Comprehensive Mapping of Histone Modifications at DNA Double-Strand Breaks Deciphers Repair Pathway Chromatin Signatures.

Double-strand breaks (DSBs) are extremely detrimental DNA lesions that can lead to cancer-driving mutations and translocations. Non-homologous end joining (NHEJ) and homologous recombination (HR) represent the two main repair pathways operating in the context of chromatin to ensure genome stability. Despite extensive efforts, our knowledge of DSB-induced chromatin still remains fragmented. Here, we describe the distribution of 20 chromatin features at multiple DSBs spread throughout the human genome using ChIP-seq. We provide the most comprehensive picture of the chromatin landscape set up at DSBs and identify NHEJ- and HR-specific chromatin events. This study revealed the existence of a DSB-induced monoubiquitination-to-acetylation switch on histone H2B lysine 120, likely mediated by the SAGA complex, as well as higher-order signaling at HR-repaired DSBs whereby histone H1 is evicted while ubiquitin and 53BP1 accumulate over the entire γH2AX domains.

Getting insight into the pan-genome structure with PangTree.

BACKGROUND: The term pan-genome was proposed to denominate collections of genomic sequences jointly analyzed or used as a reference. The constant growth of genomic data intensifies development of data structures and algorithms to investigate pan-genomes efficiently. RESULTS: This work focuses on providing a tool for discovering and visualizing the relationships between the sequences constituting a pan-genome. A new structure to represent such relationships - called affinity tree - is proposed. Each node of this tree has assigned a subset of genomes, as well as their homogeneity level and averaged consensus sequence. Moreover, subsets assigned to sibling nodes form a partition of the genomes assigned to their parent. CONCLUSIONS: Functionality of affinity tree is demonstrated on simulated data and on the Ebola virus pan-genome. Furthermore, two software packages are provided: PangTreeBuild constructs affinity tree, while PangTreeVis presents its result.

Defective XRN3-mediated transcription termination in Arabidopsis affects the expression of protein-coding genes.

Arabidopsis thaliana contains two nuclear XRN2/3 5'-3' exonucleases that are homologs of yeast and human Rat1/Xrn2 proteins involved in the processing and degradation of several classes of nuclear RNAs and in transcription termination of RNA polymerase II. Using strand-specific short read sequencing we show that knockdown of XRN3 leads to an altered expression of hundreds of genes and the accumulation of uncapped and polyadenylated read-through transcripts generated by inefficiently terminated Pol II. Our data support the notion that XRN3-mediated changes in the expression of a subset of genes are caused by upstream read-through transcription and these effects are enhanced by RNA-mRNA chimeras generated in xrn3 plants. In turn, read-through transcripts that are antisense to downstream genes may trigger production of siRNA. Our results highlight the importance of XRN3 exoribonuclease in Pol II transcription termination in plants and show that disturbance in this process may significantly alter gene expression.

Ssb1 and Ssb2 cooperate to regulate mouse hematopoietic stem and progenitor cells by resolving replicative stress.

Hematopoietic stem and progenitor cells (HSPCs) are vulnerable to endogenous damage and defects in DNA repair can limit their function. The 2 single-stranded DNA (ssDNA) binding proteins SSB1 and SSB2 are crucial regulators of the DNA damage response; however, their overlapping roles during normal physiology are incompletely understood. We generated mice in which both Ssb1 and Ssb2 were constitutively or conditionally deleted. Constitutive Ssb1/Ssb2 double knockout (DKO) caused early embryonic lethality, whereas conditional Ssb1/Ssb2 double knockout (cDKO) in adult mice resulted in acute lethality due to bone marrow failure and intestinal atrophy featuring stem and progenitor cell depletion, a phenotype unexpected from the previously reported single knockout models of Ssb1 or Ssb2 Mechanistically, cDKO HSPCs showed altered replication fork dynamics, massive accumulation of DNA damage, genome-wide double-strand breaks enriched at Ssb-binding regions and CpG islands, together with the accumulation of R-loops and cytosolic ssDNA. Transcriptional profiling of cDKO HSPCs revealed the activation of p53 and interferon (IFN) pathways, which enforced cell cycling in quiescent HSPCs, resulting in their apoptotic death. The rapid cell death phenotype was reproducible in in vitro cultured cDKO-hematopoietic stem cells, which were significantly rescued by nucleotide supplementation or after depletion of p53. Collectively, Ssb1 and Ssb2 control crucial aspects of HSPC function, including proliferation and survival in vivo by resolving replicative stress to maintain genomic stability.

Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing.

