Sequence and structure databanks

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Sequence and structure databanks e divided into many different categories. f the most important is Supervised databanks with gatekeeper. Examples: Swissprot Refseq (at NCBI) Entries are checked for accuracy. + more reliable annotations Repositories without gatekeeper. Examples: GenBank EMBL TrEMBL Everything is accepted + everything is available

description

Sequence and structure databanks. can be divided into many different categories. One of the most important is. Supervised databanks with gatekeeper. Examples: Swissprot Refseq (at NCBI) Entries are checked for accuracy. + more reliable annotations -- frequently out of date. - PowerPoint PPT Presentation

Transcript of Sequence and structure databanks

Sequence and structure databanks can be divided into many different categories. One of the most important is

Supervised databanks with gatekeeper. Examples:

SwissprotRefseq (at NCBI)

Entries are checked for accuracy.+ more reliable annotations-- frequently out of date

Repositories without gatekeeper. Examples:

GenBankEMBLTrEMBL

Everything is accepted + everything is available-- many duplicates-- poor reliability of annotations

Description of Group B Streptococcus Pan-genome

Genome comparisons of 8 closely related GBS strains

Tettelin, Fraser et al., PNAS 2005 Sep 27;102(39)

Method

Bacterial CoreGenes that are shared among all

Bacteria

Bit score cutoff 50.0 (~10E-4)

f(x) = A1*exp(-K1*x) + A2*exp(-K2*x) + A3*exp(-K3*x) + Plateau

Genes without homologs

f(x) = A1*exp(-K1*x) + A2*exp(-K2*x) + A3*exp(-K3*x) + A4*exp(-K4*x)

+ A5*exp(-K5*x) + Plateau

Decomposed function

Core

Essential genes(Replication, energy,

homeostasis)

~ 116 genefamilies

Extended Core

Set of genes that define groups or species

(Symbiosis,photosynthesis)

~ 17,060 genefamilies

Accessory PoolGenes that can be used to distinguish strains or

serotypes(Mostly genes of unknown functions)

~ 114,800 geneFamilies uncovered so far

76.6%

3.8%

19.6%

Gene frequency in individual genomes

Core

Extended Core

Accessory Pool

f(x) = A1*exp(-k1*x) + A2*exp(-k2*x) + A3*exp(-k3*x) + A4*exp(-k4*x) + A5*exp(-k5*x) + Plateau

1/k1 = 0.48 1/k2 = 2.31/k3 = 10.161/k4 = 31.401/k5 = 162.6

A1 = 939.4A2 = 731.1A3 = 455.2A4 = 328.6A5 = 385.5

Number of genomes added

Kézdy-Swinbourne Plot

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Novel genes after looking in x genomes

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A Kézdy-Swinbourne Plot plot can be used to estimate the value that a decay function approaches as time goes to infinity.

Assume the simple decay function f(x) = K + A e-kx , then f(x + ∆x) = K + A e-k(x+∆x).Through elimination of A: f(x+∆x)=e-k ∆x f(x) + K’

For the plot of f(x+∆x) against f(x) the slope is e-k ∆x. For x both f(x) and f(x+∆x) approach the same constant : f(x)K, f(x+∆x)K. (see the def. for the decay function)The Kézdy-Swinbourne Plot is rather insensitive to deviations from a simple single component decay function.

More at Hiromi K: Kinetics of Fast Enzyme Reactions. New York: Halsted Press (Wiley); 1979

Kézdy-Swinbourne Plot

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50

100

150

200

250

300

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400

450

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-100 0 100 200 300 400 500 600

delta x = 10

2030405060708090100110120130

Novel genes after looking in x genomes

Nov

el g

enes

aft

er lo

oki

ng

in x

+ ∆

x ge

nom

es

~230 novel genes per genome