STRUCTURAL GENOMICS, FUNCTIONAL GENOMICS, COMPARATIVE GENOMICS

 Initial phase of genome analysis
 Includes construction of genetic & physical maps of a genome, identification
of genes, annotation of gene features & comparison of genome structures
 It characterizes the physical nature of whole genome
 It describes the 3D structure of every protein encoded by a given genome
 The main difference between structural genomics and traditional structure
prediction is :- the former attempts to determine the structure of every
protein encoded by the genome & the later focus on a particular protein
 Describe the 3-dimensional structure of every protein encoded by a given
genome
 Has the potential to inform knowledge of protein function
 Can be used to identify novel protein folds and potential targets for drug
discovery
 While most structural biologists pursue structures of individual proteins or
protein groups, specialists in structural genomics pursue structures of
proteins on a genome wide scale. This implies large-scale cloning,
expression and purification

ADVANTAGES
 Economy of scale
 Scientific community gets immediate
access to new structure as well as to
reagents such as clones & proteins
DISADVANTAGES
 Many of the structure of protein are
of unknown function & don’t have
corresponding publication
 It requires new ways of
communicating the structural
information to the broader research
community

 Identify novel protein folds done via ab initio modelling
 Understanding of protein functions
 Has potential implications for drug discovery and protein engineering

 Takes advantage of completed genome sequences in several ways in order to
determine protein structures
 The gene sequence of the target protein can also be compared to a known sequence
and structural information can then be inferred from the known protein’s
structure
 Used to predict novel protein folds based on other structural data
 Structural genomics can also take modeling-based approach that relies on
homology between the unknown protein and a solved protein structure

 Completed genome sequences allow every open reading frame (ORF), to
be cloned and expressed as protein
 These proteins are then purified and crystallized, and then subjected to
one of two types of structure determination: X-ray crystallography and
nuclear magnetic resonance (NMR)
 The whole genome sequence allows for the design of every primer
required in order to amplify all of the ORFs, clone them into bacteria,
and then express them
 By using a whole-genome approach to this traditional method of protein
structure determination, all of the proteins encoded by the genome can
be expressed at once
 This approach allows for the structural determination of every protein
that is encoded by the genome.

 This approach uses protein
sequence data and the chemical and
physical interactions of the encoded
amino acids to predict the 3-D
structures of proteins with no
homology to solved protein
structures
 One highly successful method for ab
initio modeling is the Rosetta
program, which divides the protein
into short segments and arranges
short polypeptide chain into a low-
energy local conformation
 Rosetta is available for commercial
use and for non-commercial use
through its public program, Robetta
 ab initio modelling

 Compares the gene sequence of an unknown protein with sequences of
proteins with known structures
 Depending on the degree of similarity between the sequences, the structure
of the known protein can be used as a model for solving the structure of the
unknown protein
 Highly accurate modeling is considered to require at least 50% amino acid
sequence identity between the unknown protein and the solved structure
 30-50% sequence identity gives a model of intermediate-accuracy, and
sequence identity below 30% gives low-accuracy models
 It has been predicted that at least 16,000 protein structures will need to be
determined in order for all structural motifs to be represented at least once
and thus allowing the structure of any unknown protein to be solved
accurately through modeling
 One disadvantage of this method, however, is that structure is more
conserved than sequence and thus sequence-based modeling may not be the
most accurate way to predict protein structures.
Sequence based modelling

 Threading bases structural modeling on fold similarities rather than sequence
identity
 This method may help identify distantly related proteins and can be used to infer
molecular functions.
Threading

 Mycobacterium tuberculosis
proteome
 The goal of the TB Structural
Genomics Consortium is to determine
the structures of potential drug targets
in Mycobacterium tuberculosis, the
bacterium that causes tuberculosis.
The development of novel drug
therapies against tuberculosis are
particularly important given the
growing problem of multi-drug-
resistant tuberculosis
 The fully sequenced genome of M.
tuberculosis has allowed scientists to
clone many of these protein targets
into expression vectors for purification
and structure determination by X-ray
crystallography
 Studies have identified a number of
target proteins for structure
determination, including extracellular
proteins that may be involved in
pathogenesis, iron-regulatory proteins,
current drug targets, and proteins
predicted to have novel folds. So far,
structures have been determined for
708 of the proteins encoded by M.
tuberculosis.
 Thermotogo maritima proteome
 One current goal of the Joint Center
for Structural Genomics (JCSG), a part
of the Protein Structure Initiative
(PSI) is to solve the structures for all
the proteins in Thermotogo maritima,
a thermophillic bacterium
 T. maritima was selected as a
structural genomics target based on its
relatively small genome consisting of
1,877 genes and the hypothesis that
the proteins expressed by a
thermophilic bacterium would be
easier to crystallize.
 Lesley et al used Escherichia coli to
express all the open-reading frames
(ORFs) of T. martima. These proteins
were then crystallized and structures
were determined for successfully
crystallized proteins using X-ray
crystallography. Among other
structures, this structural genomics
approach allowed for the
determination of the structure of the
TM0449 protein, which was found to
exhibit a novel fold as it did not share
structural homology with any known
proteins

