Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast
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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/431087", "https://ndownloader.figshare.com/files/431105", "https://ndownloader.figshare.com/files/431131", "https://ndownloader.figshare.com/files/431178", "https://ndownloader.figshare.com/files/431236", "https://ndownloader.figshare.com/files/431308", "https://ndownloader.figshare.com/files/431359", "https://ndownloader.figshare.com/files/431412", "https://ndownloader.figshare.com/files/431456", "https://ndownloader.figshare.com/files/431485", "https://ndownloader.figshare.com/files/431518"], "description"=>"<div><p>DNA sequence polymorphism in a regulatory protein can have a widespread transcriptional effect. Here we present a computational approach for analyzing modules of genes with a common regulation that are affected by specific DNA polymorphisms. We identify such regulatory-linkage modules by integrating genotypic and expression data for individuals in a segregating population with complementary expression data of strains mutated in a variety of regulatory proteins. Our procedure searches simultaneously for groups of co-expressed genes, for their common underlying linkage interval, and for their shared regulatory proteins. We applied the method to a cross between laboratory and wild strains of <em>S. cerevisiae</em>, demonstrating its ability to correctly suggest modules and to outperform extant approaches. Our results suggest that middle sporulation genes are under the control of polymorphism in the sporulation-specific tertiary complex Sum1p/Rfm1p/Hst1p. In another example, our analysis reveals novel inter-relations between Swi3 and two mitochondrial inner membrane proteins underlying variation in a module of aerobic cellular respiration genes. Overall, our findings demonstrate that this approach provides a useful framework for the systematic mapping of quantitative trait loci and their role in gene expression variation.</p></div>", "links"=>[], "tags"=>["transcription", "yeast"], "article_id"=>145079, "categories"=>["Genetics", "Biological Sciences"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.s001", "https://dx.doi.org/10.1371/journal.pgen.1000800.s002", "https://dx.doi.org/10.1371/journal.pgen.1000800.s003", "https://dx.doi.org/10.1371/journal.pgen.1000800.s004", "https://dx.doi.org/10.1371/journal.pgen.1000800.s005", "https://dx.doi.org/10.1371/journal.pgen.1000800.s006", "https://dx.doi.org/10.1371/journal.pgen.1000800.s007", "https://dx.doi.org/10.1371/journal.pgen.1000800.s008", "https://dx.doi.org/10.1371/journal.pgen.1000800.s009", "https://dx.doi.org/10.1371/journal.pgen.1000800.s010", "https://dx.doi.org/10.1371/journal.pgen.1000800.s011"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Understanding_Gene_Sequence_Variation_in_the_Context_of_Transcription_Regulation_in_Yeast/145079", "title"=>"Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-01-08 01:24:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/869502"], "description"=>"<p>(A) The “linkage matrix” contains eQTL likelihood scores between particular genetic markers (columns) and target genes (rows). The compendium of regulatory signatures consists of gene expression arrays from strains mutated in a variety of regulatory proteins. First, we aim to detect groups of genes that are co-expressed in one signature and co-linked to a specific genetic marker. Given a regulatory signature j and genetic marker i, we ask to what extent genes with high linkage to i manifest a distinct distribution in signature j. The difference between the distributions is estimated, producing a P-value called “ReL score.” The “ReL matrix” represents these scores across all regulatory signatures (columns) and genetic markers (rows). Blue/white indicates significant/non-significant ReL scores. Next, we aim to detect a group of genes that are co-expressed in a few signatures and co-linked to a common linkage interval. To that end, we look for high-scoring sub-matrices in the ReL matrix, referred as “ReL modules.” (B) Each ReL module represents a triplet: (i) a range of genetic markers (linkage interval) that contains a genetic causal regulator, (ii) a set of regulatory signatures matching particular regulatory proteins, and (iii) a group of target genes that are co-regulated by the module's regulatory proteins and co-linked to the module's causal regulator.</p>", "links"=>[], "tags"=>["rel"], "article_id"=>539961, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_ReL_analysis_procedure_/539961", "title"=>"Overview of the ReL analysis procedure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-01-08 02:46:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/869630"], "description"=>"<p>A matrix of genetic markers (rows) versus regulatory signatures (columns), where the colors indicate the ReL score. A blue (white) entry indicates significant (non-significant) ReL score. Shown are only columns and rows that are part of at least one ReL module as well as five additional flanking genetic markers on the boundaries of the module's linkage interval. Genetic markers are displayed according to their genomic order, and the modules' linkage intervals are shown as black vertical lines. Columns are grouped according to the biclustering solution whereas columns that are shared among multiple modules are assigned to one of their modules arbitrarily. The visualization highlights the existence of high intensity sub-matrices (modules) within the ReL matrix. The number of target genes in each module is shown as gray bars on the left.</p>", "links"=>[], "tags"=>["rel"], "article_id"=>540092, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_ReL_matrix_/540092", "title"=>"The ReL matrix.