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Research Article
Transparent Process
Open Access

Mammalian splicing divergence is shaped by drift, buffering in trans, and a scaling law

View ORCID ProfileXudong Zou, View ORCID ProfileBernhard Schaefke, Yisheng Li, Fujian Jia, Wei Sun, Guipeng Li, Weizheng Liang, Tristan Reif, Florian Heyd, Qingsong Gao, Shuye Tian, Yanping Li, Yisen Tang, View ORCID ProfileLiang Fang, View ORCID ProfileYuhui Hu, View ORCID ProfileWei Chen  Correspondence email
Xudong Zou
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Conceptualization, Data curation, Software, Formal analysis, Methodology, Project administration, Writing—original draft, review, and editing
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Bernhard Schaefke
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
3Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China
Roles: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing—review and editing
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Yisheng Li
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Data curation, Formal analysis, Writing—review and editing
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Fujian Jia
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Formal analysis
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Wei Sun
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Resources, Data curation
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Guipeng Li
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
3Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China
Roles: Software, Methodology
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Weizheng Liang
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Validation
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Tristan Reif
4Institute for Biochemistry, Freie Universität Berlin, Berlin, Germany
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Florian Heyd
4Institute for Biochemistry, Freie Universität Berlin, Berlin, Germany
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Qingsong Gao
5Laboratory for Systems Biology and Functional Genomics, Berlin Institute for Medical Systems Biology, Max-Delbrück-Centrum für Molekulare Medizin, Berlin, Germany
Roles: Methodology
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Shuye Tian
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Data curation, Validation
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Yanping Li
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Data curation, Validation
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Yisen Tang
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
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Liang Fang
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
3Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China
Roles: Conceptualization, Validation
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Yuhui Hu
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
Roles: Methodology, Writing—review and editing
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Wei Chen
1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
2Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
3Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China
Roles: Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing—review and editing
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  • For correspondence: chenw@sustech.edu.cn
Published 30 December 2021. DOI: 10.26508/lsa.202101333
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  • Figure 1.
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    Figure 1. Quantification of alternative splicing patterns and diversity within and across tissues.

    (A) Scheme of experimental design. (B) Proportion of high percent dominant isoform (PDI) (PDI > 0.9) and low PDI (PDI ≤ 0.9) SE events within each tissue. Numbers on the top of bars indicate the number of events in corresponding categories. (C) Correlation of PDI of SE event and gene expression of the corresponding gene in cerebral cortex. (B) Events are classified into two groups based on PDI values as in (B). The regression line (red) is fitted with a generalized additive model. See also Tables S1–S4. (D) 203 micro-exons exhibit Switch-Like changes between cerebral cortex and other tissue(s). The percent spliced in difference between cerebral cortex and other tissues (cortex–most different tissue) reveals higher inclusion levels in cerebral cortex than in other tissues. Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest, and highest value within 1.5 IQR.

  • Figure S1.
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    Figure S1. Correlations between pairs of the 14 samples (two biological replicates of the seven tissues/cell lines).

    The lower triangle shows the scatter plots of percent spliced in of sample pairs, and the upper triangle shows Pearson’s correlation coefficients of sample pairs. Correlation coefficients between replicates in each tissue/cell line are highlighted in green. The lower correlation between two replicate ES samples is likely due to the fact that the two samples are from two distinct ES clones.

  • Figure S2.
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    Figure S2. Switch-Like events show potentially functional importance.

