• Equipment

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High-throughput technology

• The technology handling high volume of data or
  material
• Large-scale methods to purify, identify, and
  characterize DNA, RNA, proteins and other
  molecules. These methods are usually automated,
  allowing rapid analysis of very large numbers of
  samples.

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Microarray

• A tool used to sift through and analyze the
  information contained within a genome. A
  microarray consists of different nucleic acid
  probes that are chemically attached to a
  substrate, which can be a microchip, a glass
  slide or a microsphere-sized bead.

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DNA microarray

• A microarray of immobilized single-stranded DNA
  fragments of known nucleotide sequence that is
  used especially in the identification and
  sequencing of DNA samples and in the analysis of
  gene expression (as in a cell or tissue)

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Protein microarray

• Protein microarray is a piece of glass on which
  different molecules of protein have been affixed
  at separate locations in an ordered manner thus
  forming a microscopic array.

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Mass spectrometry

• An instrumental method for identifying the
  chemical constitution of a substance by means of
  the separation of gaseous ions according to their
  differing mass and charge -- called also mass
  spectroscopy
• Mass spectrometry A method used to determine the
  masses of atoms or molecules in which an
  electrical charge is placed on the molecule and
  the resulting ions are separated by their mass to
  charge

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Tandem mass spectrometry

• Multiple steps of mass spectrometry selection,
  with some form of fragmentation occurring in
  between the stages
• Immunofluorescence and immunocytochemistry,
  ELISA, immunoblotting

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Dry lab

• A laboratory for making computer simulations or
  for data analysis especially by computers (as in
  bioinformatics)called also dry laboratory

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Gene prioritization

• The results of experimental or computational
  analyses in the post-genomic era (e.g., those
  from microarrays, proteomics, ChIP-chip,
  genome-wide in silico searches, genetic linkages,
  etc.) often consist of long lists of candidate
  genes. There are methods that provide score to
  the gene and rank them. This process is known as
  gene prioritization.

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PhenoGO

• PhenoGO is a multiorganism database that provides
  phenotypic context, such as the cell type,
  disease, and tissue and organ to existing
  associations between gene products and Gene
  Ontology (GO) terms as specified in the Gene
  Ontology Annotations (GOA).

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BioMedLEE

• One existing Natural Language Processing (NLP)
  system, known as BioMedLEE, automatically
  extracts biological information consisting of
  bio-molecular substances and phenotypic data.

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MeSH

• Medical Subject Heading
• MeSH is the National Library of Medicine's
  controlled vocabulary thesaurus. It consists of
  sets of terms naming descriptors in a
  hierarchical structure that permits searching at
  various levels of specificity.

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PhenOS

• Phenotype Organizer System, PhenOS is a system
  under development by the Lussier research group
  with purpose of bridging the gap between
  heterogeneous biomedical terminologies.

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Inparanoid algorithm

• The protein interaction networks of two species
  are aligned by assigning proteins to sequence
  homology clusters using the Inparanoid algorithm

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POCUS

• Prioritization of candidate genes using
  statistics
• Reference Turner FS, Clutterbuck DR, Semple CA.
  POCUS mining genomic sequence annotation to
  predict disease genes. Genome Biol.
  20034(11)R75.

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OMIM

• Mendelian Inheritance in Man
• The Online Mendelian Inheritance in Man. A
  catalog of human genes and genetic disorders
  authored and edited by Dr. Victor A. McKusick and
  his colleagues at Johns Hopkins and elsewhere,
  and provided through NCBI. The database contains
  information on disease phenotypes and genes,
  including extensive descriptions, gene names,
  inheritance patterns, map locations and gene
  polymorphisms.

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TOM

• A web-based integrated approach for
  identification of candidate disease genes,
  Transcriptomics of OMIM
• Reference Rossi S, Masotti D, Nardini C, Bonora
  E, Romeo G, Macii E, Benini L, Volinia S. TOM a
  web-based integrated approach for identification
  of candidate disease genes. Nucleic Acids Res.
  2006 Jul 134

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Data mining

• Data mining (sometimes called data or knowledge
  discovery) is the process of analyzing data from
  different perspectives and summarizing it into
  useful information

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Online Predicted Human Interactions Database or
OPHID

• Designed to be both a resource for the laboratory
  scientist to explore known and predicted
  protein-protein interactions, and to facilitate
  bioinformatics initiatives exploring protein
  interaction networks.

