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The cancer syndrome polymerase proofreading-associated polyposis results from germline mutations in the POLE and POLD1 genes. Mutations in the exonuclease domain of these genes are associated with hyper- and ultra-mutated tumors with a predominance of base substitutions resulting from faulty proofreading during DNA replication. When a new variant is identified by gene testing of POLE and POLD1, it is important to verify whether the variant is associated with PPAP or not, to guide genetic counseling of mutation carriers. In 2015, we reported the likely pathogenic (class 4) germline POLE c.1373A > T p.(Tyr458Phe) variant and we have now characterized this variant to verify that it is a class 5 pathogenic variant. For this purpose, we investigated (1) mutator phenotype in tumors from two carriers, (2) mutation frequency in cell-based mutagenesis assays, and (3) structural consequences based on protein modeling. Whole-exome sequencing of two tumors identified an ultra-mutator phenotype with a predominance of base substitutions, the majority of which are C > T. A SupF mutagenesis assay revealed increased mutation frequency in cells overexpressing the variant of interest as well as in isogenic cells encoding the variant. Moreover, exonuclease repair yeast-based assay supported defect in proofreading activity. Lastly, we present a homology model of human POLE to demonstrate structural consequences leading to pathogenic impact of the p.(Tyr458Phe) mutation. The three lines of evidence, taken together with updated co-segregation and previously published data, allow the germline variant POLE c.1373A > T p.(Tyr458Phe) to be reclassified as a class 5 variant. That means the variant is associated with PPAP.

Background Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications. Results A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT). Conclusions The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms—from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis. © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Background Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression. Results We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes. Conclusion This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Immediate-early genes (IEGs) can be activated and transcribed within minutes after stimulation, without the need for de novo protein synthesis, and they are stimulated in response to both cell-extrinsic and cell-intrinsic signals. Extracellular signals are transduced from the cell surface, through receptors activating a chain of proteins in the cell, in particular extracellular-signal-regulated kinases (ERKs), mitogen-activated protein kinases (MAPKs) and members of the RhoA-actin pathway. These communicate through a signaling cascade by adding phosphate groups to neighboring proteins, and this will eventually activate and translocate TFs to the nucleus and thereby induce gene expression. The gene activation also involves proximal and distal enhancers that interact with promoters to simulate gene expression. The immediate-early genes have essential biological roles, in particular in stress response, like the immune system, and in differentiation. Therefore they also have important roles in various diseases, including cancer development. In this paper we summarize some recent advances on key aspects of the activation and regulation of immediate-early genes.

Cytoscape is often used for visualization and analysis of metabolic pathways. For example, based on KEGG data, a reader for KEGG Markup Language (KGML) is used to load files into Cytoscape. However, although multiple genes can be responsible for the same reaction, the KGML-reader KEGGScape only presents the first listed gene in a network node for a given reaction. This can lead to incorrect interpretations of the pathways. Our new method, FunHoP, shows all possible genes in each node, making the pathways more complete. FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq. Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads, and weighting the reads on gene length and ratio, a new expression value is calculated for the node as a whole. Differential expression at node level is then applied to the networks. Using prostate cancer as model, we integrate RNA-seq data from two patient cohorts with metabolism data from literature. Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically. Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.

Maintenance of a genome requires DNA repair integrated with chromatin remodeling. We have analyzed six transcriptome data sets and one data set on translational regulation of known DNA repair and remodeling genes in synchronized human cells. These data are available through our new database: www.dnarepairgenes.com. Genes that have similar transcription profiles in at least two of our data sets generally agree well with known protein profiles. In brief, long patch base excision repair (BER) is enriched for S phase genes, whereas short patch BER uses genes essentially equally expressed in all cell cycle phases. Furthermore, most genes related to DNA mismatch repair, Fanconi anemia and homologous recombination have their highest expression in the S phase. In contrast, genes specific for direct repair, nucleotide excision repair, as well as non-homologous end joining do not show cell cycle-related expression. Cell cycle regulated chromatin remodeling genes were most frequently confined to G1/S and S. These include e.g. genes for chromatin assembly factor 1 (CAF-1) major subunits CHAF1A and CHAF1B; the putative helicases HELLS and ATAD2 that both co-activate E2F transcription factors central in G1/S-transition and recruit DNA repair and chromatin-modifying proteins and DNA double strand break repair proteins; and RAD54L and RAD54B involved in double strand break repair. TOP2A was consistently most highly expressed in G2, but also expressed in late S phase, supporting a role in regulating entry into mitosis. Translational regulation complements transcriptional regulation and appears to be a relatively common cell cycle regulatory mechanism for DNA repair genes. Our results identify cell cycle phases in which different pathways have highest activity, and demonstrate that periodically expressed genes in a pathway are frequently co-expressed. Furthermore, the data suggest that S phase expression and over-expression of some multifunctional chromatin remodeling proteins may set up feedback loops driving cancer cell proliferation.

