Transcription Factor Datasets Research Articles

CREPE: a Shiny app for transcription factor cataloguing.

Transcription factors (TFs) are proteins that directly interpret the genome to regulate gene expression and determine cellular phenotypes. TF identification is a common first step in unraveling gene regulatory networks. We present CREPE, an R Shiny app to catalogue and annotate TFs. CREPE was benchmarked against curated human TF datasets. Next, we use CREPE to explore the TF repertoires of Heliconius erato and Heliconius melpomene butterflies. CREPE is available as a Shiny app package available at GitHub (github.com/dirostri/CREPE). Supplementary data are available at Bioinformatics Advances online.

Columbianetin acetate inhibits the occurrence and development of pancreatic cancer cells by down-regulating the expression of Meiotic nuclear divisions 1

Columbianetin acetate (CE) is one of the effective components of Angelica pubescens. So far, the specific role and molecular mechanism of CE in pancreatic cancer are not clear. Thus, this study aimed to explore the specific mechanism of CE on pancreatic cancer. The target genes combined with CE were predicted through the PharmMapper database and the 3D molecular structure of CE. Then, the Cancer Genome Atlas (TCGA) and Cistrome data browser (DB) databases were used to screen Meiotic nuclear divisions 1 (MND1)-related genes, transcription factors, and transcription factor data sets, and the intersection of the above data sets. The “limma” package in the R and gene expression profiling interactive analysis (GEPIA) databases were used to analyze the correlation and survival difference between the target genes and MND1 to predict the degree of association between CE and MND1. Western blotting and RT-PCR experiments revealed the regulatory relationship among CE, E2F1, and MND1 at the cellular level. The specific effects of CE on pancreatic cancer cells were explored through CCK8, wound healing, migration, and flow cycle experiments. E2F1, also the predictive transcription factor of MND1, was also the predictive target protein of CE. At the same time, E2F1 and MND1 were closely related in pancreatic tissue. In the cell function experiment, CE and interference with E2F1 expression could reduce the gene and protein expression of MND1, which was closely associated with cell proliferation, migration, and cycle development. Similarly, interfering with the expression of mnd1 can also inhibit the further development of tumor cells. CE may inhibit the development of pancreatic cancer cells by reducing the expression of MND1. This implies that CE may be a potential novel agent for the treatment of pancreatic cancer.

GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction.

Transcription factors (TFs) are important factors that regulate gene expression. Revealing the mechanism affecting the binding specificity of TFs is the key to understanding gene regulation. Most of the previous studies focus on TF-DNA binding sites at the sequence level, and they seldom utilize the contextual features of DNA sequences. In this paper, we develop an integrated spatiotemporal context-aware neural network framework, named GNet, for predicting TF-DNA binding signal at single nucleotide resolution by achieving three tasks: single nucleotide resolution signal prediction, identification of binding regions at the sequence level, and TF-DNA binding motif prediction. GNet extracts implicit spatial contextual information with a gated highway neural mechanism, which captures large context multi-level patterns using linear shortcut connections, and the idea of it permeates the encoder and decoder parts of GNet. The improved dual external attention mechanism, which learns implicit relationships both within and among samples, and improves the performance of the model. Experimental results on 53 human TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets shows that GNet outperforms the state-of-the-art methods in the three tasks, and the results of cross-species studies on 15 human and 18 mouse TF datasets of the corresponding TF families indicate that GNet also shows the best performance in cross-species prediction over the competitive methods.

Identification of lncRNA Biomarkers and LINC01198 Promotes Progression of Chronic Rhinosinusitis with Nasal Polyps through Sponge miR-6776-5p.