    (A) Statistics of events expressed in different numbers of tissues. Numbers on the top of bars indicate the total number of events in corresponding categories. (B, C, D, E) Among the 11,729 events expressed in two or more tissues, 4,328 were classified as “Non-Differential” (switch score < 0.1), 3,229 as “Low” (0.1 ≤ switch score < 0.2), 1,443 as “Moderate-Low” (0.2 ≤ switch score < 0.3), 1,331 as “Moderate-High” (0.3 ≤ switch score < 0.5) and 1,398 as “Switch-Like” (switch score > 0.5). According to the error hypothesis, we would expect that genes with higher switch scores have lower expression levels and higher dN/dS ratios than those with smaller differences. However, we found patterns resembling U-shaped or hump-shaped distributions: “Non-Differential” and “Switch-Like” events both affect coding regions more often than events with “Moderate-Low” or “Moderate-High” switch scores (B; numbers in each bar denote the number of coding and non-coding SE events in the corresponding group). (C, D) Furthermore, the genes harboring events in either of these categories have higher expression levels (C) and those with “Switch-Like” events also have significantly lower dN/dS ratios than those with events in the “Low” and “Moderate-Low” categories (D). Importantly, the flanking regions of “Switch-Like” events also are more conserved (with higher PhastCons scores) than those of events in all three intermediate categories. (E) Events expressed in two or more tissues and with exon size ≥60 bp and intron size ≥200 bp were used. The mean PhastCons score of each position across events within the same tissue-regulatory group for exonic regions (including 30 bp at both 5′ and 3′ end of alternative exon) and intronic regions (200 bp flanking sequences on each side) around alternative splicing sites was compared between Switch-Like events and the other four tissue-regulatory groups. (F) Enrichment of micro-exons in Switch-Like events. Events expressed in two or more tissues are considered. Percentages of micro-exons (exons with length ≤ 30 bp) and regular exons are indicated for the five tissue-regulatory groups. Numbers within and on top of bars denote the number of regular exons and micro-exons in each group, respectively. (B, C) One-sided Wilcoxon rank-sum test was used in (B) and (C). Box plot elements: boxes span values from the first (Q1) to the third (Q3) quartile, with a horizontal line indicating the median (Q2). The whiskers extend from the edges of the box to the points representing the largest and smallest observed values within the 1.5 * IQR (interquartile range = Q3 − Q1) from the edges of the box. Outlier values are plotted as dots outside the whiskers. n.s. not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure 2.
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    Figure 2. Cis-regulatory sequence variants and scaling law affect allelic splicing divergence.

    (A) Classification of events expressed in two or more tissues into three groups based on allelic divergence across tissues. Numbers on the top of bars denote the count and percentage of events in corresponding groups. (B) Distribution of variant density in each divergence group. Variants in the alternative exon plus its intronic flanking regions (200 bp on both sides) were counted and divided by the total length of the region. Numbers in parentheses denote the numbers of events in corresponding groups (Two-sided Wilcoxon rank-sum test). (C) Percentage of SE events with (steel blue) or without (light sky blue) variants in splicing sites. Only the 5′ and 3′ splicing site of the alternative exon are considered. Numbers on the top of the bars indicate the total number of events within the corresponding groups (Fisher’s exact test). (D) Cumulative distributions of the absolute difference between predicted scores for splicing site strengths of C57BL/6J (BL6) and SPRET/EiJ (SPR). Scores of the 5′ and 3′ splicing sites were summed up for each alternative exon. (E) Scaling law for allelic splicing divergence. The average BL6 percent spliced in (PSI) across tissues is treated as starting PSI, and the |ΔPSI| (y-axis) between SPR and BL6 is compared between different ranges of starting PSI (x-axis). Numbers on the top of boxes indicate the number of events in corresponding PSI ranges (one-sided Wilcoxon rank-sum test). (F) Distribution of variant density in alternative exon and its flanking introns (200 bp on both sides) for events in different PSI ranges. (E) The 10 bins of PSI are based on the average PSI of the BL6 allele across expressing tissues (starting allele in E). Boxplots show the Q1 to Q3 quartile values (the box limits), the median (the horizontal lines), and values within the 1.5 * IQR (the whiskers). n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure S3.
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    Figure S3. All-Divergent events have higher magnitude of ΔPSI between alleles; allelic divergence primarily affects genes under relaxed selective constraints.

    (A, B, C) Median, maximum, and minimum of absolute ΔPSI across divergent tissues are compared between All-Divergent (steel blue) and Some-Divergent (golden yellow) events (one-sided Wilcoxon rank-sum test). (D, E, F) Comparing median, maximum, and minimum of absolute ΔPSI between events in All-Divergent with (deep sky blue) and without (light sky blue) splice site change (one-sided Wilcoxon rank-sum test). (G) dN/dS ratios of genes associated with SE events belonging to different divergence groups (indicated by different colors). Events expressed in two to seven tissues whose associated genes have orthologs in rat are used (one-sided Wilcoxon rank-sum test). (H) Genes associated with “All-Divergent” events have lower expression levels than those associated with “Non-Divergent” events (one-sided Wilcoxon rank-sum test). Events expressed in two to seven tissues are used. (I) Comparison of dN/dS ratio between genes associated with divergent events (cyan) and those associated with non-divergent events (red) in each individual tissue (one-sided Wilcoxon rank-sum test). (J) Comparison of gene expression level between genes associated with divergent and those associated with non-divergent events in each tissue (one-sided Wilcoxon rank-sum test). Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure 3.
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    Figure 3. Genes under relaxed selective constraints exhibit more divergence in alternative splicing.