Background Transcription factors are key proteins in the regulation of gene transcription. An important step in this process is the opening of chromatin in order to make genomic regions available for transcription. Data on DNase I hypersensitivity has previously been used to label a subset of transcription factors as Pioneers, Settlers and Migrants to describe their potential role in this process. These labels represent an interesting hypothesis on gene regulation and possibly a useful approach for data analysis, and therefore we wanted to expand the set of labeled transcription factors to include as many known factors as possible. We have used a well-annotated dataset of 1175 transcription factors as input to supervised machine learning methods, using the subset with previously assigned labels as training set. We then used the final classifier to label the additional transcription factors according to their potential role as Pioneers, Settlers and Migrants. The full set of labeled transcription factors was used to investigate associated properties and functions of each class, including an analysis of interaction data for transcription factors based on DNA co-binding and protein-protein interactions. We also used the assigned labels to analyze a previously published set of gene lists associated with a time course experiment on cell differentiation. Results The analysis showed that the classification of transcription factors with respect to their potential role in chromatin opening largely was determined by how they bind to DNA. Each subclass of transcription factors was enriched for properties that seemed to characterize the subclass relative to its role in gene regulation, with very general functions for Pioneers, whereas Migrants to a larger extent were associated with specific processes. Further analysis showed that the expanded classification is a useful resource for analyzing other datasets on transcription factors with respect to their potential role in gene regulation. The analysis of transcription factor interaction data showed complementary differences between the subclasses, where transcription factors labeled as Pioneers often interact with other transcription factors through DNA co-binding, whereas Migrants to a larger extent use protein-protein interactions. The analysis of time course data on cell differentiation indicated a shift in the regulatory program associated with Pioneer-like transcription factors during differentiation. Conclusions The expanded classification is an interesting resource for analyzing data on gene regulation, as illustrated here on transcription factor interaction data and data from a time course experiment. The potential regulatory function of transcription factors seems largely to be determined by how they bind DNA, but is also influenced by how they interact with each other through cooperativity and protein-protein interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1349-2) contains supplementary material, which is available to authorized users.

Background The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both. Results Here we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests. Conclusions The TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Alginates are commercially valuable and complex polysaccharides composed of varying amounts and distribution patterns of 1-4-linked β-D-mannuronic acid (M) and α-L-guluronic acid (G). This structural variability strongly affects polymer physicochemical properties and thereby both commercial applications and biological functions. One promising approach to alginate fine structure elucidation involves the use of alginate lyases, which degrade the polysaccharide by cleaving the glycosidic linkages through a β-elimination reaction. For such studies one would ideally like to have different lyases, each of which cleaves only one of the four possible linkages in alginates: G-G, G-M, M-G, and M-M. So far no lyase specific for only G-G linkages has been described, and here we report the construction of such an enzyme by mutating the gene encoding Klebsiella pneumoniae lyase AlyA (a polysaccharide lyase family 7 lyase), which cleaves both G-G and G-M linkages. After error-prone PCR mutagenesis and high throughput screening of ∼7000 lyase mutants, enzyme variants with a strongly improved G-G specificity were identified. Furthermore, in the absence of Ca(2+), one of these lyases (AlyA5) was found to display no detectable activity against G-M linkages. G-G linkages were cleaved with ∼10% of the optimal activity under the same conditions. The substitutions conferring altered specificity to the mutant enzymes are located in conserved regions in the polysaccharide lyase family 7 alginate lyases. Structure-function analyses by comparison with the known three-dimensional structure of Sphingomonas sp. A1 lyase A1-II' suggests that the improved G-G specificity might be caused by increased affinity for nonproductive binding of the alternating G-M structure.

