SFMBT1 functions with LSD1 to regulate expression of canonical histone genes and chromatin-related factors.

SFMBT1 (Scm [Sex comb on midleg] with four MBT [malignant brain tumor] domains 1) is a poorly characterized mammalian MBT domain-containing protein homologous to Drosophila SFMBT, a Polycomb group protein involved in epigenetic regulation of gene expression. Here, we show that SFMBT1 regulates transcription in somatic cells and during spermatogenesis through the formation of a stable complex with LSD1 and CoREST. When bound to its gene targets, SFMBT1 recruits its associated proteins and causes chromatin compaction and transcriptional repression. SFMBT1, LSD1, and CoREST share a large fraction of target genes, including those encoding replication-dependent histones. Simultaneous occupancy of histone genes by SFMBT1, LSD1, and CoREST is regulated during the cell cycle and correlates with the loss of RNA polymerase II at these promoters during G2, M, and G1. The interplay between the repressive SFMBT1-LSD1-CoREST complex and RNA polymerase II contributes to the timely transcriptional regulation of histone genes in human cells. SFMBT1, LSD1, and CoREST also form a stable complex in germ cells, and their chromatin binding activity is regulated during spermatogenesis.

PCGF homologs, CBX proteins, and RYBP define functionally distinct PRC1 family complexes.

The heterogeneous nature of mammalian PRC1 complexes has hindered our understanding of their biological functions. Here, we present a comprehensive proteomic and genomic analysis that uncovered six major groups of PRC1 complexes, each containing a distinct PCGF subunit, a RING1A/B ubiquitin ligase, and a unique set of associated polypeptides. These PRC1 complexes differ in their genomic localization, and only a small subset colocalize with H3K27me3. Further biochemical dissection revealed that the six PCGF-RING1A/B combinations form multiple complexes through association with RYBP or its homolog YAF2, which prevents the incorporation of other canonical PRC1 subunits, such as CBX, PHC, and SCM. Although both RYBP/YAF2- and CBX/PHC/SCM-containing complexes compact chromatin, only RYBP stimulates the activity of RING1B toward H2AK119ub1, suggesting a central role in PRC1 function. Knockdown of RYBP in embryonic stem cells compromised their ability to form embryoid bodies, likely because of defects in cell proliferation and maintenance of H2AK119ub1 levels.

Close proximity to Igh is a contributing factor to AID-mediated translocations.

Class switch recombination (CSR) has the potential to generate genomic instability in B cells as activation-induced cytidine deaminase (AID), which mediates this process, is known to target many sites outside Igh. Nonetheless we do not fully understand what factors influence AID targeting genome-wide. Given that errors in CSR can lead to dangerous, oncogenic chromosomal translocations it is important to identify the elements that determine which genes are at risk of being "hit" and could be involved in aberrant rearrangements. Here we have investigated the influence of nuclear organization in determining "off-target" activity and the choice of fusion partners. Our studies indicate that the vast majority of known AID-mediated Igh translocation partners are found in chromosomal domains that contact this locus during class switching. Further, these interaction domains can be used to identify other genes that are hit by AID.

FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells.

Promoters and enhancers establish precise gene transcription patterns. The development of functional approaches for their identification in mammalian cells has been complicated by the size of these genomes. Here we report a high-throughput functional assay for directly identifying active promoter and enhancer elements called FIREWACh (Functional Identification of Regulatory Elements Within Accessible Chromatin), which we used to simultaneously assess over 80,000 DNA fragments derived from nucleosome-free regions within the chromatin of embryonic stem cells (ESCs) and identify 6,364 active regulatory elements. Many of these represent newly discovered ESC-specific enhancers, showing enriched binding-site motifs for ESC-specific transcription factors including SOX2, POU5F1 (OCT4) and KLF4. The application of FIREWACh to additional cultured cell types will facilitate functional annotation of the genome and expand our view of transcriptional network dynamics.

Ranking and combining multiple predictors without labeled data.

In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier's accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.

Identifying peaks in *-seq data using shape information.