We present a genome-wide approach to map DNA double-strand breaks (DSBs) at nucleotide resolution by a method we termed BLESS (direct in situ breaks labeling, enrichment on streptavidin and next-generation sequencing). We validated and tested BLESS using human and mouse cells and different DSBs-inducing agents and sequencing platforms. BLESS was able to detect telomere ends, Sce endonuclease-induced DSBs and complex genome-wide DSB landscapes. As a proof of principle, we characterized the genomic landscape of sensitivity to replication stress in human cells, and we identified >2,000 nonuniformly distributed aphidicolin-sensitive regions (ASRs) overrepresented in genes and enriched in satellite repeats. ASRs were also enriched in regions rearranged in human cancers, with many cancer-associated genes exhibiting high sensitivity to replication stress. Our method is suitable for genome-wide mapping of DSBs in various cells and experimental conditions, with a specificity and resolution unachievable by current techniques.

Optimally choosing PWM motif databases and sequence scanning approaches based on ChIP-seq data.

BACKGROUND: For many years now, binding preferences of Transcription Factors have been described by so called motifs, usually mathematically defined by position weight matrices or similar models, for the purpose of predicting potential binding sites. However, despite the availability of thousands of motif models in public and commercial databases, a researcher who wants to use them is left with many competing methods of identifying potential binding sites in a genome of interest and there is little published information regarding the optimality of different choices. Thanks to the availability of large number of different motif models as well as a number of experimental datasets describing actual binding of TFs in hundreds of TF-ChIP-seq pairs, we set out to perform a comprehensive analysis of this matter. RESULTS: We focus on the task of identifying potential transcription factor binding sites in the human genome. Firstly, we provide a comprehensive comparison of the coverage and quality of models available in different databases, showing that the public databases have comparable TFs coverage and better motif performance than commercial databases. Secondly, we compare different motif scanners showing that, regardless of the database used, the tools developed by the scientific community outperform the commercial tools. Thirdly, we calculate for each motif a detection threshold optimizing the accuracy of prediction. Finally, we provide an in-depth comparison of different methods of choosing thresholds for all motifs a priori. Surprisingly, we show that selecting a common false-positive rate gives results that are the least biased by the information content of the motif and therefore most uniformly accurate. CONCLUSION: We provide a guide for researchers working with transcription factor motifs. It is supplemented with detailed results of the analysis and the benchmark datasets at http://bioputer.mimuw.edu.pl/papers/motifs/ .

A probabilistic approach to learn chromatin architecture and accurate inference of the NF-κB/RelA regulatory network using ChIP-Seq.

Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for iterative learning of regulatory networks. For every possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative distance and orientation are calculated to learn which TFBSs are most likely to regulate a given gene. Weighted TFBS contributions to putative gene regulation are integrated to derive an NF-κB gene network. A de novo motif enrichment analysis uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-κB/RelA TFBSs. Comparison with experimental ENCODE ChIP-Seq data indicates that experimental TFBSs highly correlate with predicted sites. We observe that RelA-SP1-enriched promoters have distinct expression profiles from that of RelA-AP1 and are enriched in introns, CpG islands and DNase accessible sites. Sixteen novel NF-κB/RelA-regulated genes and TFBSs were experimentally validated, including TANK, a negative feedback gene whose expression is NF-κB/RelA dependent and requires a functional interaction with the AP1 TFBSs. Our probabilistic method yields more accurate NF-κB/RelA-regulated networks than a traditional, distance-based approach, confirmed by both analysis of gene expression and increased informativity of Genome Ontology annotations. Our analysis provides new insights into how co-occurring TFBSs and local chromatin context orchestrate activation of NF-κB/RelA sub-pathways differing in biological function and temporal expression patterns.

BNFinder2: Faster Bayesian network learning and Bayesian classification.

SUMMARY: Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. AVAILABILITY AND IMPLEMENTATION: BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user's manual, introductory tutorial and supplementary methods.

Linearization of genome sequence graphs revisited.

The need to include the genetic variation within a population into a reference genome led to the concept of a genome sequence graph. Nodes of such a graph are labeled with DNA sequences occurring in represented genomes. Due to double-stranded nature of DNA, each node may be oriented in one of two possible ways, resulting in marking one end of the labeling sequence as in-side and the other as out-side. Edges join pairs of sides and reflect adjacency between node sequences in genomes constituting the graph. Linearization of a sequence graph aims at orienting and ordering graph nodes in a way that makes it more efficient for visualization and further analysis, e.g. access and traversal. We propose a new linearization algorithm, called ALIBI - Algorithm for Linearization by Incremental graph BuIlding. The evaluation shows that ALIBI is computationally very efficient and generates high-quality results.

BNFinder: exact and efficient method for learning Bayesian networks.

MOTIVATION: Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic programming abilities, which restricts their potential user base. Our goal was to provide software which would be freely available, efficient and usable to non-programmers. RESULTS: We present a BNFinder software, which allows for Bayesian network reconstruction from experimental data. It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks. The main advantage of BNFinder is the use exact algorithm, which is at the same time very efficient (polynomial with respect to the number of observations).

Finding evolutionarily conserved cis-regulatory modules with a universal set of motifs.