Interferon regulatory factors (IRFs) are crucial for transcription during innate immune responses. We have previously shown that the tyrosine kinase c-Src enhances IRF-3-dependent transcription in response to viral double-stranded RNA. In this study, we show that c-Src has distinct roles in Toll-like receptor (TLR)-mediated activation of IRF-5 and IRF-3. Surprisingly, c-Src inhibition markedly enhanced IRF-5 activation after treatment with unmethylated CpG, while suppressing IRF-3 activation. Also, CpG-elicited interleukin-6 mRNA production was increased, whereas IP10 mRNA synthesis was reduced in cells deficient in c-Src. Interestingly, c-Src regulated TLR-stimulated induction of activating transcription factor 3 (ATF3), a transcriptional repressor. Depletion of ATF3 by small interfering RNA markedly enhanced interleukin-6 production after CpG treatment, whereas IP10 production was reduced. These results demonstrate functional specificity for c-Src in TLR-stimulated responses and suggest that c-Src modulation and ATF3 activity may contribute to differential regulation of IRF-3- versus IRF-5-mediated gene expression.

Shiga toxin-producing Escherichia coli (STEC) strains are a common cause of both sporadic infection and outbreaks of enteric disease in humans. Here, we present draft genome sequences of 11 STEC strains of different serotypes (O145, O121, O26, O177, and O-type unknown), that have been isolated from patients with enteric disease of various degrees of severity, in the years 2001 to 2014 at St. Olavs Hospital in Trondheim, Norway.

Alginates are industrially important, linear copolymers of beta-d-mannuronic acid (M) and its C-5-epimer alpha-l-guluronic acid (G). The G residues originate from a postpolymerization reaction catalyzed by mannuronan C-5-epimerases (MEs), leading to extensive variability in M/G ratios and distribution patterns. Alginates containing long continuous stretches of G residues (G blocks) can form strong gels, a polymer type not found in alginate-producing bacteria belonging to the genus Pseudomonas. Here we show that the Pseudomonas syringae genome encodes a Ca(2+)-dependent ME (PsmE) that efficiently forms such G blocks in vitro. The deduced PsmE protein consists of 1610 amino acids and is a modular enzyme related to the previously characterized family of Azotobacter vinelandii ME (AlgE1-7). A- and R-like modules with sequence similarity to those in the AlgE enzymes are found in PsmE, and the A module of PsmE (PsmEA) was found to be sufficient for epimerization. Interestingly, an R module from AlgE4 stimulated Ps-mEA activity. PsmE contains two regions designated M and RTX, both presumably involved in the binding of Ca(2+). Bacterial alginates are partly acetylated, and such modified residues cannot be epimerized. Based on a detailed computer-assisted analysis and experimental studies another PsmE region, designated N, was found to encode an acetylhydrolase. By the combined action of N and A PsmE was capable of redesigning an extensively acetylated alginate low in G from a non gel-forming to a gel-forming state. Such a property has to our knowledge not been previously reported for an enzyme acting on a polysaccharide.

Shiga toxin-producing Escherichia coli (STEC) cause infections in humans ranging from asymptomatic carriage to bloody diarrhoea and haemolytic uremic syndrome (HUS). Here we present whole genome comparison of Norwegian non-O157 STEC strains with the aim to distinguish between strains with the potential to cause HUS and less virulent strains. Whole genome sequencing and comparisons were performed across 95 non-O157 STEC strains. Twenty-three of these were classified as HUS-associated, including strains from patients with HUS (n = 19) and persons with an epidemiological link to a HUS-case (n = 4). Genomic comparison revealed considerable heterogeneity in gene content across the 95 STEC strains. A clear difference in gene profile was observed between strains with and without the Locus of Enterocyte Effacement (LEE) pathogenicity island. Phylogenetic analysis of the core genome showed high degree of diversity among the STEC strains, but all HUS-associated STEC strains were distributed in two distinct clusters within phylogroup B1. However, non-HUS strains were also found in these clusters. A number of accessory genes were found to be significantly overrepresented among HUS-associated STEC, but none of them were unique to this group of strains, suggesting that different sets of genes may contribute to the pathogenic potential in different phylogenetic STEC lineages. In this study we were not able to clearly distinguish between HUS-associated and non-HUS non-O157 STEC by extensive genome comparisons. Our results indicate that STECs from different phylogenetic backgrounds have independently acquired virulence genes that determine pathogenic potential, and that the content of such genes is overlapping between HUS-associated and non-HUS strains. This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

