Abstract

A large set of yeast mRNA 3'-processing regulatory sequences was analyzed statistically, revealing a systematic variation that correlates with measured mRNA stability. Transcripts with relatively short half-lives have a higher frequency of inclusion of 3'-processing elements that include the core sequence of binding sites for the PUF proteins, which enhance mRNA turnover. These results suggest that regulatory sequence variation, typically modeled as random, may instead arise from the necessity or advantage of specifying multiple functions in a common sequence element.

Supplemental Information and Data

Sequences

The sequences for analysis were obtained by combining the transcript half-life measurements made available by Pat Brown's group at Stanford, from the associated manuscript, Wang Y et al, "Precision and functional specificity in mRNA decay" (2002) PNAS 99:5860-5865, with the Hidden Markov Model (HMM-)based 3'-processing site prediction tool from Graber JH, McAllister GD, and Smith TF, "Probabilistic prediction of Saccharomyces cerevisiae mRNA 3'-processing sites." (2002) Nucleic Acids Res 30:1851-8. The sequence files contain the final 100 nt of the predicted transcripts, based on the most probable 3'-processing site:

Supplemental Table 1: Tests on equality of the proportions displayed in Figure 2 of the manuscript

1. In the first version, words are measured on a "per sequence basis", which is to say that the measured counts are the number of sequences that contain at least one copy of that word. Z and p are the same as in Table 1. The sequences are then sorted based on Z-scores. The top and bottom five rows are shown below, and the complete table is available in either html or tab-delimited text formats. The probablility values are, of course, computed under the assumption of a single test. At the very least a Bonferroni correction, or some similar compensation for multiple testing should be performed.

   	SLOW count	SLOW fraction	FAST count	FAST fraction	Z	p
   UGUA	195	0.514512	270	0.687023	-4.896047895	9.79227E-07
   UGUAU	108	0.28496	163	0.414758	-3.777365604	0.000158547
   UAUGUA	65	0.171504	108	0.274809	-3.44119599	0.000579246
   AUGUA	102	0.269129	151	0.384224	-3.405940422	0.000659468
   UGUAUA	65	0.171504	104	0.264631	-3.128226086	0.001758783
   |	|	|	|	|	|	|
   GGUUUU	22	0.0580475	7	0.0178117	2.939298967	0.003289692
   GCC	166	0.437995	131	0.333333	2.988054601	0.002807732
   GUUU	197	0.519789	161	0.409669	3.067249102	0.002160525
   AACUUC	15	0.0395778	2	0.00508906	3.264404125	0.001097067
   UUCAA	96	0.253298	61	0.155216	3.384748915	0.000712539

2. In the second version, words are measured as their count relative to the total number of words counted (of the same size), which is to say that of all tetramers counted, 290, or 0.788% were UGUA. As above, the top and bottom five rows are shown below, and the complete table is available in either html or tab-delimited text formats. The probablility values are, of course, computed under the assumption of a single test. At the very least a Bonferroni correction, or some similar compensation for multiple testing should be performed.

   	SLOW count	SLOW fraction	FAST count	FAST fraction	Z	p
   UGUA	290	0.00788837	439	0.0115359	-5.077765312	3.82551E-07
   AUAU	900	0.0244811	1093	0.0287216	-3.601119673	0.000316924
   AU	4534	0.120839	5023	0.129322	-3.541646742	0.00039772
   UGUAU	141	0.00387533	213	0.00565557	-3.51137193	0.000445887
   |	|	|	|	|	|	|
   AAAAA	541	0.0148692	460	0.0122139	3.12831989	0.001758222
   AACUUC	15	0.000416609	2	5.36639E-05	3.261813759	0.00110714
   GUU	641	0.0172581	548	0.014253	3.319558351	0.000901714
   UUCAA	108	0.00296834	63	0.00167277	3.672182253	0.000240553
   AAAAAA	340	0.00944313	248	0.00665432	4.278118528	1.88611E-05

Supplemental Figure 1: Variation in the distribution of t_1/2 with number of copies of UGUA

Distribution of half-life measurements, with transcripts grouped based on the number of copies of UGUA in the final 100 nucleotides of the projected 3'-UTR. All distributions fit a log-normal curve well. Note that the distributions of the UGUA-containing transcripts (red and blue plots) show a small but consistent shift throughout the entire distribution when compared to the UGUA-absent trnascripts. This shift is consistent with the introduction of a small population of fast-turnover transcripts mediated by a UGUA-containing motif.

Supplemental Figure 2 (not referenced in the paper): Sequence Logo Representation of Gibbs Sampler Analysis of the SLOW and FAST sets

SLOW

FAST