Background Chronic sinusitis (CRS) was a chronic inflammation that originated in the nasal mucosa and affected the health of most people around the world. Chronic rhinosinusitis with nasal polyps (CRSwNP) was one kind of chronic sinusitis. Emerging research had suggested that long noncoding RNAs (lncRNAs) played vital parts in inflammatories and inflammation development. Methods We acquired GEO data to analyze the differential expression between the miRNA, immune genes, TF, and lncRNA data in CRSWNP and the corresponding control tissues. Bioinformatic analysis by coexpression of endogenous RNA network and competitive way enrichment, analysis, and forecasting functions of these noncoding RNA. The different pathway expressions in CRSwNP patients were confirmed using GSVA to analyze the differentially expressed immune genes and TF data sets in CRSwNP patients. The differential immune gene and transcription factor data set in CRSwNP perform functional notes and protein-protein interaction (PPI) network structure. We predicted the potential genes and RNAs related to CRSWNP by constructing a ceRNA network. In addition, we also used 19 hub immune genes to predict the potential drugs of CRSWNP. lncRNA biomarkers in CRSwNP were identified by lncRNAs LASSO regression. The CIBERSORT algorithm was used to contrast the divergence in immune infiltrations between CRSwNP and usual inferior turbinate organizations in 22 immunocyte subgroups. Results We identified a total of 48 miRNAs, 304 lncRNAs, 92 TFs, and 525 immune genes as CRSwNP-specific RNAs. GO and KEGG pathways both analyzed differentially expressed immune genes and transcription factor data sets. We predicted the potential genes GNG7, TUSC8, LINC01198, and has-miR-6776-5p by constructing ceRNA and PPI networks. At the same time, we found that the above genes were involved in two important pathways: chemokine signal path and PI3K/AKT signal path. In addition, we predicted 5 small molecule drugs to treat CRSwNP by analyzing 19 central immune genes, namely, danazol, ikarugamycin, semustine, cefamandole, and molindone. Finally, we identified 5 biomarkers in CRSwNP, namely, LINC01198, LINC01094, LINC01798, LINC01829, and LINC01320. Conclusions We had identified CRSwNP-related miRNAs, lncRNAs, TFs, and immune genes, which may be making use of latent therapeutic target for CRSwNP. At the same time, we identified 5 lncRNA biomarkers in CRSwNP. The results of this study showed that LINC01198 promoted the progression of CRSwNPs through spongy miR-6776-5p. Our studies provide a new way for further analyses of the pathogenesis of CRSwNP.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

MotivationMany peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks. The main idea is to manually label a small subset of the genome, and then learn a model that makes consistent peak predictions on the rest of the genome.ResultsWe created 7 new histone mark datasets with 12 826 visually determined labels, and analyzed 3 existing transcription factor datasets. We observed that default peak detection parameters yield high false positive rates, which can be reduced by learning parameters using a relatively small training set of labeled data from the same experiment type. We also observed that labels from different people are highly consistent. Overall, these data indicate that our supervised labeling method is useful for quantitatively training and testing peak detection algorithms.Availability and ImplementationLabeled histone mark data http://cbio.ensmp.fr/~thocking/chip-seq-chunk-db/, R package to compute the label error of predicted peaks https://github.com/tdhock/PeakErrorSupplementary information Supplementary data are available at Bioinformatics online.

Convolutional neural network architectures for predicting DNA-protein binding.

Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology.Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu.Contact: gifford@mit.eduSupplementary information: Supplementary data are available at Bioinformatics online.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data.

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) iswidely used to identify the genomic binding sites for protein of interest. Most conventionalapproaches to ChIP-seq data analysis involve the detection of the absolutepresence (or absence) of a binding site. However, an alternative strategy is toidentify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses,as changes in binding can be associated with the biological difference beinginvestigated. The aim of this article is to facilitate the implementation of DB analyses,by comprehensively describing a computational workflow for the detection ofDB regions from ChIP-seq data. The workflow is based primarily on R softwarepackages from the open-source Bioconductor project and covers all steps of theanalysis pipeline, from alignment of read sequences to interpretation and visualizationof putative DB regions. In particular, detection of DB regions will be conductedusing the counts for sliding windows from the csaw package, with statistical modellingperformed using methods in the edgeR package. Analyses will be demonstratedon real histone mark and transcription factor data sets. This will providereaders with practical usage examples that can be applied in their own studies.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes

A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about 40,000 such sequences. The discrepancy between the number of possible configurations and the available activities is enormous. Thus, albeit that systems biology datasets are large in absolute terms, they oftentimes require methods developed for rare events due to the combinatorial increase in the number of possible configurations of biological systems. A plethora of techniques for handling large datasets, such as Empirical Bayes, or rare events, such as importance sampling, have been developed in the literature, but these cannot always be simultaneously utilized. Here we introduce a principled approach to Empirical Bayes based on importance sampling, information theory, and theoretical physics in the general context of sequence phenotype model induction. We present the analytical calculations that underlie our approach. We demonstrate the computational efficiency of the approach on concrete examples, and demonstrate its efficacy by applying the theory to publicly available protein binding microarray transcription factor datasets and to data on synthetic cAMP-regulated enhancer sequences. As further demonstrations, we find transcription factor binding motifs, predict the activity of new sequences and extract the locations of transcription factor binding sites. In summary, we present a novel method that is efficient (requiring minimal computational time and reasonable amounts of memory), has high predictive power that is comparable with that of models with hundreds of parameters, and has a limited number of optimized parameters, proportional to the sequence length.

