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Study on the Impact of Codon Usage and Ribosomal Binding on Translation Speed in Yeast, Lecture notes of Statistics

South Louisiana Community College (SLCC) - Lafayette Campus Statistics

A study investigating the role of ribosomal binding to transcript sequence in modulating translation speed in yeast, rather than codon usage itself. The researchers used a randomization method to determine codon enrichment in the kozak sequence and compared it to the genome frequencies. They performed various tests, including binomial tests and χ2 tests, to determine the contribution of similarity to the kozak sequence to slowing translation. The study found that similarity to the kozak sequence does not significantly explain slowing in several quantiles or increased slowing.

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Table S3.

!

q1Δr#

(count)#

q2Δr#

q3Δr#

q4Δr#

χ2#test#P#value#

(Bonferroni#

correction)#

A.##

Z#score##

1#

483#

517#

479#

522#

0.39#

!

0#

337#

334#

340#

347#

0.96#

!

A1##

426#

394#

426#

376#

0.21#

!

Binomial#test#

on#+1#and#A1#

charge#score#

counts,#P#

value##

(Bonferroni#

correction)#

0.063#

5.2eA05#

(0.00021)#

0.084#

1.2eA06#

(5.0eA06)#

A#

B.##

Z#score#when#

charge#score#

=#0#

1#

94#

99#

84#

87#

0.68#

!

0#

71#

59#

70#

57#

0.48#

!

A1#

91#

88#

63#

0.084#

!

Binomial#test#

on#+1#and#A1#

tAI#score#

counts,#P#

value##

(Bonferroni#

correction)#

0.88#

0.61#

#

0.82#

0.060#

A#

C.##

Z#score#

adjusted#for#

amino#acid#

usage#

1#

323#

358%

340#

340%

314#

276%

251#

265%

0.0020#(0.0060)#

0.00013%(0.00039)%

%

0#

373#

412%

393#

424%

381#

435%

382#

436%

0.91#

0.83%

!

A1#

330#

256%

285#

254%

283#

267%

267#

199%

0.056#

0.0095%(0.028)%

!

Binomial#test#

on#+1#and#A1#

rare#pair#

score#counts,#

P#value##

(Bonferroni#

correction)#

0.81#

4.4e105%

(0.00018)%

0.031#(0.12)#

0.00048%

(0.0019)%

0.22#

0.73%

0.51#

0.0025%

(0.010)%

A#

Table S3. Sequence similarity to the yeast Kozak sequence cannot explain the

greatest slowing within transcripts. Given that transcript similarity to the Shine-

Dalgarno sequence has been shown to slow ribosomes in bacteria due to interactions

of the sequence with components of the ribosomal RNA [17], we wondered whether

translation speed in yeast might not be modulated by codon usage per se but by the

ability of ribosomes to bind to transcript sequence which mirrors the eukaryotic

Kozak sequence. Specifically, we wanted to determine whether codons which are in

high-ribosomal occupancy windows within a gene might be more likely to correspond

to the Kozak sequence (as compared to codons in low-occupancy windows within the

same genes) and hence bind ribosomes, slowing translation. We first determined

which codons were enriched in the Kozak sequence relative to the codon frequencies

seen throughout the yeast genome at large using a simple randomization. Nucleotide

frequencies at each position of the Kozak sequence in yeast were taken from Cavener

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Table S3.

q1Δ r

(count)

q2Δ r q3Δ r q4Δ r χ^2 test P value

(Bonferroni correction) A. Z score

Binomial test on +1 and -‐ 1 charge score counts, P value (Bonferroni correction) 0.063 5.2e-‐ 05 (0.00021) 0.084 1.2e-‐ 06 (5.0e-‐06)

B.

Z score when charge score = 0

Binomial test on +1 and -‐ 1 tAI score counts, P value (Bonferroni correction)

C.

Z score adjusted for amino acid usage

Binomial test on +1 and -‐ 1 rare pair score counts, P value (Bonferroni correction)

4.4e-‐ 05 (0.00018)

Table S3. Sequence similarity to the yeast Kozak sequence cannot explain the

greatest slowing within transcripts. Given that transcript similarity to the Shine-

Dalgarno sequence has been shown to slow ribosomes in bacteria due to interactions

of the sequence with components of the ribosomal RNA [17], we wondered whether

translation speed in yeast might not be modulated by codon usage per se but by the

ability of ribosomes to bind to transcript sequence which mirrors the eukaryotic

Kozak sequence. Specifically, we wanted to determine whether codons which are in

high-ribosomal occupancy windows within a gene might be more likely to correspond

to the Kozak sequence (as compared to codons in low-occupancy windows within the

same genes) and hence bind ribosomes, slowing translation. We first determined

which codons were enriched in the Kozak sequence relative to the codon frequencies

seen throughout the yeast genome at large using a simple randomization. Nucleotide

frequencies at each position of the Kozak sequence in yeast were taken from Cavener

and Ray 1991 [57]. To determine the frequencies of all the possible ‘codons’ among

the Kozak sequence space, we randomly created 20000 possible Kozak sequences

from the delineated nucleotide frequencies at each site in the consensus sequence. We

then counted all possible triplet ‘codons’ within each sequence, regardless of reading

frame (since we assume that as the ribosome traverses RNA, it may bind the Kozak

sequence regardless of the surrounding reading frame). The counts of all possible