BACKGROUND: Peak calling is a fundamental step in the analysis of data generated by ChIP-seq or similar techniques to acquire epigenetics information. Current peak callers are often hard to parameterise and may therefore be difficult to use for non-bioinformaticians. In this paper, we present the ChIP-seq analysis tool available in CLC Genomics Workbench and CLC Genomics Server (version 7.5 and up), a user-friendly peak-caller designed to be not specific to a particular *-seq protocol. RESULTS: We illustrate the advantages of a shape-based approach and describe the algorithmic principles underlying the implementation. Thanks to the generality of the idea and the fact the algorithm is able to learn the peak shape from the data, the implementation requires only minimal user input, while still being applicable to a range of *-seq protocols. Using independently validated benchmark datasets, we compare our implementation to other state-of-the-art algorithms explicitly designed to analyse ChIP-seq data and provide an evaluation in terms of receiver-operator characteristic (ROC) plots. In order to show the applicability of the method to similar *-seq protocols, we also investigate algorithmic performances on DNase-seq data. CONCLUSIONS: The results show that CLC shape-based peak caller ranks well among popular state-of-the-art peak callers while providing flexibility and ease-of-use.

Comparative FAIRE-seq analysis reveals distinguishing features of the chromatin structure of ground state- and primed-pluripotent cells.

Both pluripotent embryonic stem cells (ESCs), established from preimplantation murine blastocysts, and epiblast stem cells (EpiSCs), established from postimplantation embryos, can self-renew in culture or differentiate into each of the primary germ layers. While the core transcription factors (TFs) OCT4, SOX2, and NANOG are expressed in both cell types, the gene expression profiles and other features suggest that ESCs and EpiSCs reflect distinct developmental maturation stages of the epiblast in vivo. Accordingly, "naïve" or "ground state" ESCs resemble cells of the inner cell mass, whereas "primed" EpiSCs resemble cells of the postimplantation egg cylinder. To gain insight into the relationship between naïve and primed pluripotent cells, and of each of these pluripotent states to that of nonpluripotent cells, we have used FAIRE-seq to generate a comparative atlas of the accessible chromatin regions within ESCs, EpiSCs, multipotent neural stem cells, and mouse embryonic fibroblasts. We find a distinction between the accessible chromatin patterns of pluripotent and somatic cells that is consistent with the highly related phenotype of ESCs and EpiSCs. However, by defining cell-specific and shared regions of open chromatin, and integrating these data with published gene expression and ChIP analyses, we also illustrate unique features of the chromatin of naïve and primed cells. Functional studies suggest that multiple stage-specific enhancers regulate ESC- or EpiSC-specific gene expression, and implicate auxiliary TFs as important modulators for stage-specific activation by the core TFs. Together these observations provide insights into the chromatin structure dynamics accompanying transitions between these pluripotent states.

TrAp: a tree approach for fingerprinting subclonal tumor composition.

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures.

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein-chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.

IL-7 functionally segregates the pro-B cell stage by regulating transcription of recombination mediators across cell cycle.

Ag receptor diversity involves the introduction of DNA double-stranded breaks during lymphocyte development. To ensure fidelity, cleavage is confined to the G(0)-G(1) phase of the cell cycle. One established mechanism of regulation is through periodic degradation of the RAG2 recombinase protein. However, there are additional levels of protection. In this paper, we show that cyclical changes in the IL-7R signaling pathway functionally segregate pro-B cells according to cell cycle status. In consequence, the level of a downstream effector of IL-7 signaling, phospho-STAT5, is inversely correlated with cell cycle expression of Rag, a key gene involved in recombination. Higher levels of phopho-STAT5 in S-G(2) correlate with decreased Rag expression and Rag relocalization to pericentromeric heterochromatin. These cyclical changes in transcription and locus repositioning are ablated upon transformation with v-Abl, which renders STAT5 constitutively active across the cell cycle. We propose that this activity of the IL-7R/STAT5 pathway plays a critical protective role in development, complementing regulation of RAG2 at the protein level, to ensure that recombination does not occur during replication. Our data, suggesting that pro-B cells are not a single homogeneous population, explain inconsistencies in the role of IL-7 signaling in regulating Igh recombination.

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments.

Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algorithm to detect regions of increased ChIP read density relative to background. Qeseq employs critical novel elements, such as iterative recalibration and neighbor joining of reads to identify enriched regions of any length. To objectively assess its performance relative to other 14 ChIP-Seq peak finders, we designed a novel protocol based on Validation Discriminant Analysis (VDA) to optimally select validation sites and generated two validation datasets, which are the most comprehensive to date for algorithmic benchmarking of key epigenetic marks. In addition, we systematically explored a total of 315 diverse parameter configurations from these algorithms and found that typically optimal parameters in one dataset do not generalize to other datasets. Nevertheless, default parameters show the most stable performance, suggesting that they should be used. This study also provides a reproducible and generalizable methodology for unbiased comparative analysis of high-throughput sequencing tools that can facilitate future algorithmic development.

