Automatic extraction of reliable regions from multiple sequence alignments-1

<p><b>Copyright information:</b></p><p>Taken from "Automatic extraction of reliable regions from multiple sequence alignments"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S9</p><p>BMC Bioinformatics 2007;8(Suppl 5):S9-S9.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892097.</p><p></p>n to the cumulative running time of the alignment programs used to generate the input alignments. The running times of Mumsa were multiplied by 100 to be visible in the plot. The sequence files were generated by ROSE [16] using an average sequence length of 500 residues and and average evolutionary distance of 250. It is clear that the running time of Mumsa is at least two orders of magnitude lower than that required by the alignment programs

Automatic extraction of reliable regions from multiple sequence alignments-0

<p><b>Copyright information:</b></p><p>Taken from "Automatic extraction of reliable regions from multiple sequence alignments"</p><p>http://www.biomedcentral.com/1471-2105/8/S5/S9</p><p>BMC Bioinformatics 2007;8(Suppl 5):S9-S9.</p><p>Published online 24 May 2007</p><p>PMCID:PMC1892097.</p><p></p>d Dialign alignment of the Balibase 3.0 test case BB20007. The parameter was chosen to be two, requiring that residues in the output alignment appear in at least two input alignments. Each residue is colored according to the average occurrence of the POARs it is involved in. Regions that appear in red are identically aligned in all 5 input alignments while green and blue regions are only aligned identically in fewer and fewer cases. It is clear that all alignment programs find conserved motifs in the sequences but disagree on how the residues in between should be aligned

View of the ceruloplasmin (CP) locus in the human genome.

<p>A. CP is located on on the negative (purple) strand of chromosome 3 at position 3q23-q25, flanked by HPS3 on the positive (green) strand and CPHL1 on the negative strand. B. RefSeq mRNA models of the locus. C. Promoter activity signal distribution as measured by CAGE in the locus. The majority of expression comes from the 5′ end of CP. D. Expression (tags per million, TPM) across the locus for the cell lines present in FANTOM5. Cell lines with expression above 10 TPM are shown here; in total 269 cell lines were profiled. Obesity associated cell lines are indicated by black (O.R. > = 1.30) and gray (O.R. > = 1.20) arrows.</p

Figure S1. Methodology from Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

For each time series, the Bayesian evidence (log Z) is calculated for each of four predefined models. The time series is classified to the best fitting model. In this example, a time course for the immediate early gene FOS is assigned to the peak model. The prediction of the peak model for the inferred parameters is shown by the blue line in the bottom plot

Outline of the trajectory models and experimental design of the data.

<p><b>A.</b> Schematic view of the gene expression trajectory models for MCF-7 cells undergoing proliferation or differentiation in response to EGF or HRG, respectively. <b>B.</b> Experimental design of the time course experiments where time points were selected to cover early to late stages of the cell fate transition.</p

Figure S3. Sharing of peaking TSSs for known IEGs and candidate IEGs in the robust set from Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

For each gene in the robust set, bar charts show the number of datasets where each TSS is found to peak

Figure S8. Distributions of expression change and t_p across datasets from Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

The differences between maximal expression and basal expression are greater for IEG than for other protein coding (A) and for non-coding genes (B). Times of peaking are comparable between peaking IEGs and other protein coding genes (C) but are significantly different between IEGs and non-coding RNAs (significant difference is indicated by an asterisk)

Supplementary data file 1 from Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

Peaking times for genes in the robust set in all dataset

Examples of promoters with generic and stimuli-specific expression profiles.

<p><b>A.</b> the promoter region maps to gene SEMA3F and has a generic expression profile across the EGF and HRG profiles. <b>B.</b> the promoter mapping to SULF2 also has a generic profile. <b>C.</b> the stimuli-specific promoter maps to gene FHL2 and <b>D.</b> the stimuli-specific promoter maps to gene FLNA.</p

Figure S5. Classifications for non-coding RNA TSS from Conserved temporal ordering of promoter activation implicates common mechanisms governing the immediate early response across cell types and stimuli

Stacked barcharts show, for each dataset, the numbers of TSSs for non-coding genes classified as peak, decay, dip, and linear, and the total percentage of TSS that could be classified