RNA triplets that we observe within our simulated sequences are the observed

‘codons’ within the Kozak sequence. In order to determine whether or not certain

codons are over- or under-used in the Kozak sequence, we compare them to the

counts of codons observed (again in any reading frame) across 20000 randomized

sequences derived from the basal codon frequencies in the S. cerevisiae genome and

of the same length as the Kozak sequence. We calculate Z , a measure of the over- or

under-usage of a particular codon within the Kozak sequence (as compared to the rest

of the genome) as Z codon = [Observed codon count (in Kozak sequence) – Expected

count (from genome frequencies)] / Expected SD of codon. We can then perform a

test similar to the one in Methods V, but where we consider possible slowing codons

to be those with a positive Z (GAT GAC AAC TGC CAA GGC GTA GTC TAT

ACA TGG ATA CAT AAA TGT AAT ATG). A score of 1 indicates there are more

codons with positive Z within the more occluded intra-transcript window; - 1, less

present; 0, present in both windows in equal amounts. A. Similarity to Kozak

sequence cannot explain slowing in several quantiles (binomial tests), nor can it

explain increased slowing (χ

2

Study on the Impact of Codon Usage and Ribosomal Binding on Translation Speed in Yeast, Lecture notes of Statistics

Related documents

Partial preview of the text

Download Study on the Impact of Codon Usage and Ribosomal Binding on Translation Speed in Yeast and more Lecture notes Statistics in PDF only on Docsity!

Table S3.

q1Δ r

(count)

q2Δ r q3Δ r q4Δ r χ^2 test P value

B.

C.

Table S3. Sequence similarity to the yeast Kozak sequence cannot explain the

greatest slowing within transcripts. Given that transcript similarity to the Shine-

Dalgarno sequence has been shown to slow ribosomes in bacteria due to interactions

of the sequence with components of the ribosomal RNA [17], we wondered whether

translation speed in yeast might not be modulated by codon usage per se but by the

ability of ribosomes to bind to transcript sequence which mirrors the eukaryotic

Kozak sequence. Specifically, we wanted to determine whether codons which are in

high-ribosomal occupancy windows within a gene might be more likely to correspond

to the Kozak sequence (as compared to codons in low-occupancy windows within the

same genes) and hence bind ribosomes, slowing translation. We first determined

which codons were enriched in the Kozak sequence relative to the codon frequencies

seen throughout the yeast genome at large using a simple randomization. Nucleotide

frequencies at each position of the Kozak sequence in yeast were taken from Cavener

and Ray 1991 [57]. To determine the frequencies of all the possible ‘codons’ among

the Kozak sequence space, we randomly created 20000 possible Kozak sequences

from the delineated nucleotide frequencies at each site in the consensus sequence. We

then counted all possible triplet ‘codons’ within each sequence, regardless of reading

frame (since we assume that as the ribosome traverses RNA, it may bind the Kozak

sequence regardless of the surrounding reading frame). The counts of all possible

RNA triplets that we observe within our simulated sequences are the observed

‘codons’ within the Kozak sequence. In order to determine whether or not certain

codons are over- or under-used in the Kozak sequence, we compare them to the

counts of codons observed (again in any reading frame) across 20000 randomized

sequences derived from the basal codon frequencies in the S. cerevisiae genome and

of the same length as the Kozak sequence. We calculate Z , a measure of the over- or

under-usage of a particular codon within the Kozak sequence (as compared to the rest

of the genome) as Z codon = [Observed codon count (in Kozak sequence) – Expected

count (from genome frequencies)] / Expected SD of codon. We can then perform a

test similar to the one in Methods V, but where we consider possible slowing codons

to be those with a positive Z (GAT GAC AAC TGC CAA GGC GTA GTC TAT

ACA TGG ATA CAT AAA TGT AAT ATG). A score of 1 indicates there are more

codons with positive Z within the more occluded intra-transcript window; - 1, less

present; 0, present in both windows in equal amounts. A. Similarity to Kozak

sequence cannot explain slowing in several quantiles (binomial tests), nor can it

explain increased slowing (χ

tests). B. Even when the number of positive charges is

the same between the two windows, we do not detect a significant contribution of

similarity to Kozak sequence to slowing. C. Controlling for amino acid usage in two

different ways, we detect no contribution of similarity to Kozak sequence to slowing;

in fact, as the degree of slowing increases, the ability of Kozak similarity to explain

slowing decreases (χ^2 tests). Method one (in bold): a gene is scored ‘1’ if the slow

window contains more codons with positive Z , ‘-1’ if it contains fewer. Method two

(in italics): the magnitude of all the positive Z values is averaged in each window, and

the gene is scored ‘1’ if the slower window has a higher average Z , ‘-1’ if its average

Z is lower.