Supplementary text and figures: Comparative assessment of methods for aligning multiple genome sequences

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1 Supplementary text and figures: Comparative assessment of methods for aligning multiple genome sequences Xiaoyu Chen Martin Tompa Department of Computer Science and Engineering Department of Genome Sciences Box University of Washington Seattle, WA U.S.A. Nature Biotechnology: doi:.38/nbt.637

2 S Comparing StatSigMA-w with Previous Accuracy Assessments Margulies et al. [2] estimated the specificity of the ENCODE alignments using the following two measures: Alu exclusion, defined as the fraction of human Alu residues that are not aligned in other species, and coding sequence periodicity. By both of these measures, they found TBA and Pecan to be the most accurate alignments, with TBA slightly more accurate than Pecan. They performed these analyses only on the mammals, providing no information about the accuracies of nonmammal alignments. Because Alu sequences occur mostly in intronic and intergenic regions [], their analysis should correspond to our accuracy analysis in the intronic and intergenic categories. For the placental mammals, we do indeed find that TBA and Pecan are comparable and have the lowest suspicious% values. However, our accuracy results disagree sharply with theirs on the nonplacental species included in both analyses, namely monodelphis and platypus, in each of the four location categories. As Figure 3 in the main paper illustrates, suspicious% increases in these species in the order Pecan, MAVID, TBA, MLAGAN. In the intronic and intergenic categories, TBA s suspicious% is 5 times that of Pecan in platypus, and -2 times that of Pecan in monodelphis. In contrast, in terms of Alu exclusion for these two species, Margulies et al. [2] showed that TBA is best, with Pecan and MAVID close behind. Unlike our analysis, in their analysis monodelphis and platypus do not exhibit significantly different patterns of alignment accuracy from the placental mammals cow, dog, armadillo, elephant, tenrec, shrew, bat, and rabbit. Differences between the accuracy measures may have led to these differences in conclusions. First, Alu sequences represent only a narrow portion of the spectrum of genomic sequences. Even if no nonhuman sequences are aligned to human Alu bases, it remains unknown whether those sites that are aligned are done so correctly. Second, Alu exclusion is calculated with reference to the total number of human Alu residues, rather than the number of human residues aligned to a given species. As such, two species that have the same Alu exclusion may achieve very different alignment specificity. For example, in the TBA alignments, coverage of human intergenic/intronic residues is 2 Mbp for armadillo and 6 Mbp for monodelphis. These two species have a very similar level of Alu exclusion (96%) for TBA. These facts together suggest that the percentage of misaligned residues is twice as high in monodelphis as in armadillo. S2 Correlation of Discordance and Alignment Agreement This section describes a clear correlation between level of alignment agreement and suspicious regions. Namely, when compared to low discordance regions, suspicious regions are highly depleted in alignment-agreeing coordinates and highly enriched in alignment-unique coordinates. This correlation gives additional supporting evidence from the other alignments 2 Nature Biotechnology: doi:.38/nbt.637

3 % 9% 8% 7% agree% 6% 5% 4% 3% 2% % % chimp baboon macaque marmoset galago bat armadillo dog elephant cow rabbit mouse rat shrew susp_tba susp_mavid susp_mlagan good_tba good_mavid good_mlagan tenrec monodelphis platypus chicken xenopus tetraodon fugu zebrafish Figure S: Agreement percentage in suspicious and low discordance regions for each species and three alignments. In the legend, susp denotes suspicious regions and good denotes low discordance regions. Note how much greater agree% is in low discordance regions than in suspicious regions, for each alignment and each species. that suspicious regions may be misaligned. If one alignment A (for example, TBA) misaligns a human coordinate h to a coordinate s from a given species S (for example, mouse), the other alignments are unlikely to align h to the same coordinate s, particularly if s is part of a longer region of S that is misaligned in A. Recall from the section on level of alignment agreement that this human coordinate h is said to agree (for target alignment A and target species S) if and only if there is at least one other alignment that aligns h to the same coordinate s of S. Therefore, given suspicious regions of the alignment A where species S is the worst aligned species, we expect the comparison percentage agree% for A and S to be very low in these regions. Conversely, suppose we consider a region of alignment A that contains human and species S, and for which StatSigMA-w s reported discordance score is less than at all sites in the region. We call such regions low discordance for alignment A and species S. In such a low discordance region we expect the comparison percentage agree% for A and S to be high. Figure S plots the comparison percentage agree% in both suspicious regions and low discordance regions for all 22 nonhuman species and the three alignments for which we have computed comparison percentages. The correlation of agree% with region type is as expected, and demonstrates significant difference between the two types of regions, for any given species: agree% varies between.5% and 3% for suspicious regions, whereas it varies between 56% and 99% for low discordance regions. To make this difference even clearer, the ratio of agree% in low discordance regions to agree% in suspicious regions exceeds 5.8, for each species and each alignment. That is, suspicious regions are highly depleted in 3 Nature Biotechnology: doi:.38/nbt.637