 Field of molecular biology
 Attempts to make use of the vast wealth of data given by genomic and
transcriptomic projects (such as genome sequencing projects and RNA
sequencing) to describe gene (and protein) functions and interactions
 Focuses on the dynamic aspects such as gene transcription, translation,
regulation of gene expression and protein–protein interactions, as
opposed to the static aspects of the genomic information such as DNA
sequence or structures.
 Attempts to answer questions about the function of DNA at the levels of
genes, RNA transcripts, and protein products
 Characteristic of functional genomics studies is their genome-wide
approach to these questions, generally involving high-throughput
methods rather than a more traditional “gene-by-gene” approach

 To understand the function of larger numbers of genes or proteins,
eventually all components of a genome
 Long-term goal is to understand the relationship between an organism's
genome and its phenotype
 The term functional genomics is often used broadly to refer to the many
technical approaches to study an organism's genes and proteins, including
the "biochemical, cellular, and/or physiological properties of each and every
gene product“
 while some authors include the study of nongenic elements in his definition
 May also include studies of natural genetic variation over time (such as an
organism's development) or space (such as its body regions), as well as
functional disruptions such as mutations
 The promise of functional genomics is to generate and synthesize genomic
and proteomic knowledge into an understanding of the dynamic properties
of an organism
 This would provide a more complete picture than studies of single genes
 Integration of functional genomics data is also the goal of systems biology.

 Includes function-related aspects of the genome itself such as mutation
and polymorphism (such as single nucleotide polymorphism (SNP)
analysis), as well as measurement of molecular activities
 The latter comprise a number of "-omics" such as transcriptomics (gene
expression), proteomics (protein production), and metabolomics
 Uses mostly multiplex techniques to measure the abundance of many or
all gene products such as mRNAs or proteins within a biological sample
 Together these measurement modalities endeavor to quantitate the
various biological processes and improve our understanding of gene and
protein functions and interactions

GENETIC INTERACTION MAPPING
 Systematic pairwise deletion of
genes or inhibition of gene
expression can be used to identify
genes with related function, even
if they do not interact physically
 Epistasis refers to the fact that
effects for two different gene
knockouts may not be additive;
that is, the phenotype that
results when two genes are
inhibited may be different from
the sum of the effects of single
knockouts
THE ENCODE PROJECT
 The ENCODE (Encyclopedia of DNA
elements) project is an in-depth
analysis of the human genome whose
goal is to identify all the functional
elements of genomic DNA, in both
coding and noncoding regions
 Only the pilot phase of the study has
been completed, involving hundreds of
assays performed on 44 regions of
known or unknown function comprising
1% of the human genome
 Important results include evidence
from genomic tiling arrays that most
nucleotides are transcribed as coding
transcripts, noncoding RNAs, or
random transcripts, the discovery of
additional transcriptional regulatory
sites, further elucidation of chromatin-
modifying mechanisms.

MICROARRAYS
 Measure the amount of mRNA in a
sample that corresponds to a given
gene or probe DNA sequence
 Probe sequences are immobilized on a
solid surface and allowed to hybridize
with fluorescently labeled “target”
Mrna
 Intensity of fluorescence of a spot is
proportional to the amount of target
sequence that has hybridized to that
spot, and therefore to the abundance of
that mRNA sequence in the sample
 Microarrays allow for identification of
candidate genes involved in a given
process based on variation between
transcript levels for different conditions
and shared expression patterns with
genes of known function
SAGE
 Serial analysis of gene expression
 Alternate method of analysis based
on RNA sequencing rather than
hybridization
 Relies on the sequencing of 10–17
base pair tags which are unique to
each gene
 These tags are produced from poly-
A mRNA and ligated end-to-end
before sequencing
 SAGE gives an unbiased
measurement of the number of
transcripts per cell, since it does
not depend on prior knowledge of
what transcripts to study

RNA SEQUENCING
 The most efficient way to study transcription and gene expression
 Typically done by next-generation sequencing
 A subset of sequenced RNAs are small RNAs, a class of non-coding RNA molecules
that are key regulators of transcriptional and post-transcriptional gene silencing,
or RNA silencing
 Next generation sequencing is the gold standard tool for non-coding RNA
discovery, profiling and expression analysis