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Single nucleotide polymorphisms (SNPs)

• A single nucleotide polymorphism (SNP, pronounced
  snip), is a DNA sequence variation occurring when
  a single nucleotide - A, T, C, or G - in the
  genome (or other shared sequence) differs between
  members of a species (or between paired
  chromosomes in an individual).

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Synonymous - nonsynonymous substitutions

• Substitutions that result in amino acid
  replacements are said to be nonsynonymous while
  substitutions that do not cause an amino acid
  replacement (such as a GGG to GGC change - both
  codons still encode glycine) are said to be
  synonymous substitutions. Because of the
  difference in their effects on the physiology of
  the organism, synonymous and nonsynonymous
  substitutions can have quite different dynamics.
  For example, synonymous substitutions usually
  occur at a much faster rate than do nonsynonymous
  substitutions. Hence, for coding sequence it is
  often desirable to separate these two.

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Ka/Ks values

• In genetics, the Ka/Ks ratio or dN/dS ratio is
  the ratio of the rate of non-synonymous
  substitutions (Ka) to the rate of synonymous
  substitutions (Ks), which can be used as an
  indication of selection on a protein-coding gene.

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dbSNP

• db (Database) of Single nucleotide polymorphism
• A public-domain archive for a broad collection of
  Single Nucleotide Polymorphisms (SNPs) and is
  hosted at the National Center for Biotechnology
  Information.

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Orthodisease

• OrthoDisease, a comprehensive database of model
  organism genes that are orthologous to human
  disease genes
• Orthodisease is constructed primarily using
  Inparanoid analysis. Inparanoid is a program that
  automatically detects orthologs (or groups of
  orthologs) from 2 species

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Field Biology

• Biology of organisms living in their natural
  environments
• Applications in Ecology and Evolutionary Biology

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Epidemiology

• Epidemiology is the study of how often disease
  occur in different groups of people and why
• Planning and evaluating strategies to prevent
  illness
• Guide to the management of patients in whom
  disease is already developed
• Reference Epidemiology for the uninitiated by
  Coggon, Rose and Barker

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Population at risk

• The population at risk is the group of people,
  healthy or sick, who would be counted as cases if
  they had the disease being studied
• It defines the denominator for the calculation of
  rates of incidences and prevalence
• It is the number of persons potentially capable
  of experiencing the event or outcome of interest

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Floating numerator

• Numerator floating without its denominator
• Common error occurring in field investigations
• The error occurs due to the number of cases not
  relating to the at risk population
• Epidemiological conclusions (on risk) cannot be
  drawn from purely clinical data (on the number of
  sick people seen)

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Target population

• It is the population about which the conclusions
  are to be drawn
• Sometimes measurement can be made on the full
  target population else study samples are used

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Study population and study sample

• The group of individuals in a study
• In a clinical trial, the participants make up the
  study population
• Study sample is chosen from study population

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Aetiology

• The study of the factors that predispose to or
  precipitate the disease
• External agent, a susceptible host, and an
  environment that brings the host and agent
  together is a disease etiology triad

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Surveillance

• Watching over a population and recording data
  likely to have epidemiological significance,
  usually with the aim of early detection of
  disease. Essentially an interventionist exercise
  compared with monitoring, which is passive.