4 % 9% 8% 7% unique% 6% 5% 4% 3% 2% % % chimp baboon macaque marmoset galago bat armadillo dog elephant cow rabbit mouse rat shrew susp_tba susp_mavid susp_mlagan good_tba good_mavid good_mlagan tenrec monodelphis platypus chicken xenopus tetraodon fugu zebrafish Figure S2: Unique percentage in suspicious and low discordance regions for each species and three alignments. In the legend, susp denotes suspicious regions and good denotes low discordance regions. Note how much greater unique% is in suspicious regions than in low discordance regions, for each alignment and each species. alignment-agreeing coordinates compared to low discordance regions. In suspicious regions of alignment A we would expect unique% for A to be high, and this is nearly always the case for all alignments and all species. Figure S2 plots the comparison percentage unique% in both suspicious regions and low discordance regions for all 22 nonhuman species and the three alignments for which we have computed comparison percentages. The correlation of unique% with region type is again as expected, and demonstrates significant difference between the two types of regions: unique% varies between 39% and 9% for suspicious regions, whereas it varies between.2% and 2% for low discordance regions. To make this difference even clearer, the ratio of unique% in suspicious regions to unique% in low discordance regions exceeds 3, for each species and each alignment. That is, suspicious regions are highly enriched in alignment-unique coordinates compared to low discordance regions. The general trends in all the curves of Figures S and S2 is that agree% decreases and unique% increases as species distance to human increases. This trend is in agreement with the more general trend observed for all the noncoding location categories in Figure 2 of the main paper. This is not coincidental: since agree% is so low and unique% so high in noncoding regions of the distant species, there will tend to be fewer agreeing coordinates and more unique coordinates in nearly any subset of coordinates. What this says, though, is that the correlations shown in Figures S and S2 for the suspicious regions of placental mammals are all the more striking. Whereas agree% > 43% for all alignments, all placental mammals, and all location categories generally, agree% < 3% in suspicious regions for all alignments and 4 Nature Biotechnology: doi:.38/nbt.637

5 all placental mammals. Conversely, whereas unique% < 25% for all alignments, all placental mammals, and all location categories generally, unique% > 38% in suspicious regions for all alignments and all placental mammals. S3 Improving Suspicious Alignments Figure S3 shows scatter plots for three representative species (baboon, mouse, and zebrafish) and each of the four alignments as target alignment. Notice that, for any given species, the distribution of points is very similar for all four alignments. This suggests that StatSigMA-w is not biased toward any particular alignment method. The plots labeled Baboon (+/ ) show baboon alignments using the same pairwise alignment scoring function used for mouse and zebrafish. The plots labeled Baboon (+/ 2) show baboon alignments with mismatch score 2, which better reflects the smaller divergence between human and baboon. The change in scoring function does not have much effect on the shapes of the scatter plots. S4 Length Distribution of Gaps Figure S4 shows the length distribution of gaps for the four alignments in ENm3, a representative ENCODE region. Although the figure only shows the distributions for two selected species, the same trends hold for all species. Note the small fraction of gaps exceeding 5 bp in TBA compared to the other three alignments. S5 Assessing Whole-Genome Multiple Sequence Alignments In the future, we plan to apply our analyses to whole-genome multiple sequence alignments, particularly when comparable whole-genome alignments (that is, using the same species and assemblies) are available to assess and compare. These analyses will guide alignment users in their choice of alignment and will warn them about regions that may be misaligned. The only difficulty we envision in extending our analyses to whole-genome alignments is the amount of computation involved: we estimate that performing these analyses on a wholegenome alignment such as the MULTIZ vertebrate alignment currently available from the UCSC Genome Browser would require a few weeks on a few hundred processors. We expect the coverage and accuracy results presented here for % of the whole-genome alignment to be representative of what we will see when applied to the whole genome. It is possible that accuracy will be slightly worse in whole-genome alignments, because the challenge of identifying orthologous regions to align to whole human chromosomes is so much greater than it was in the ENCODE pilot project, where the aligners were given as input the orthologous sequences for each individual human ENCODE region. This predicted decrease in accuracy 5 Nature Biotechnology: doi:.38/nbt.637