    (A) Proportion of coding (steel blue) and non-coding (light sky blue) alternative exons in the three divergence groups. The percentage of coding alternative exons in each group is indicated within the corresponding bar, and numbers on the top of bars denote total numbers of events in the corresponding groups. (B) Comparison of dN/dS ratios of genes belonging to different divergence groups (indicated by different colors) based on splicing divergence score score (see the Materials and Methods section). Numbers in parentheses indicate the number of genes within corresponding groups (one-sided Wilcoxon rank-sum test). (C) Comparison of average gene expression level among the three divergence groups. Numbers in parentheses indicate the number of genes within corresponding groups (one-sided Wilcoxon rank-sum test).

  • Figure S4.
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    Figure S4. Comparing splicing divergence patterns between down-sampled dataset and original dataset.

    (A, B, C, D, E, F, G) We down-sampled junction reads of each event in either allele across all samples to the same level of 20 reads, and determined divergent and non-divergent splicing based on percent spliced in (PSI) difference (divergent: ΔPSI ≥ 0.1). The x-axis and y-axis denote PSI difference before down-sampling and after down-sampling, respectively. The colors distinguish “Divergent” (red) and “Non-Divergent” (light blue) in the original dataset. The proportions of consistent classification of “Divergent” events and “Non-Divergent” events between down-sampled (use PSI difference of 0.1 as cutoff) and original datasets have been labeled near the corresponding points. (H) The mean and 95% confidence interval (calculated by down-sampling for 100 times) of proportions of consistent classification of “Divergent” events (red) and “Non-Divergent” events (blue) between down-sampled and original datasets. (I) The mean and 95% confidence interval (calculated by down-sampling for 100 times) of Pearson’s correlation coefficient (R) of PSI divergence between down-sampled and original datasets.

  • Figure S5.
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    Figure S5. Divergence analysis with down-sampled dataset.

    (A) Comparison of average gene expression level among the three divergence groups. Numbers in parentheses indicate the number of genes within corresponding groups. (One-sided Wilcoxon rank-sum test). (B) Comparison of average gene expression level of genes with “Some-Divergent” events in divergent tissues versus non-divergent tissues, the number in parentheses is the number of “Some-Divergent” events (One-sided Wilcoxon signed-rank test). (C) Distribution of Spearman’s correlation coefficients between |ΔPSI| and gene expression across tissues for events expressed in ≥3 tissues and divergent in ≥1 tissue(s) (one-sided Wilcoxon signed-rank test). Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure S6.
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    Figure S6. Switch-Like events and Non-Differential events both are more conserved than intermediate groups.

    The bar plot shows the proportions of the three divergence groups in each of the five tissue-regulatory groups. Allelic events expressed in two or more tissues which are also included in the set of expressed events based on total reads were used. The classification of tissue-regulatory groups is based on the switch score calculated from total reads. The percentages of “Non-Divergent” and “Some-Divergent” events are indicated in each bar, and the total number of events in each group is indicated in parentheses. When comparing the alternative splicing patterns across tissues, “Switch-Like” events also are more conserved between the C57BL/6J and the SPRET/EIJ allele (see also Table S8), indicating that for the majority of Switch-Like events, the tissue-switch between the two isoforms is functional in both alleles.

  • Figure S7.
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    Figure S7. Five candidate genes with potentially functional alternative splicing events.

    (A) Allele-specific splicing patterns of exon 5 in Pkp4 across tissues. Two single-nucleotide variants between the two species have been identified in the 5′ splicing site of this exon. (B) Tissue-dependent allele-specific splicing patterns of exon 7 in Apbb3. Three variants including two small indels (indicated with asterisks) and one single-nucleotide variant (ellipse) were predicted to contribute. (C) Allele-specific splicing pattern of exon 6 in Thyn1. This event is divergent between the two alleles in all expressing tissues. (D) Allele-specific splicing pattern of exon 19 in Limch1. This exon is partially included only in heart and cortex in the SPRET/EiJ allele, whereas it is completely skipped in all expressing tissues in the C57BL/6J allele. (E) Allele-specific splicing pattern of exon 7 in Mff. This exon is heart-specifically included in the SPRET/EiJ allele, while it is completely skipped in all expressing tissues in the C57BL/6J allele. The gene local structure around the alternative exon and sequence conservation are presented with the alternative exon highlighted in red.