 Field of biological research in which the genomic features of different
organisms are compared
 The genomic features may include the DNA sequence, genes, gene order,
regulatory sequences, and other genomic structural landmarks
 In this branch of genomics, whole or large parts of genomes resulting
from genome projects are compared to study basic biological similarities
and differences as well as evolutionary relationships between organisms
 The major principle of comparative genomics is that common features of
two organisms will often be encoded within the DNA that is
evolutionarily conserved between them
 Therefore, comparative genomic approaches start with making some
form of alignment of genome sequences and looking for orthologous
sequences (sequences that share a common ancestry) in the aligned
genomes and checking to what extent those sequences are conserve
 Based on these, genome and molecular evolution are inferred and this
may in turn be put in the context of, for example, phenotypic evolution
or population genetics

 Virtually started as soon as the whole genomes of two organisms became
available (that is, the genomes of the bacteria Haemophilus influenzae
and Mycoplasma genitalium) in 1995
 Comparative genomics is now a standard component of the analysis of
every new genome sequence
 With the explosion in the number of genome projects due to the
advancements in DNA sequencing technologies, particularly the next-
generation sequencing methods in late 2000s, this field has become more
sophisticated, making it possible to deal with many genomes in a single
study
 Comparative genomics has revealed high levels of similarity between
closely related organisms, such as humans and chimpanzees, and, more
surprisingly, similarity between seemingly distantly related organisms,
such as humans and the yeast Saccharomyces cerevisiae
 It has also showed the extreme diversity of the gene composition in
different evolutionary lineages

 Computational approaches to genome comparison have recently become a common
research topic in computer science
 A public collection of case studies and demonstrations is growing, ranging from
whole genome comparisons to gene expression analysis
 This has increased the introduction of different ideas, including concepts from
systems and control, information theory, strings analysis and data mining
 It is anticipated that computational approaches will become and remain a
standard topic for research and teaching, while multiple courses will begin
training students to be fluent in both topics

 Computational tools for analyzing sequences and complete genomes are
developing quickly due to the availability of large amount of genomic data
 At the same time, comparative analysis tools are progressed and improved
 In the challenges about these analyses, it is very important to visualize the
comparative results
 Visualization of sequence conservation is a tough task of comparative
sequence analysis
 It is highly inefficient to examine the alignment of long genomic regions
manually
 Internet-based genome browsers provide many useful tools for investigating
genomic sequences due to integrating all sequence-based biological
information on genomic regions
 When we extract large amount of relevant biological data, they can be very
easy to use and less time-consuming

 UCSC Browser: This site contains the reference sequence and working
draft assemblies for a large collection of genomes.
 Ensembl: The Ensembl project produces genome databases for
vertebrates and other eukaryotic species, and makes this information
freely available online.
 MapView: The Map Viewer provides a wide variety of genome mapping
and sequencing data.
 VISTA is a comprehensive suite of programs and databases for
comparative analysis of genomic sequences. It was built to visualize the
results of comparative analysis based on DNA alignments. The
presentation of comparative data generated by VISTA can easily suit
both small and large scale of data.
 BlueJay Genome Browser: a stand-alone visualization tool for the multi-
scale viewing of annotated genomes and other genomic elements.
 An advantage of using online tools is that these websites are being
developed and updated constantly. There are many new settings and
content can be used online to improve efficiency

AGRICULTURE
 Agriculture is a field that reaps
the benefits of comparative
genomics
 Identifying the loci of
advantageous genes is a key step
in breeding crops that are
optimized for greater yield, cost-
efficiency, quality, and disease
resistance
 Not only is this methodology
powerful, it is also quick
 Previous methods of identifying
loci associated with agronomic
performance required several
generations of carefully
monitored breeding of parent
strains, a time consuming effort
that is unnecessary for
comparative genomic studies
MEDICINE
 Vaccinology in particular has
experienced useful advances in
technology due to genomic
approaches to problems
 In an approach known as reverse
vaccinology, researchers can
discover candidate antigens for
vaccine development by analyzing
the genome of a pathogen or a
family of pathogens
 Applying a comparative genomics
approach by analyzing the
genomes of several related
pathogens can lead to the
development of vaccines that are
multiprotective
 Comparative genomics can also be
used to generate specificity for
vaccines against pathogens that
are closely related to commensal
microorganisms

 As DNA sequencing technology has become more accessible, the number
of sequenced genomes has grown
 With the increasing reservoir of available genomic data, the potency of
comparative genomic inference has grown as well
 A notable case of this increased potency is found in recent primate
research
 Comparative genomic methods have allowed researchers to gather
information about genetic variation, differential gene expression, and
evolutionary dynamics in primates that were indiscernible using
previous data and methods
 The Great Ape Genome Project used comparative genomic methods to
investigate genetic variation with reference to the six great ape species,
finding healthy levels of variation in their gene pool despite shrinking
population size

STRUCTURAL GENOMICS, FUNCTIONAL GENOMICS, COMPARATIVE GENOMICS

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STRUCTURAL GENOMICS, FUNCTIONAL GENOMICS, COMPARATIVE GENOMICS