Gene trajectory inference for single-cell data by optimal transport metrics.

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

LMD: Multiscale Marker Identification in Single-cell RNA-seq Data.

Accurate cell marker identification in single-cell RNA-seq data is crucial for understanding cellular diversity and function. An ideal marker is highly specific in identifying cells that are similar in terms of function and state. Current marker identification methods, commonly based on clustering and differential expression, capture general cell-type markers but often miss markers for subtypes or functional cell subsets, with their performance largely dependent on clustering quality. Moreover, cluster-independent approaches tend to favor genes that lack the specificity required to characterize regions within the transcriptomic space at multiple scales. Here we introduce Localized Marker Detector (LMD), a novel tool to identify "localized genes" - genes with expression profiles specific to certain groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD's strategy involves building a cell-cell affinity graph, diffusing the gene expression value across the cell graph, and assigning a score to each gene based on its diffusion dynamics. We show that LMD exhibits superior accuracy in recovering known cell-type markers in the Tabula Muris bone marrow dataset relative to other methods for marker identification. Notably, markers favored by LMD exhibit localized expression, whereas markers prioritized by other clustering-free algorithms are often dispersed in the transcriptomic space. We further group the markers suggested by LMD into functional gene modules to improve the separation of cell types and subtypes in a more fine-grained manner. These modules also identify other sources of variation, such as cell cycle status. In conclusion, LMD is a novel algorithm that can identify fine-grained markers for cell subtypes or functional states without relying on clustering or differential expression analysis. LMD exploits the complex interactions among cells and reveals cellular diversity at high resolution.

Integrated analysis of tumor samples sheds light on tumor heterogeneity.

The heterogeneity of tumor samples is a major challenge in the analysis of high-throughput profiling of tumor biopsies and cell lines. The measured aggregate signals of multigenerational progenies often represent an average of several tumor subclones with varying genomic aberrations and different gene expression levels. The goal of the present study was to integrate copy number analyses from SNP-arrays and karyotyping, gene expression profiling, and pathway analyses to detect heterogeneity, identify driver mutations, and explore possible mechanisms of tumor evolution. We showed the heterogeneity of the studied samples, characterized the global copy number alteration profiles, and identified genes whose copy number status and expression levels were aberrant. In particular, we identified a recurrent association between two BRAF(V600E) and BRAF(V600K) mutations and changes in DKK1 gene expression levels, which might indicate an association between the BRAF and WNT pathways. These findings show that the integrated approaches used in the present study can robustly address the challenging issue of tumor heterogeneity in high-throughput profiling.

Computational studies on the interaction of ABO-active saccharides with the norovirus VA387 capsid protein can explain experimental binding data.

Norovirus strains are known to cause recurring epidemics of winter vomiting disease. The crystal structure of the capsid protein of VA387, a representative of the clinically important GII.4 genocluster, was recently solved in complex with histo-blood group A- and B-trisaccharides. However, the VA387 strain is known to bind also to other natural carbohydrates for which detailed structural information of the complexes is not available. In this study we have computationally explored the fit of the VA387 with a set of naturally occurring carbohydrate ligands containing a terminal alpha1,2-linked fucose. MD simulations both with explicit and implicit solvent models indicate that type 1 and 3 extensions of the ABO-determinant including ALe(b) and BLe(b) pentasaccharides can be well accommodated in the site. Scoring with Glide XP indicates that the downstream extensions of the ABO-determinants give an increase in binding strength, although the alpha1,2-linked fucose is the single strongest interacting residue. An error was discovered in the geometry of the GalNAc-Gal moiety of the published crystal structure of the A-trisaccharide/VA387 complex. The present modeling of the complexes with histo-blood group A-active structures shows some contacts which provide insight into mutational data, explaining the involvement of I389 and Q331. Our results can be applicable in structure-based design of adhesion inhibitors of noroviruses.

Selenoglycosides in silico: ab initio-derived reparameterization of MM4, conformational analysis using histo-blood group ABH antigens and lectin docking as indication for potential of bioactivity.

