two PWMs using the traditional Smith-Waterman algorithm for sequence alignment. The median pair-wise PWM similarity scores for the 95 TF-pairs used in the main analysis are provided in worksheet #1 in supplementary Data file S2. We have additionally ensured that the pairwise motif similarity is not correlated with the motif complexity (Data file S1). Gene expression Human gene expression data was obtained from the Novartis human tissue survey [20] processed with the gcRMA preprocess- ing probe set algorithm and consists of 79 different tissue samples. We used this data directly provided ‘‘as is’’ from the Novartis website without any further processing steps. We refer the readers to their web documentation (wombat.gnf.org) for details. Howev- er, we only considered data from the U133A human array since we had reliable annotation data only for this chip. The gcRMA processed data was chosen since gcRMA offers a good balance between accuracy and precision for analyzing gene expression data, and correcting for background noise[37]. We also repeated our analysis on a subset of 23 tissues that were previously identified to be independent and thus, non-redundant[24]. Each sample included expression values for 17220 human genes. Recently, there have been several papers addressing the interpretation of Affymetrix gene expression data[38,39]. We have chosen the mappings from probe to transcript protein mapping (IPI identifier) provided in [39] for the U133A human array in our analysis. Mappings from IPI protein identifier to genes were obtained from Ensembl release 47 (www.ensembl.org). Because multiple probes are mapped to a single gene or transcript, the probe-level data needs to be aggregated in order to obtain a gene-level or transcript-level expression value, which invariably raises issues of accuracy. Probe-level expression analysis bypasses these issues and yield reliable results[39]. However, we have shown that our primary result holds at the probe, transcript and gene level, with three different approaches to aggregating the probe-level data (see below). It is also well-established that the lowest level intensity probes are most prone to error. Therefore, we have disregarded all probes having a value less than 50 (,8% of all probes). The median gene expression values for the 98 TF genes used in this study across 79 tissues are provided in worksheet #2 in supplementary Data file S2. Genes and transcripts having multiple probe sets On the Affymetrix chip underlying the genome-wide expression studies, genes are often represented by multiple probe sets and thus, have multiple expression values. Several solutions have been posed to contend with this issue: (i) only choosing probe sets with a one-to-one correspondence with genes[40], (ii) choosing for each gene the probe set with the maximum expression [41] and (iii) choosing for each gene a probe set at random[42]. To account for the expression values for all probe sets while minimizing the effect of erroneous values, as the default, we used the median expression value across the probe sets for a given gene. However, to ensure the robustness of our results, we have repeated our analysis by also using both the maximum probe value and selecting one probe set at random given a choice of multiple probe sets. Similarly, we have used three aggregation strategies for the transcript-level analyses and finally we have also done a probe-level analysis to bypass the aggregation altogether. Kendall tau measure of correlation Pearson correlation coefficient (PCC) is a standard measure of interdependence between two random variables. PCC is applica- ble only when we wish to measure a linear relationship between Diversification of TF Paralogs two variables and the variables are from bivariate normal distributions. Because we cannot assume normally-distributed data in our case, we require a nonparametric measure of correlation. The two most common choices are Spearman’s rank order correlation (Spearman rho) and Kendall tau correlation. Both measures offer similar sensitivity in detecting associations and almost always lead to the same conclusions. However, we use Kendall’s tau because it has better statistical properties and there is a direct interpretation of Kendall’s tau in terms of probabilities of observing concordant and discordant pairs[31]. It is important to note that the correlation measure we have employed is inherently conservative. As a result, we do not expect strong correlation values based on the nature of the data being dealt with. The more relevant factors for our analysis are the significance and sign of correlations. Our choice of correlation measure inherently assumes that the input data consists of independent samples. In our application, this assumption is not necessarily true and consequently employing the theoretical p-value estimation of Kendall’ tau is potentially erroneous. Therefore, we chose to use a permutation-based method for computing the p-value for Kendall’s tau. In a given tissue sample, we pool all expression values for all TFs in our dataset, and then randomly assign expression values to each TF. This procedure effectively shuffles the TF expression values and hence shuffles the corresponding hE values for the paralogous TF pairs. We compute the Kendall’s tau for 1000 such permutations and use the fraction of permutations in which the tau value exceeds the observed tau value (for the un-shuffled data) as an estimate of the p-value. KOG family analysis KOG data and the associated gi identifiers were obtained from ftp://ftp.ncbi.nih.gov/pub/COG/KOG/. The proteins were mapped to Ensembl transcript and gene ids using data from www.ensembl.org. The KOG data resulted in 242 paralogous pairs with 99 unique genes. In terms of families, we obtained 24 families, 9 of size 2, 6 of size 3, and 9 of size greater than or equal to 4. Ks analysis Nucleotide coding sequences were obtained from Ensembl for all TFs, and the pairwise Ks rates computed using the bp_pairwise_kaks.pl script from BioPerl[43]. Supporting Information Data File S1 This file contains supplementary results Found at: doi:10.1371/journal.pone.0002345.s001 (0.22 DOC) MB Data File S2 This contains supplementary data in two work- sheets Found at: doi:10.1371/journal.pone.0002345.s002 (0.04 MB XLS) Acknowledgments Authors would like to thank S. Mahony for providing the pair-wise PWM similarity measures, P. Sethupathy, S. Date, G. Grant, C. Maley, U. Ohler, and the anonymous reviewers for providing excellent suggestions to improve the manuscript. Author Contributions Conceived and designed the experiments: SH. Performed the experiments: LS. Analyzed the data: LS. Wrote the paper: SH LS. PLoS ONE | www.plosone.org 11 June 2008 | Volume 3 | Issue 6 | e2345