MOTIVATION: Hydrophobic or non-polar contacts in proteins are important for protein folding, protein stability and protein-protein interactions. In particular, in the interior of a protein, in the hydrophobic core, a large number of such contacts are found. The residues involved in these contacts often form a tightly packed cluster of atoms. It is useful for the understanding of protein structure to be able to identify and analyse such clusters. RESULTS: Tools for hierarchical cluster analysis of non-polar contacts in proteins are described. These tools allow for efficient identification of clusters of non-polar interactions in proteins, both internal clusters and clusters involved in protein-protein contacts. The non-polar contacts are represented by a dendrogram structure, which is a simple approach for flexible identification of clusters by visual inspection. The tools are demonstrated on the structure of crambin, the structure of the complex between human growth hormone and the human growth hormone binding protein, and a pair of lipase/esterase structures. AVAILABILITY: On request from the author.

Bacitracin is a dodecapeptide antibiotic produced by Bacillus sp. The antibacterial activity depends upon the peptide binding a divalent metal. Hitherto, the exact coordination of the cation has not been established. In particular the role played by the sulphur and nitrogen atoms of the thiazoline ring of bacitracin A has not been clear. Here the coordination of Zn2+ by bacitracin A has been studied using extended X-ray absorption fine structure. The experimental data are consistent with a model in which zinc is coordinated by one oxygen and three nitrogen atoms with the sulphur atom of the thiazoline ring not being directly involved in the zinc coordination.

MOTIVATION: The search for repeated patterns in DNA and protein sequences is important in sequence analysis. The rapid increase in available sequences, in particular from large-scale genome sequencing projects, makes it relevant to develop sensitive automatic methods for the identification of repeats. RESULTS: A new method for finding periodic patterns in biological sequences is presented. The method is based on evolutionary distance and 'phase shifts' corresponding to insertions and deletions. A given sequence is aligned to itself in a certain sense, trying to minimize a distance to periodicity. Relationships between different such periodicity measures are discussed. An iterative algorithm is used, and the running time is nearly proportional to the sequence length. The alignment produces a periodic consensus pattern. A 'phase score' is used to indicate a statistical significance of the periodicity. Three examples using both DNA and protein sequences illustrate how the method can be used to find patterns. AVAILABILITY: On request from the authors. CONTACT: evindc@mat nu.no; finn.drablos@unimed.sintef.no

Background Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen as peaks in enrichment profiles when the sequencing reads are mapped to a reference genome. However, the profiles are normally noisy, making it challenging to identify all significantly enriched regions in a reliable way and with an acceptable false discovery rate. Results We present the Triform algorithm, an improved approach to automatic peak finding in ChIP-Seq enrichment profiles for transcription factors. The method uses model-free statistics to identify peak-like distributions of sequencing reads, taking advantage of improved peak definition in combination with known characteristics of ChIP-Seq data. Conclusions Triform outperforms several existing methods in the identification of representative peak profiles in curated benchmark data sets. We also show that Triform in many cases is able to identify peaks that are more consistent with biological function, compared with other methods. Finally, we show that Triform can be used to generate novel information on transcription factor binding in repeat regions, which represents a particular challenge in many ChIP-Seq experiments. The Triform algorithm has been implemented in R, and is available via http://tare.medisin.ntnu.no/triform.

Summary: Computational methods designed to discover transcription factor binding sites in DNA sequences often have a tendency to make a lot of false predictions. One way to improve accuracy in motif discovery is to rely on positional priors to focus the search to parts of a sequence that are considered more likely to contain functional binding sites. We present here a program called PriorsEditor that can be used to create such positional priors tracks based on a combination of several features, including phylogenetic conservation, nucleosome occupancy, histone modifications, physical properties of the DNA helix and many more. Availability: PriorsEditor is available as a web start application and downloadable archive from http://tare.medisin.ntnu.no/priorseditor (requires Java 1.6). The web site also provides tutorials, screenshots and example protocol scripts. Contact: kjetil.klepper@ntnu.no

