in English - Bioinformatics

fleagoldfishBiotechnology

Oct 2, 2013 (3 years and 8 months ago)

73 views

Virtual
Bioinformat
ics

Bioinformatics in Functional genomics




Martti Tolvane
n


IMT Bioinformatics


University of
Tamper
e

Course basics


extent: 4 cp, or 80 hours of work


duration: 12 weeks max.


starting: whenever it fits your personal
schedule, excepting holiday periods
when the application system is closed

Genome
-
wide bioinformatics (1)


comparative genom
i
cs


whole
-
genome analyses


evolution studies


analyses of components in a ”complete” system


functional genom
i
cs = inferring functions from data


expression patterns, gene regulation


sequence comparisons, homologue relationships


studies of gene variation, altered phenotypes


Genome
-
wide bioinformatics (2)


proteomics


expression proteomics = differential proteomics =
2D
-
PE + MS


interaction proteomics


functional proteomics

= systematic perturbation or
functional inactivation of proteins in a given
environment


structural proteomics (with a frequently used
misnomer: structural genome cs)

Topics of our FG course


Genomes


Gene variations


DNA microarrays


Proteomics

Course topics
-

genomes


genome projects


genome annotation


analysis and predicition of functions and
orthologous genes


gene identification and prediction

Course topics (3)


gene variations


mutation data banks


DNA microarrays


data manipulation


clustering


data mining

Course topics


proteomics


expression proteomics


2D electrophoresis


mass spectrometry


functional
p
roteomics


removed/inactivated genes (”knock
-
outs”)


RNA Interference (RNAi)



strongly emerging


structural proteomics


interaction proteomics


metabolic networks

How the course works


you need to choose your own focus
areas and plan your schedule at the
start of the course


main chapters (2.
-
5.) can be taken in
any order


a quick look at all material may help
you to decide how to proceed

How the course works (2)


all chapters provide learning goals and
exercises, but none of them are
obligatory


it is up to each student to find
appropriate tasks to help them achieve
the goals they set for themselves


you need to document your course
work in the Learning diary

Learning diary


material you can enter in your Learning
diary:


summaries of new things you have learned
and/or feel to be important


solutions to exercises, descriptions of the
processes how you found the solutions


article and Internet references which you have
studied carefully (especially ones you have
found outside the course material)


what you
must
write down: time you
spent each day

Goals of your Learning diary


documents your presence and the time you
spent in the course


you know when it is
time to finish


deeper learning when you produce texts of
your own from what you have read and done




you need to reserve a lot of time for diary
work; perhaps even half of your course time
should be spent in tasks aiming at specific
diary entries

Genomes and their annotation
(1)


complete genomes of many organisms
are available


goal: ”system
-
wide” understanding of
the biology of a given organism


= seeing ”parts lists” of everything an
organism needs, and figuring out how
they work together

Genomes and their annotation
(2)


gene finding is not always straightforward


problem: rare gene products, for which
you cannot find corresponding mRNA or
protein sequences in databanks


additional complication: alternative
splicing, many transcripts per gene

Genomes and their annotation
(3)


if you intend to analyze or just use data
from a databank, it is useful to know both
the goals and the reality of their
annotation level


inconsistencies, missing data


even well
-
annotated databanks provide
only a fraction of all biologically relevant
information relevant to a gene or a
molecule (compared to literature)

Annotation: a vision


databank content: all knowlegde on functions
of a gene product


add structural information




insights in structure
-
function relationships


add data on expression patterns and regulation




understanding cell differentiation and other
big questions in biology on molecular level

Introduction to DNA microarrays


massive data sets from simultaneous
expression levels of thousands of genes


impossible to grasp directly by the
human mind


methods are needed for finding
meaningful results and patterns from
the bulk of data

DNA microarray bioinformatics


data manipulation: normalization etc.


data clustering


genes which behave in a similar fashion


sample classification by profiles of predictive
genes (e.g. cancer typing)


data mining:


finding interpretation to clustering results


example: recognition of regulatory factor binding
sites in coexpressed genes

Clustered
data from a
microarray
experiment

Introduction to Proteomics


as in the transcriptome, composition of the
proteome depends on cell type,
developmental phase and conditions


proteome analyses are still struggling to solve
the ”basic proteome” of different cells and
tissues or limited changes under changing
conditions or during processes


current methods can only ”see” the most
abundant proteins

Proteomics experiments


typically a combination of 2D protein
electrophoresis and mass spectrometry


labour
-
intensive, not really ”high
-
throughput” methods


more efficient ”protein array” methods
are emerging

2
-
dimensional electrophoresis

Bioinformatics in proteomics