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Case

• Disease in populations exists as a continuum of
  severity rather than as an all or none phenomenon
• The real question in population studies is not
  has the person got the disease? but How much
  of the disease has he or she got?
• Diagnostic continuum is dichotomized into cases
  and non-cases on the basis of statistical,
  clinical, prognostic or operational options
• Hence case definition should be precise and
  unambiguous.
• Epidemiological case definitions are narrower and
  more rigid than clinical ones

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Incidence

• It is the rate at which new cases occur in a
  population during a specified period
• (number of new cases) / (Population at risk)
  (Time during which cases were ascertained)

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Prevalence

• Point prevalence
• The proportion of a population that are cases at
  a point in time
• Period prevalence
• The proportion of a population that are cases at
  any time within a stated period

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Attributable risk and relative risk

• Attributable risk is the disease rate in exposed
  persons to that in people who are unexposed
• Relative risk is the ratio of the disease rate in
  exposed persons to that in people who are
  unexposed
• Attributable risk rate of disease in unexposed
  persons (relative risk 1)

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Confounding

• Causing confusion about causation due to 2 or
  more variables associated with the disease
• Confounding may give rise to spurious
  associations when in fact there is no causal
  relation, or at other extreme, it may obscure the
  effects of a true cause

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Bias

• Bias is the deviation of inferences from the
  truth
• Selection bias is the biased selection of
  individuals into the study
• Information bias is the biased collection or
  biased analysis of the data
• Motto of the epidemiologist could well be dirty
  hands but a clean mind (manus sordidae, mens
  pura)

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Chance

• A measure of how likely it is that some event
  will occur
• Random, unpredictable influences on events
• The association between the exposure and disease
  is considered to be statistically significant
  if the probability that the test statistic lt 0.05

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Sensitivity

• The proportion of persons with the disease who
  are correctly identified by defined criteria
• The proportion of persons with the disease who
  are correctly identified by a screening test
• The ability of a system to detect epidemics and
  other changes in disease occurrence
• A sensitive test detects high proportion of the
  true cases

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Specificity

• The proportion of persons without a disease who
  are correctly identified by a test
• The number of true negative results divided by
  the total number of all those without the disease

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Randomization

• Randomization is used to obtain a similar
  allocation of individuals to each group, the
  groups are followed at the same time
• Purpose of randomization To obtain unbiased
  estimates of differences among treatment
  responses (means or effects) and to obtain an
  unbiased estimate of the random error variation
  in the experiment

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Replication and Local control

• Replication is the repetition of an experiment in
  order to test the validity of its conclusion
• Local control is blocking or grouping to
  eliminate or to control the various sources of
  variation (error)
• Replication and local control are necessary to
  achieve a reduction in the random variation among
  treatment effects in the experiment

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Observational (non-experimental) studies

• Person-level unit of observation
• 1. Longitudinal measurements
• a. Cohort samples
• b. Case control samples
• 2. Cross-sectional measurements
• Aggregate level units of observation (ecological
  studies)
• Reference Epidemiology Kept Simple An
  Introduction to Traditional and Modern
  Epidemiology by B. Burt Gerstman

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Personal-level vs. Aggregate-level

• Personal level study on smoking might collect
  information on each persons smoking habits, age
  and disease status
• Aggregate level of study on smoking might collect
  information on each regions per capita cigarette
  consumption, age distribution and disease rate

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Longitudinal studies

• Longitudinal studies are studies in which the
  sequence of events in individuals can be
  delineated over time
• In cohort studies the incidence of disease in
  exposed and non-exposed groups are compared
• In case-control studies people with disease
  (cases) and people without disease (controls) are
  sampled from the source population and exposure
  histories of cases and controls are compared

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Longitudinal vs. Cross sectional studies

• Longitudinal measurements relates exposures and
  diseases in individuals at various time
  references
• Cross-sectional measurements are not definitively
  time sequenced in individuals
• In cross-sectional studies the analysis of data
  is gathered from samples at one point in time.
  Since both the outcome and the variables are
  measured at the one time these studies are not
  strong at showing cause-effect relationships.

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Experimental studies

• In experimental studies, the investigator
  introduces or removes an exposure in order to
  observe its influence on a health outcome. Such
  allocations may be based on chance mechanism
  (randomized trials) or on other deliberate
  mechanisms built into the studys protocol
  (non-randomized trials)

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Other disease informatics lectures Supercourse
Epidemiology, the Internet and Global
Health Lecture numbers 31981, 30331, 28921,
25381, 25371, and 34011
Thank you