AlkB PCNA-interacting motif (APIM) is present in >200 proteins and may mediate PCNA binding during genotoxic stress., Numerous proteins, many essential for the DNA replication machinery, interact with proliferating cell nuclear antigen (PCNA) through the PCNA-interacting peptide (PIP) sequence called the PIP box. We have previously shown that the oxidative demethylase human AlkB homologue 2 (hABH2) colocalizes with PCNA in replication foci. In this study, we show that hABH2 interacts with a posttranslationally modified PCNA via a novel PCNA-interacting motif, which we term AlkB homologue 2 PCNA-interacting motif (APIM). We identify APIM in >200 other proteins involved in DNA maintenance, transcription, and cell cycle regulation, and verify a functional APIM in five of these. Expression of an APIM peptide increases the cellular sensitivity to several cytostatic agents not accounted for by perturbing only the hABH2–PCNA interaction. Thus, APIM is likely to mediate PCNA binding in many proteins involved in DNA repair and cell cycle control during genotoxic stress.

Background: Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery – discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cisregulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery. Results: We have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise. Conclusion: Although some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery. © 2008 Klepper et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research. Results We use a machine learning perspective to analyze collections of transcription factors with known binding sites. Algorithms are presented for finding position weight matrices (PWMs), IUPAC-type motifs and mismatch motifs with optimal discrimination of binding sites from remaining sequence. We show that for many data sets in a recently proposed benchmark suite for motif discovery, none of the common motif models can accurately discriminate the binding sites from remaining sequence. This may obscure the distinction between the potential performance of the motif discovery tool itself versus the intrinsic complexity of the problem we are trying to solve. Synthetic data sets may avoid this problem, but we show on some previously proposed benchmarks that there may be a strong bias towards a presupposed motif model. We also propose a new approach to benchmark data set construction. This approach is based on collections of binding site fragments that are ranked according to the optimal level of discrimination achieved with our algorithms. This allows us to select subsets with specific properties. We present one benchmark suite with data sets that allow good discrimination between positive and negative instances with the common motif models. These data sets are suitable for evaluating algorithms for motif discovery that rely on these models. We present another benchmark suite where PWM, IUPAC and mismatch motif models are not able to discriminate reliably between positive and negative instances. This suite could be used for evaluating more powerful motif models. Conclusion Our improved benchmark suites have been designed to differentiate between the performance of motif discovery algorithms and the power of motif models. We provide a web server where users can download our benchmark suites, submit predictions and visualize scores on the benchmarks. © 2007 Sandve et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methylating agents are ubiquitous in the environment, and central in cancer therapy. The 1-methyladenine and 3-methylcytosine lesions in DNA/RNA contribute to the cytotoxicity of such agents. These lesions are directly reversed by ABH3 (hABH3) in humans and AlkB in Escherichia coli. Here, we report the structure of the hABH3 catalytic core in complex with iron and 2-oxoglutarate (2OG) at 1.5 A resolution and analyse key site-directed mutants. The hABH3 structure reveals the beta-strand jelly-roll fold that coordinates a catalytically active iron centre by a conserved His1-X-Asp/Glu-X(n)-His2 motif. This experimentally establishes hABH3 as a structural member of the Fe(II)/2OG-dependent dioxygenase superfamily, which couples substrate oxidation to conversion of 2OG into succinate and CO2. A positively charged DNA/RNA binding groove indicates a distinct nucleic acid binding conformation different from that predicted in the AlkB structure with three nucleotides. These results uncover previously unassigned key catalytic residues, identify a flexible hairpin involved in nucleotide flipping and ss/ds-DNA discrimination, and reveal self-hydroxylation of an active site leucine that may protect against uncoupled generation of dangerous oxygen radicals.

