Genomics: 1 Genomics-sequencing of microbial genomes This lecture illustrates the strategies used in microbial genome sequencing projects, compares genome content and organisation amongst microbes, and shows how to derive information on gene function across genome. Objectives for students: • Expected to describe strategies involved in microbial genome sequencing and functional genomics • Provide examples of information that can be derived from genomics
76
Embed
Genomics: 1 Genomics-sequencing of microbial genomes This lecture illustrates the strategies used in microbial genome sequencing projects, compares genome.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Genomics: 1
Genomics-sequencing of microbial genomes
This lecture illustrates the strategies used in microbial genome sequencing projects, compares genome content and organisation amongst microbes, and shows how to derive information on gene function across genome.
Objectives for students:• Expected to describe strategies involved in microbial genome
sequencing and functional genomics• Provide examples of information that can be derived from
• problems/issues– ownership– strain choice– cost– approach– data release– some now less relevant
• Post genomic era– Comparative genomics– Functional genomics
Genomics: 7
Genome sequencing strategy• Strategy choice• large collaborative cosmid/BAC-based projects
– now better suited for larger genomes– slow
• small insert shotgun approach– centralised– rapid and efficient– choice for bacteria
• Strain choice– fresh isolate vs lab strain– clinical vs environmental– subsequent genetic analysis
Genomics: 8
Yeast genome sequence strategy• Yeast chromosomes (16) individually sequenced• several approaches used• Make genome library in cosmids• order cosmid library
– which cosmid overlaps with which– link cosmid to genome map– produced tiled set of cosmids– only sequence minimum number
• Use chromosome specific probe to identify chr-specific cosmids• sequence cosmid inserts by subcloning• Solve problems by direct PCR sequencing, walking and other libraries
(lambda)• Telomeres
Genomics: 9
Tiled set
Genomics: 10
c1A B
c2C D
c3E F
c4G H
c5I J
c1 c2 c3 c4 c5
A
B
C
D
E
F
G
H
I
J
OrderingClones
Genomics: 11
PH011
200100
80 100 120 140 160 180
70512
70449
70893
70515
70124
70266 7202
70265
70871
70463
Genomics: 12
Whole genome/chromosome shot-gun strategy (WGS)
• Rapid• Generation of small insert genomic library• Library is not initially ordered• DNA sequence ends of inserts• Depends on powerful computing to
assemble sequence reads
Genomics: 13
Main steps in generating a complete genome sequence
Isolation
Construction
Shotgun sequencing
Finishing
Annotation
Minimum time period (weeks)
2
4-6
2-4
12
12
Genomics: 14
bacterial chromosome
vectorplasmid
random shearing
size selection
libraryof
clones
sequenceend of
each clone
individual clones
Genomics: 15
Assembly
Sequencing individual clones
genome sequence with gaps
Genomics: 16
Automated sequencers: ABI 3700
• Made by Applied Biosystems
• Most widely used automated sequencers:– 96 capillaries– robot loading from
384-well plates• Two to three hours per
run• 600–700 bases per run
96–well plate
robotic arm and syringe
96 glass capillaries
load bar
Genomics: 17
Automated sequencers: MegaBACE• Made by Amersham• 96 capillaries• Robotic loading from
384–well plate• Two to four hours per
run• Can read up to 800
basesSource : GE Healthcare Life Science, Uppsala, Sweden
Genomics: 18
Automatic gel reading• Top image: confocal
detection by the MegaBACE sequencer of fluorescently labeled DNA
• Bottom image: computer image of sequence read by automated sequencer
Genomics: 19
Industrialization of sequencing• Most genome
sequencing projects divide tasks among different teams– Genome libraries– Production sequencing– Finishing
• Sequencing machines run 24/7
• Many tasks performed by robots
The Broad Institute of MIT and Harvard, www.genome.gov
Genomics: 20
The future is here?..454 sequencing
Reprinted by permission from Macmillan Publishers Ltd: [NATURE] (Margulies et al., 437: 376 copyright (2005)
• rise in contig number as amount of reads increases• steady fall as accumulating sequence bridges gaps between contigs• levels off as new reads more likely in known contig than gap• start finishing
Number of reads
Num
ber
of c
onti
gs
1
rapid gap bridging
difficult gap bridging
Finishing
Genomics: 26
Finishing• Why are gaps present?• Gap bridging
– sequence gaps• sequence gaps –choose appropriate clone and walk
– physical gaps• alternative libraries (which?)• PCR across gap
• Mistakes/poor sequence– areas where sequence reads are less than 8-10– repeated sequences -rRNA
Artemis: sequence viewer and annotation tool from the Sanger Centre (http://www.sanger.ac.uk/Software/Artemis/)
Genomics: 33
Genomics: 34
Genomics: 35
http://xbase.bham.ac.uk/
xBASE is a database for comparative genome analysis of all bacterial genome sequences
Chaudhuri RR, Pallen MJ. xBASE, a collection of online databases for bacterial comparative genomics. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D335-7.
• Functional genomics –ascribing gene function across a genome– gene function –knowns– phenotype prediction– gene function –unknowns– investigating function
• Bacteria-Yeast
Genomics: 38
Bacteria: Does size matter?• Link genome size to adaptive capability
– Regulation –sensing signals and transcriptional responses• detect change or requirement and respond
appropriately• transcriptional regulation
Genomics: 39
Not just Size but how you use it….. • Small genomes
– Mycoplasma genitalium• 580,070 bp• smallest genome for self-replicating organism• free living but only just..infects host cells (guess which!)• few biosynthesis and regulatory systems• has replication & transcription & translation, metabolism etc
functions– Borrelia burgdorferi
• 910,725 bp• Lyme disease• few cellular biosynthetic systems
Proteome• Genome-wide determination of protein expression• Gives information stimulons• protein expression linked to function• assess mutants (regulatory mutants affect several
proteins)
• Grow bacteria under defined conditions• Extract proteins• 2D-gel electrophoresis• Protein spot identification • Mass Spectrometry• peptide size predictions from Genome data
Genomics: 63
Defining the Campylobacter proteome –chasing spots
Which protein? Which conditions?
Which other proteins are co-expressed?
Genomics: 64
C. jejuni iron example
Genomics: 65
digest with
protease
pIM
ol m
ass
Mass Spec
* * ***
http://depts.washington.edu/yeastrc/pages/ms.html
Genomics: 66
Mass Mutagenesis: mutantome• Mutate every ORF in genome
– organism specific technology
• High throughput analysis of phenotype– need to analyse many 1000s of mutants under many
conditions
• Signature-tagged technology– enables analysis of mutant pools– requires array technology for genome-wide projects
• Association on ORF with mutant phenotypes• Regulators might be pleiotropic
Genomics: 67
Arrays: micro and chip• Microarrays
– Glass slides with <10000 individual samples applied in known position
– Use of robotics– Samples can be PCR products or oligos– example: oligos complementary to each unique Tag– example: oligo/PCR product complementary to each