6 Baboon (+/ ) Baboon (+/ ) Baboon (+/ ) Baboon (+/ ) Alternative alignment score Alternative alignment score.5.5 Baboon (+/ 2).5.5 Baboon (+/ 2).5.5 Baboon (+/ 2).5.5 Baboon (+/ 2) Mouse Mouse Mouse Mouse Alternative alignment score Zebrafish Zebrafish Zebrafish Zebrafish Alternative alignment score.5 MAVID alignment score.5 MLAGAN alignment score.5 TBA alignment score.5 PECAN alignment score Figure S3: Alignment scores of suspicious regions versus scores for alternative alignments of the same human region. For three representative species S (baboon, mouse, and zebrafish) and four representative target alignments, scatter plots show all points (x, y ), where x is the pairwise human-s alignment score of the target alignment region that is suspicious for species S, and y is the pairwise human-s alignment score of one of the other three alignments for the same human region that is not suspicious for S. Alignment scores are normalized by alignment length. The dashed black diagonal 6 line has equation y = x. The solid blue line has equation y x = µ, where µ is the mean value of y x for all points (x, y ) in the plot. The dotted blue lines have equations y x = µ ± σ, where σ is the standard deviation of Nature Biotechnology: y x for all doi:.38/nbt.637 points (x, y ) in the plot.

7 is consistent with what we have seen when comparing the TBA ENCODE alignment to the 7-vertebrate MULTIZ alignment of human chromosome analyzed by Prakash and Tompa [3], where the suspicious% figures of the former are about.7 times those of the latter, averaged over the nonprimate species common to both alignments. References [] C. Chen, A. J. Gentles, J. Jurka, and S. Karlin. Genes, pseudogenes, and ALU sequence organization across human chromosomes 2 and 22. Proceedings of the National Academy of Science USA, 99(5): , Mar. 22. [2] E. H. Margulies, G. M. Cooper, G. Asimenos, D. J. Thomas, C. N. Dewey, A. Siepel, E. Birney, D. Keefe, A. S. Schwartz, M. Hou, J. Taylor, S. Nikolaev, J. I. Montoya-Burgos, A. Lvytynoja, S. Whelan, F. Pardi, T. Massingham, J. B. Brown, P. Bickel, I. Holmes, J. C. Mullikin, A. Ureta-Vidal, B. Paten, E. A. Stone, K. R. Rosenbloom, W. J. Kent, G. G. Bouffard, X. Guan, N. F. Hansen, J. R. Idol, V. V. Maduro, B. Maskeri, J. C. McDowell, M. Park, P. J. Thomas, A. C. Young, R. W. Blakesley, D. M. Muzny, E. Sodergren, D. A. Wheeler, K. C. Worley, H. Jiang, G. M. Weinstock, R. A. Gibbs, T. Graves, R. Fulton, E. R. Mardis, R. K. Wilson, M. Clamp, J. Cuff, S. Gnerre, D. B. Jaffe, J. L. Chang, K. Lindblad-Toh, E. S. Lander, A. Hinrichs, H. Trumbower, H. Clawson, A. Zweig, R. M. Kuhn, G. Barber, R. Harte, D. Karolchik, M. A. Field, R. A. Moore, C. A. Matthewson, J. E. Schein, M. A. Marra, S. E. Antonarakis, S. Batzoglou, N. Goldman, R. Hardison, D. Haussler, W. Miller, L. Pachter, E. D. Green, and A. Sidow. Analyses of deep mammalian sequence alignments and constraint predictions for % of the human genome. Genome Research, 7(6):76 774, June 27. [3] A. Prakash and M. Tompa. Measuring the accuracy of genome-size multiple alignments. Genome Biology, 8(6):R24, Nature Biotechnology: doi:.38/nbt.637

8 .35 Mouse: short gaps (up to 5 bp).8 Mouse: long gaps (> 5 bp) TBA MAVID MLAGAN PECAN.6.4 TBA MAVID MLAGAN PECAN Dog: short gaps (up to 5 bp) Gap size Dog: long gaps (> 5 bp) > Figure S4: Length distribution of gaps in the alignments of ENCODE region ENm3. The distributions are shown for four alignments and two representative species, mouse and dog. The left panel shows the distributions for gaps of length -5 bp, and the right panel shows the distributions for gaps of length exceeding 5 bp. Note the small fraction of gaps exceeding 5 bp in TBA compared to the other three alignments. 8 Nature Biotechnology: doi:.38/nbt.637