Alkylation lesions in DNA and RNA result from endogenous compounds, environmental agents and alkylating drugs. Simple methylating agents, e.g. methylnitrosourea, tobacco-specific nitrosamines and drugs like temozolomide or streptozotocin, form adducts at N- and O-atoms in DNA bases. These lesions are mainly repaired by direct base repair, base excision repair, and to some extent by nucleotide excision repair (NER). The identified carcinogenicity of O(6)-methylguanine (O(6)-meG) is largely caused by its miscoding properties. Mutations from this lesion are prevented by O(6)-alkylG-DNA alkyltransferase (MGMT or AGT) that repairs the base in one step. However, the genotoxicity and cytotoxicity of O(6)-meG is mainly due to recognition of O(6)-meG/T (or C) mispairs by the mismatch repair system (MMR) and induction of futile repair cycles, eventually resulting in cytotoxic double-strand breaks. Therefore, inactivation of the MMR system in an AGT-defective background causes resistance to the killing effects of O(6)-alkylating agents, but not to the mutagenic effect. Bifunctional alkylating agents, such as chlorambucil or carmustine (BCNU), are commonly used anti-cancer drugs. DNA lesions caused by these agents are complex and require complex repair mechanisms. Thus, primary chloroethyl adducts at O(6)-G are repaired by AGT, while the secondary highly cytotoxic interstrand cross-links (ICLs) require nucleotide excision repair factors (e.g. XPF-ERCC1) for incision and homologous recombination to complete repair. Recently, Escherichia coli protein AlkB and human homologues were shown to be oxidative demethylases that repair cytotoxic 1-methyladenine (1-meA) and 3-methylcytosine (3-meC) residues. Numerous AlkB homologues are found in viruses, bacteria and eukaryotes, including eight human homologues (hABH1-8). These have distinct locations in subcellular compartments and their functions are only starting to become understood. Surprisingly, AlkB and hABH3 also repair RNA. An evaluation of the biological effects of environmental mutagens, as well as understanding the mechanism of action and resistance to alkylating drugs require a detailed understanding of DNA repair processes.

Uracil in DNA results from deamination of cytosine, resulting in mutagenic U : G mispairs, and misincorporation of dUMP, which gives a less harmful U : A pair. At least four different human DNA glycosylases may remove uracil and thus generate an abasic site, which is itself cytotoxic and potentially mutagenic. These enzymes are UNG, SMUG1, TDG and MBD4. The base excision repair process is completed either by a short patch- or long patch pathway, which largely use different proteins. UNG2 is a major nuclear uracil-DNA glycosylase central in removal of misincorporated dUMP in replication foci, but recent evidence also indicates an important role in repair of U : G mispairs and possibly U in single-stranded DNA. SMUG1 has broader specificity than UNG2 and may serve as a relatively efficient backup for UNG in repair of U : G mismatches and single-stranded DNA. TDG and MBD4 may have specialized roles in the repair of U and T in mismatches in CpG contexts. Recently, a role for UNG2, together with activation induced deaminase (AID) which generates uracil, has been demonstrated in immunoglobulin diversification. Studies are now underway to examine whether mice deficient in Ung develop lymphoproliferative malignancies and have a different life span.

Background: Traditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming. Results: Here we present MotifLab, a general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab supports comprehensive motif discovery and analysis by allowing users to integrate several popular motif discovery tools as well as different kinds of additional information, including phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, transcription factor interactions and gene expression. MotifLab offers several data-processing operations that can be used to create, manipulate and analyse data objects, and complete analysis workflows can be constructed and automatically executed within MotifLab, including graphical presentation of the results. Conclusions: We have developed MotifLab as a flexible workbench for motif analysis in a genomic context. The flexibility and effectiveness of this workbench has been demonstrated on selected test cases, in particular two previously published benchmark data sets for single motifs and modules, and a realistic example of genes responding to treatment with forskolin. MotifLab is freely available at http://www.motiflab.org. © 2013 Klepper and Drabløs; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Computational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regulatory sites in DNA. In recent years there has been increased attention towards the discovery of composite motifs, typically occurring in cis-regulatory regions of genes. Results: This paper describes Compo: a discrete approach to composite motif discovery that supports richer modeling of composite motifs and a more realistic background model compared to previous methods. Furthermore, multiple parameter and threshold settings are tested automatically, and the most interesting motifs across settings are selected. This avoids reliance on single hard thresholds, which has been a weakness of previous discrete methods. Comparison of motifs across parameter settings is made possible by the use of p-values as a general significance measure. Compo can either return an ordered list of motifs, ranked according to the general significance measure, or a Pareto front corresponding to a multi-objective evaluation on sensitivity, specificity and spatial clustering. Conclusion: Compo performs very competitively compared to several existing methods on a collection of benchmark data sets. These benchmarks include a recently published, large benchmark suite where the use of support across sequences allows Compo to correctly identify binding sites even when the relevant PWMs are mixed with a large number of noise PWMs. Furthermore, the possibility of parameter-free running offers high usability, the support for multiobjective evaluation allows a rich view of potential regulators, and the discrete model allows flexibility in modeling and interpretation of motifs. © 2008 Sandve et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.