EDISA: extracting biclusters from multiple time-series of gene expression profiles-2

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>es (equation 14), if the respective value is lower than 0.15 no line is drawn. Table 1 provides an overview of all different module types

EDISA: extracting biclusters from multiple time-series of gene expression profiles-1

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>s of noise. The overlap of the implanted modules and the modules mined by EDISA were scored (equation 15). Six runs with 400 iterations were performed, with = 0.1 and = 0.2 for ∈ [0,0.5], = 0.15 for = 0.7 and = 0.2 for = 0.9

EDISA: extracting biclusters from multiple time-series of gene expression profiles-0

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>Here, we provide three predefined module types. Given this information random samples are drawn from the dataset (preprocessing). EDISA iteratively refines these samples and stores them if they match the module definition. After a specified number of runs EDISA computes the final modules (postprocessing)

EDISA: extracting biclusters from multiple time-series of gene expression profiles-4

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>equation 14). If the respective value is lower than 0.15 no line is drawn. Table 1 provides an overview of all different module types

Phylogenetic Analyses and GAGA-Motif Binding Studies of BBR/BPC Proteins Lend to Clues in GAGA-Motif Recognition and a Regulatory Role in Brassinosteroid Signaling

Plant GAGA-motif binding factors are encoded by the BARLEY B RECOMBINANT / BASIC PENTACYSTEINE (BBR/BPC) family, which fulfill indispensable functions in growth and development. BBR/BPC proteins control flower development, size of the stem cell niche and seed development through transcriptional regulation of homeotic transcription factor genes. They are responsible for the context dependent recruitment of Polycomb repressive complexes (PRC) or other repressive proteins to GAGA-motifs, which are contained in Polycomb repressive DNA-elements (PREs). Hallmark of the protein family is the highly conserved BPC domain, which is required for DNA binding. Here we study the evolution and diversification of the BBR/BPC family and its DNA-binding domain. Our analyses supports a further division of the family into four main groups (I–IV) and several subgroups, to resolve a strict monophyletic descent of the BPC domain. We prove a polyphyletic origin for group III proteins, which evolved from group I and II members through extensive loss of domains in the N-terminus. Conserved motif searches lend to the identification of a WAR/KHGTN consensus and a TIR/K motif at the very C-terminus of the BPC-domain. We could show by DPI-ELISA that this signature is required for DNA-binding in AtBPC1. Additional binding studies with AtBPC1, AtBPC6 and mutated oligonucleotides consolidated the binding to GAGA tetramers. To validate these findings, we used previously published ChIP-seq data from GFP-BPC6. We uncovered that many genes of the brassinosteroid signaling pathway are targeted by AtBPC6. Consistently, bpc6, bpc4 bpc6, and lhp1 bpc4 bpc4 mutants display brassinosteroid-dependent root growth phenotypes. Both, a function in brassinosteroid signaling and our phylogenetic data supports a link between BBR/BPC diversification in the land plant lineage and the complexity of flower and seed plant evolution.</p

TFpredict and SABINE: Sequence-Based Prediction of Structural and Functional Characteristics of Transcription Factors

<div><p>One of the key mechanisms of transcriptional control are the specific connections between transcription factors (TF) and <i>cis</i>-regulatory elements in gene promoters. The elucidation of these specific protein-DNA interactions is crucial to gain insights into the complex regulatory mechanisms and networks underlying the adaptation of organisms to dynamically changing environmental conditions. As experimental techniques for determining TF binding sites are expensive and mostly performed for selected TFs only, accurate computational approaches are needed to analyze transcriptional regulation in eukaryotes on a genome-wide level. We implemented a four-step classification workflow which for a given protein sequence (1) discriminates TFs from other proteins, (2) determines the structural superclass of TFs, (3) identifies the DNA-binding domains of TFs and (4) predicts their <i>cis</i>-acting DNA motif. While existing tools were extended and adapted for performing the latter two prediction steps, the first two steps are based on a novel numeric sequence representation which allows for combining existing knowledge from a BLAST scan with robust machine learning-based classification. By evaluation on a set of experimentally confirmed TFs and non-TFs, we demonstrate that our new protein sequence representation facilitates more reliable identification and structural classification of TFs than previously proposed sequence-derived features. The algorithms underlying our proposed methodology are implemented in the two complementary tools TFpredict and SABINE. The online and stand-alone versions of TFpredict and SABINE are freely available to academics at <a href="http://www.cogsys.cs.uni-tuebingen.de/software/TFpredict/" target="_blank">http://www.cogsys.cs.uni-tuebingen.de/software/TFpredict/</a> and <a href="http://www.cogsys.cs.uni-tuebingen.de/software/SABINE/" target="_blank">http://www.cogsys.cs.uni-tuebingen.de/software/SABINE/</a>.</p></div

Evaluation of classifiers and feature types for superclass prediction.

<p>The classification performance of representative and widely used machine learning methods incorporating different features for superclass prediction was assessed my means of threshold-averaged ROC curves obtained from stratified 4×4-fold nested cross-validation. The differently colored curves correspond to distinct classification methods (see legend). For each classifier the area under the curve (AUC) is denoted. ROC curves were obtained from classifiers incorporating (<b>A</b>) our novel bit score percentile features, (<b>B</b>) <i>k</i>-mer features (<b>C</b>) PSSM profile features (<b>D</b>) functional domain features and (<b>E</b>) pseudo amino acid features.</p

Exhaustive Error-Correcting Output Code for TF superclass prediction.

<p> The table shows the code used for the construction of a 5-class ECOC classifier which integrates the prediction outcomes of 15 binary SVM classifiers. Each column corresponds to a two-class SVM, which treats structural classes assigned to 1 as positives and classes assigned to 0 as negatives. The rows correspond to the 5 superclasses. Each entry (bit) in the table equals to the binary prediction outcome expected from a certain SVM classifier for a query protein of a specific superclass.</p

Calculation of BLAST bit score percentile features.

<p>The protein sequence is aligned to TF and non-TF sequences in a non-redundant sequence database, which does not contain the input sequence itself. Next, the bit scores of all TFs and non-TFs among the BLAST hits are extracted from the BLAST result. The bit score distributions observed for TFs and non-TFs, respectively, are represented based on the minimum <i>p₀</i>, the lower quartile <i>p₂₅</i>, the median <i>p₅₀</i>, the upper quartile <i>p₇₅</i> and the maximum <i>p₁₀₀</i>. The bit score feature representation is then obtained by concatenation of the components calculated for the TF and non-TF class. In addition to binary classification tasks this feature representation is also applicable to multiclass problems, such as the prediction of TF superclasses. For this purpose, the feature vector components capturing the bit score distributions of each superclass were concatenated.</p

Evaluation of classifiers and feature types for TF/non-TF discrimination.

<p>(<b>A</b>) Each of the shown curves corresponds to one of five supervised machine learning methods trained on our novel bit score percentile features, which were employed to distinguish TFs from other proteins. The individual curves obtained for each of the four cross-validation folds were averaged based on the class discrimination cutoffs. Averaged ROC curves were computed in an analogous manner for (<b>B</b>) <i>k</i>-mer features, (<b>C</b>) PSSM profile features, (<b>D</b>) functional domain features and (<b>E</b>) pseudo amino acid features. The sensitivity and specificity achieved by the naive BLAST-based approach correspond to a single point in ROC space marked by an asterisk.</p