BACKGROUND: Finding functional regulatory elements in DNA sequences is a very important problem in computational biology and providing a reliable algorithm for this task would be a major step towards understanding regulatory mechanisms on genome-wide scale. Major obstacles in this respect are that the fact that the amount of non-coding DNA is vast, and that the methods for predicting functional transcription factor binding sites tend to produce results with a high percentage of false positives. This makes the problem of finding regions significantly enriched in binding sites difficult. RESULTS: We develop a novel method for predicting regulatory regions in DNA sequences, which is designed to exploit the evolutionary conservation of regulatory elements between species without assuming that the order of motifs is preserved across species. We have implemented our method and tested its predictive abilities on various datasets from different organisms. CONCLUSION: We show that our approach enables us to find a majority of the known CRMs using only sequence information from different species together with currently publicly available motif data. Also, our method is robust enough to perform well in predicting CRMs, despite differences in tissue specificity and even across species, provided that the evolutionary distances between compared species do not change substantially. The complexity of the proposed algorithm is polynomial, and the observed running times show that it may be readily applied.

qDSB-Seq is a general method for genome-wide quantification of DNA double-strand breaks using sequencing.

DNA double-strand breaks (DSBs) are among the most lethal types of DNA damage and frequently cause genome instability. Sequencing-based methods for mapping DSBs have been developed but they allow measurement only of relative frequencies of DSBs between loci, which limits our understanding of the physiological relevance of detected DSBs. Here we propose quantitative DSB sequencing (qDSB-Seq), a method providing both DSB frequencies per cell and their precise genomic coordinates. We induce spike-in DSBs by a site-specific endonuclease and use them to quantify detected DSBs (labeled, e.g., using i-BLESS). Utilizing qDSB-Seq, we determine numbers of DSBs induced by a radiomimetic drug and replication stress, and reveal two orders of magnitude differences in DSB frequencies. We also measure absolute frequencies of Top1-dependent DSBs at natural replication fork barriers. qDSB-Seq is compatible with various DSB labeling methods in different organisms and allows accurate comparisons of absolute DSB frequencies across samples.

Applying dynamic Bayesian networks to perturbed gene expression data.

BACKGROUND: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. RESULTS: We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. CONCLUSION: We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes.

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used. Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge. Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

MSARC: Multiple sequence alignment by residue clustering.

BACKGROUND: Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences. RESULTS: We propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods. Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments. AVAILABILITY: MSARC is available at http://bioputer.mimuw.edu.pl/msarc.

Bi-billboard: symmetrization and careful choice of informant species results in higher accuracy of regulatory element prediction.

The identification of cis-regulatory modules (CRM) is one of the most important problems towards the understanding of transcriptional regulation in higher eukaryotes. Computational methods for CRM detection are gaining importance due to the availability of genomic data on one side, and costs and difficulties of experimental methods on the other side. One of proposed approaches, called Billboard, predicts CRMs based on the location of transcription factor binding sites in an analyzed sequence and a related one in so-called informant species. In the present article, we show how to combine information obtained in two symmetric runs (on the sequence of interest and on the related one) of the Billboard tool. In a series of experiments on data from various organisms, we show that the predictive power of our symmetric approach is significantly higher than the power of the one-way approach of Billboard. Moreover, we show that the evolutionary distance between organisms considerably influences the quality of prediction and we provide guidelines on the choice of an informant species.

Comparative analysis of cis-regulation following stroke and seizures in subspaces of conserved eigensystems.

BACKGROUND: It is often desirable to separate effects of different regulators on gene expression, or to identify effects of the same regulator across several systems. Here, we focus on the rat brain following stroke or seizures, and demonstrate how the two tasks can be approached simultaneously. RESULTS: We applied SVD to time-series gene expression datasets from the rat experimental models of stroke and seizures. We demonstrate conservation of two eigensystems, reflecting inflammation and/or apoptosis (eigensystem 2) and neuronal synaptic activity (eigensystem 3), between the stroke and seizures. We analyzed cis-regulation of gene expression in the subspaces of the conserved eigensystems. Bayesian networks analysis was performed separately for either experimental model, with cross-system validation of the highest-ranking features. In this way, we correctly re-discovered the role of AP1 in the regulation of apoptosis, and the involvement of Creb and Egr in the regulation of synaptic activity-related genes. We identified a novel antagonistic effect of the motif recognized by the nuclear matrix attachment region-binding protein Satb1 on AP1-driven transcriptional activation, suggesting a link between chromatin loop structure and gene activation by AP1. The effects of motifs binding Satb1 and Creb on gene expression in brain conform to the assumption of the linear response model of gene regulation. Our data also suggest that numerous enhancers of neuronal-specific genes are important for their responsiveness to the synaptic activity. CONCLUSION: Eigensystems conserved between stroke and seizures separate effects of inflammation/apoptosis and neuronal synaptic activity, exerted by different transcription factors, on gene expression in rat brain.