  • Figure 4.
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    Figure 4. Negative correlation between gene expression and splicing divergence.

    (A) Comparison of average gene expression level of genes with “Some-Divergent” events in divergent tissues versus non-divergent tissues, the number in parentheses is the number of “Some-Divergent” events (One-sided Wilcoxon signed-rank test). (B) Distribution of Spearman’s correlation coefficients between |ΔPSI| and gene expression across tissues for events expressed in ≥3 tissues and divergent in ≥1 tissue(s) (one-sided Wilcoxon signed-rank test). Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure S8.
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    Figure S8. Relationship between total junction reads and percent spliced in (PSI) difference between biological replicates.

    The left panel shows the relationship between total reads count (all junction reads, log2 transformed) and PSI difference between replicates based on the original dataset. The right panel shows the relationship between original read count (log2 transformed) and PSI difference estimated based on the down-sampled dataset. The red line in each scatter plot was fitted by linear regression. Pearson’s correlation coefficient (R) and P-value are presented for each subfigure.

  • Figure 5.
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    Figure 5. Patterns of tissue-dependent allelic splicing divergence.

    (A) Scheme illustrating the four scenarios. Each segment indicates the pattern of splicing change of one allele between tissues and the two alleles are indicated with two different colors. (B) Allele-specific tissue-regulatory patterns. The x-axis presents the percent spliced in (PSI) difference of the C57BL/6J (BL6) allele between the two tissues with maximal difference in allelic ΔPSI, and the y-axis presents the corresponding information for the SPRET/EiJ (SPR) allele. (C) Scenario 2 holds higher PSI difference between tissues than Scenario 3. The maximum between-tissue PSI differences of the two alleles are compared among the four scenarios. Median value of the ΔPSIT in each scenario is indicated in the box, and numbers in parentheses represent the numbers of events belonging to the corresponding scenarios. One-sided Wilcoxon rank-sum test was used to test for significance. (D) Micro-exons are enriched in Scenario 2 and less likely to be in Scenario 3 or 4. The bar plot shows the percentage of each scenario compared between micro-exons (dark orange) and other exons (forest green). Fisher’s exact test was used to test for significance. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure S9.
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    Figure S9. Classification of the four scenarios with more stringent percent spliced in difference threshold (0.2).

    We changed the threshold of 0.1 as in Fig 5B to 0.2, and the number of events in “Scenario 3” (761) is still much higher than that in “Scenario 1” (1) and “Scenario 2” (121).

  • Figure S10.
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    Figure S10. Tissue-regulatory patterns of splicing divergence in four selected tissues (cortex, heart, kidney, and liver).

    (A) Tissue-regulatory patterns of splicing divergence in four selected tissues. The x-axis presents the ΔPSI of the C57BL/6J (BL6) allele between the two tissues with maximal difference in allelic ΔPSI, and the y-axis presents the corresponding information for the SPRET/EiJ (SPR) allele. (B) Definition of the conserved allele by using two out-groups (Mus caroli and Mus pahari). (C) Micro-exon events in Scenario 2 have larger PSI differences between tissues than those falling in Scenario 3. The magnitude of the difference is even bigger than the difference between the two scenarios in all exons. The y-axis represents the maximum of {∆PSI between tissues of the BL6 allele, ∆PSI between tissues of the SPR allele}. The median value of the ∆PSI in each scenario is indicated in the box, and numbers in parentheses represent the numbers of events belonging to the corresponding scenarios. One-sided Wilcoxon rank-sum test was used to test for significance. Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure 6.
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    Figure 6. Many previously buffered non-adaptive cis-regulatory changes are unmasked by perturbation of the splicing machinery.