The identification of glycan epitopes such as the histo-blood group ABH determinants as docking sites for bacterial/viral infections and signals in growth regulation fuels the interest to develop non-hydrolysable mimetics for therapeutic applications. Inevitably, the required substitution of the linkage oxygen atom will alter the derivative's topology. Our study addresses the question of the impact of substitution of oxygen by selenium. In order to characterize spatial parameters and flexibility of selenoglycosides, we first performed ab initio calculations on model compounds to refine the MM4 force field. The following application of the resulting MM4R version appears to reduce the difference to ab initio data when compared to using the MM4 estimator. Systematic conformational searches on the derivatives of histo-blood group ABH antigens revealed increased flexibility with acquisition of additional low-energy conformer(s), akin to the behavior of S-glycosides. Docking analysis using the Glide program for eight test cases indicated potential for bioactivity, giving further experimental investigation a clear direction to testing Se-glycosides as lectin ligands.

Conformational analysis of thioglycoside derivatives of histo-blood group ABH antigens using an ab initio-derived reparameterization of MM4: implications for design of non-hydrolysable mimetics.

Histo-blood group ABH antigens serve as recognition sites for infectious microorganisms and tissue lectins in intercellular communication, e.g. in tumor progression. Thus, they are of interest as a starting point for drug design. In this respect, potent non-hydrolysable derivatives such as thioglycosides are of special interest. As prerequisite to enable estimations of ligand properties relative to their natural counterparts, conformational properties of the thioglycosidic derivatives of ABH trisaccharides and their disaccharide units were calculated using systematic and filtered systematic searches with the MM4 force field. Parameters for the glycosidic torsions of thioglycosides were independently derived from ab initio calculations. The resulting energy deviations required a reparameterization of MM4 to a new parameter set called MM4R. The data sets obtained using MM4R reveal that the thioglycosides have somewhat increased levels of flexibility about the major low-energy conformations shared with the corresponding O-glycosides. In the trisaccharides, the thiosubstitution of the Gal[NAc]alpha1-3Gal linkage leads to a preference for a conformation which is the secondary minimum of the natural counterparts. This conformation also generates contacts between the N-acetyl group and the fucose moiety in the blood group A derivative. Calculations further indicate that thiosubstitution of only the Fucalpha1-2Gal linkage does not affect the conformational preferences compared to the natural trisaccharide. Thiosubstitution of both linkages in the trisaccharide results in increased flexibility but the favored conformation of the natural trisaccharides is preferred. The study suggests that thioglycoside derivatives of ABH antigens could have pharmaceutical interest as ligands of lectins and other carbohydrate-binding proteins.

Conformation of the exopolysaccharide of Burkholderia cepacia predicted with molecular mechanics (MM3) using genetic algorithm search.

We present a computational conformational analysis of the exopolysaccharide of Burkholderia cepacia, which is believed to play a role in colonization and persistence of B. cepacia in the lungs of cystic fibrosis patients. The repeating unit of the exopolysaccharide is a heptasaccharide with three branches, which cause significant steric restraints. Conformational searches using glygal, an in-house developed software using genetic algorithm search methods, were performed on fragments as well as on the complete repeating unit with wrap-over residues. The force field used for the calculations was MM3(96). The search showed four favored conformations for an isolated repeating unit. However, for a sequence of several repeating units, the calculations indicate a single, well-defined linear conformation.

The use of a genetic algorithm search for molecular mechanics (MM3)-based conformational analysis of oligosaccharides.

We have implemented a system called glygal that can perform conformational searches on oligosaccharides using several different genetic algorithm (GA) search methods. The searches are performed in the torsion angle conformational space, considering both the primary glycosidic linkages as well as the pendant groups (C-5-C-6 and hydroxyl groups) where energy calculations are performed using the MM3(96) force field. The system includes a graphical user interface for setting calculation parameters and incorporates a 3D molecular viewer. The system was tested using dozens of structures and we present two case studies for two previously investigated O-specific oligosaccharides of the Shigella dysenteriae type 2 and 4. The results obtained using glygal show a significant reduction in the number of structures that need to be sampled in order to find the best conformation, as compared to filtered systematic search.

VDA, a method of choosing a better algorithm with fewer validations.

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power.Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico.VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms.Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/