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-01-08 00:01:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/869714"], "description"=>"<p>(A) The components: the modules' regulatory proteins (in ovals), its putative causal regulator (diamond-shaped), and eight meiosis-/sporulation-specific target genes (in rectangles). Protein–protein interactions between the components are indicated as dashed lines (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800.s007\" target=\"_blank\">Text S2</a>). (B) Gene expression patterns (y-axis) of the eight meiosis-/sporulation-specific target genes through the sporulation time course (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800-Primig1\" target=\"_blank\">[30]</a>; x-axis). The plot shows a temporal induction of the target genes during mid-sporulation. (C) The linkage plot. The eQTL likelihood (y-axis) is plotted for each genetic marker position along the module's linkage interval and its flanking regions (x-axis). Rfm1, a subunit of the Sum1p/Rfm1p/Hst1p complex that controls mid-sporulation genes, is located within the eQTL likelihood peak (black box). (D) Expression levels of parents and segregants for a marker in <i>SNR31</i>, which lies near <i>RFM1</i>. The plot shows average expression levels over the target genes for six replicates of each parent and for segregants that inherited the marker from BY and RM. The effect of the locus is in the same direction as the difference between the parents.</p>", "links"=>[], "tags"=>["sporulation", "module"], "article_id"=>540172, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_sporulation_module_13_/540172", "title"=>"The sporulation module (#13).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-01-08 00:02:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/869775"], "description"=>"<p>(A) The linkage plot. The eQTL likelihood (y-axis) is plotted at each genetic marker (x-axis) residing within or near the linkage interval of modules #12 and #5. The modules' linkage intervals are highlighted in gray. The putative causal regulators <i>CAT5</i> and <i>CRD1</i> (black boxes), which play a role in respiration, are located within the eQTL likelihood peaks. (B) Expression levels of parents and segregants for a marker in <i>CCT4</i> and <i>RGA1</i>, which lie near <i>CRD1</i> (top) and <i>CAT5</i> (bottom). The plots are shown as in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen-1000800-g003\" target=\"_blank\">Figure 3D</a>. In both cases, the BY parents and segregants carrying the BY allele have higher expression levels than the RM parents and segregants. (C) Distribution of expression values for the target genes of modules #12 (red) and #5 (green), compared with the rest of the genes (black) in <i>swi3</i> knockout experiment. Shown are log2-transformed <i>swi3</i> vs. wild-type expression ratios <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800-Hu1\" target=\"_blank\">[16]</a>.</p>", "links"=>[], "tags"=>["respiration", "modules"], "article_id"=>540231, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_respiration_modules_12_and_5_/540231", "title"=>"The respiration modules (#12 and #5).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-01-08 00:03:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/869837"], "description"=>"<p>(A) A scatter plot showing the linkage (eQTL likelihood score) to <i>CAT5</i>, module #12's putative causal regulator (x axis), and to <i>CRD1</i>, module #5's putative causal regulator (y axis), across all genes. Genes that are tightly linked (eQTL likelihood >2.5) to one of the causal regulators are indicated by the red and green boxes. (B) An illustration of mitochondrial respiratory-related reactions and complexes. Shown are the electron transport chain (complexes I, II, III, and IV), the ATP synthase complex, phosphate and ATP/ADP carriers, and the TCA cycle. Proteins that are part of these complexes, whose gene expression is also linked to the putative causal regulators of module #5 only, #12 only, or both, are denoted by red, green and black dots, respectively. (C) Expression levels of parents and segregants for every combination of module #5 and module #12 putative causal regulator alleles. RM<sup>12</sup>-RM<sup>5</sup> and BY<sup>12</sup>-BY<sup>5</sup> indicates that both alleles are from the RM and BY parents, respectively. RM<sup>12</sup>-BY<sup>5</sup> (BY<sup>12</sup>-RM<sup>5</sup>, respectively) indicates that only module #12's allele is from the RM (BY, respectively) strain, whereas module #5's allele is from the other parental strain. The expression levels are averaged over the twelve overlapping target genes depicted in (A). The plot indicates an additive effect of module #5's and module #12's linkage intervals. In plots (A–C), we used the genetic markers residing within <i>CCT4</i> and <i>RGA1</i>, which are located proximal to the <i>CRD1</i> and <i>CAT5</i> genes, respectively.</p>", "links"=>[], "tags"=>["interactions", "respiration"], "article_id"=>540295, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genetic_interactions_between_the_respiration_modules_/540295", "title"=>"Genetic interactions between the respiration modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-01-08 00:04:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/869911"], "description"=>"<p>A list of all modules with ReL score >3. The columns in the table (left to right) are as follows: module number, the module's ReL score, linkage interval, number of regulatory signatures, number of target genes, the primary biological process of the module (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800.s004\" target=\"_blank\">Table S3</a>), the best-scoring regulatory protein of the module, and the predicted causal regulator of the module (<a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800.s007\" target=\"_blank\">Text S2</a>). A comprehensive description of these ReL modules is given in <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000800#pgen.1000800.s002\" target=\"_blank\">Table S1</a>.</p>", "links"=>[], "tags"=>["rel"], "article_id"=>540366, "categories"=>["Genetics", "Computational Biology"], "users"=>["Irit Gat-Viks", "Renana Meller", "Martin Kupiec", "Ron Shamir"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000800.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_ReL_modules_/540366", "title"=>"Summary of ReL modules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-01-08 00:06:06"}

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