    (A) Incidence of Retained Introns (RI) and Skipped Exons (SE) increased after pladienolide B (0.1 μM) treatment (one-sided Wilcoxon signed-rank test). (B) Increase in allelic splicing divergence after pladienolide B treatment. x-axis and y-axis are percent spliced in (PSI) divergence between the two alleles in the DMSO treated sample and the pladienolide B–treated sample, respectively. Events are classified into different groups based on PSI divergence in the two samples. The count of events in each group is indicated by numbers in parentheses. (C) Patterns of PSI divergence after pladienolide B treatment are consistent with those in other F1 tissues. Events were classified into different groups based on allelic splicing divergence after pladienolide B treatment, and the maximal ΔPSI between alleles in F1 tissues are compared for the above groups. (D) Divergent events in F1 tissues are also more likely divergent after pladienolide B treatment and with consistent direction. Events were classified into three groups based on the divergence pattern in F1 tissues, and the ΔPSI (BL6-SPR) values were compared between pladienolide B–treated samples and DMSO-treated samples. Outliers in each group are hidden and the y-axis has been limited to −0.35 to 0.35. One-sided Wilcoxon signed-rank test was used to test difference from zero for median values within each group, whereas one-sided Wilcoxon rank-sum test was used for comparing between groups. Box plot elements: center line, median; box limits, lower and upper quartiles; whiskers, lowest and highest value within 1.5 IQR. n.s., not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

  • Figure S11.
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    Figure S11. Additive effects (“A”) on splicing efficiency of cis-regulatory variants under treatment (“A_PladB”) and control (“A_DMSO”) samples.

    We used the lower percent spliced in (PSI) value of the two alleles as starting PSI and the higher one as the final PSI to calculate the effects on splicing efficiency of cis-regulatory variants under treatment and control samples separately and compared “A” between the two conditions. Wilcoxon rank-sum test was used to test the difference.

  • Figure S12.
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    Figure S12. Sequence conservation and average percent dominant isoform (PDI) between tissue-regulatory patterns, divergent patterns between tissues.

    (A) Sequence conservation of regions around splicing sites of Non-Differential and Switch-Like events. Events expressed in two or more tissues and with exon size ≥60 bp and intron size ≥200 bp were used. The mean PhastCons score of each position across events within the same tissue-regulatory group for exonic regions (including 30 bp at both 5′ and 3′ end of alternative exon) and intronic regions (200-bp flanking sequences on each side) around alternative splicing sites was compared between Non-Differential events with high PDI (PDI > 0.9) and Non-Differential events with low PDI (PDI ≤ 0.9) and Switch-Like events. (B) Comparison of average PDI of events in the five tissue-regulatory groups. (C) Stacked bar plot shows the proportion of allelic divergence patterns between two tissues. Only events expressed in the selected tissue (indicated below each bar) and one other tissue are used. Proportions of four allelic divergence patterns are indicated with different colors within each bar.

Supplementary Materials

  • Figures
  • Table S1 Sequencing and mapping statistics.

  • Table S2 Statistics of alternative splicing events across tissues.

  • Table S3 Quantification of skipped exon events across tissues.

  • Table S4 Diversity of alternative splicing in each tissue according to percent dominant isoform values of SE events.

  • Table S5 Correlation between percent dominant isoform and gene expression and correlation between percent dominant isoform and absolute ΔPSI between replicates within each tissue.

  • Table S6 Quantification of allelic alternative splicing divergence across tissues.

  • Table S7 Tissue-regulatory patterns between BL6 allele and SPR allele.

  • Table S8 Classification of the four scenarios under different cutoffs.

  • Table S9 Splicing patterns of events in “Scenario 3” compared with out-group species.

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Splicing divergence in hybrid mice
Xudong Zou, Bernhard Schaefke, Yisheng Li, Fujian Jia, Wei Sun, Guipeng Li, Weizheng Liang, Tristan Reif, Florian Heyd, Qingsong Gao, Shuye Tian, Yanping Li, Yisen Tang, Liang Fang, Yuhui Hu, Wei Chen
Life Science Alliance Dec 2021, 5 (4) e202101333; DOI: 10.26508/lsa.202101333

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Splicing divergence in hybrid mice
Xudong Zou, Bernhard Schaefke, Yisheng Li, Fujian Jia, Wei Sun, Guipeng Li, Weizheng Liang, Tristan Reif, Florian Heyd, Qingsong Gao, Shuye Tian, Yanping Li, Yisen Tang, Liang Fang, Yuhui Hu, Wei Chen
Life Science Alliance Dec 2021, 5 (4) e202101333; DOI: 10.26508/lsa.202101333
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Volume 5, No. 4
April 2022
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