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets.

BackgroundThe global effort to annotate the non-coding portion of the human genome relies heavily on chromatin immunoprecipitation data generated with high-throughput DNA sequencing (ChIP-seq). ChIP-seq is generally successful in detailing the segments of the genome bound by the immunoprecipitated transcription factor (TF), however almost all datasets contain genomic regions devoid of the canonical motif for the TF. It remains to be determined if these regions are related to the immunoprecipitated TF or whether, despite the use of controls, there is a portion of peaks that can be attributed to other causes.ResultsAnalyses across hundreds of ChIP-seq datasets generated for sequence-specific DNA binding TFs reveal a small set of TF binding profiles for which predicted TF binding site motifs are repeatedly observed to be significantly enriched. Grouping related binding profiles, the set includes: CTCF-like, ETS-like, JUN-like, and THAP11 profiles. These frequently enriched profiles are termed ‘zingers’ to highlight their unanticipated enrichment in datasets for which they were not the targeted TF, and their potential impact on the interpretation and analysis of TF ChIP-seq data. Peaks with zinger motifs and lacking the ChIPped TF’s motif are observed to compose up to 45% of a ChIP-seq dataset. There is substantial overlap of zinger motif containing regions between diverse TF datasets, suggesting a mechanism that is not TF-specific for the recovery of these regions.ConclusionsBased on the zinger regions proximity to cohesin-bound segments, a loading station model is proposed. Further study of zingers will advance understanding of gene regulation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0412-4) contains supplementary material, which is available to authorized users.

On the identification of potential regulatory variants within genome wide association candidate SNP sets.

BackgroundGenome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regulatory sequences that are likely to contribute to the phenotype assessed.MethodsWe focused investigation on SNPs within lung and breast cancer GWAS loci that reached genome-wide significance for potential roles in gene regulation with a specific focus on SNPs likely to disrupt transcription factor binding sites. Within risk loci, the regulatory potential of sub-regions was classified using relevant open chromatin and epigenetic high throughput sequencing data sets from the ENCODE project in available cancer and normal cell lines. Furthermore, transcription factor affinity altering variants were predicted by comparison of position weight matrix scores between disease and reference alleles. Lastly, ChIP-seq data of transcription associated factors and topological domains were included as binding evidence and potential gene target inference.ResultsThe sets of SNPs, including both the disease-associated markers and those in high linkage disequilibrium with them, were significantly over-represented in regulatory sequences of cancer and/or normal cells; however, over-representation was generally not restricted to disease-relevant tissue specific regions. The calculated regulatory potential, allelic binding affinity scores and ChIP-seq binding evidence were the three criteria used to prioritize candidates. Fitting all three criteria, we highlighted breast cancer susceptibility SNPs and a borderline lung cancer relevant SNP located in cancer-specific enhancers overlapping multiple distinct transcription associated factor ChIP-seq binding sites.ConclusionIncorporating high throughput sequencing epigenetic and transcription factor data sets from both cancer and normal cells into cancer genetic studies reveals potential functional SNPs and informs subsequent characterization efforts.

PePr: a peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data.

ChIP-Seq is the standard method to identify genome-wide DNA-binding sites for transcription factors (TFs) and histone modifications. There is a growing need to analyze experiments with biological replicates, especially for epigenomic experiments where variation among biological samples can be substantial. However, tools that can perform group comparisons are currently lacking. We present a peak-calling prioritization pipeline (PePr) for identifying consistent or differential binding sites in ChIP-Seq experiments with biological replicates. PePr models read counts across the genome among biological samples with a negative binomial distribution and uses a local variance estimation method, ranking consistent or differential binding sites more favorably than sites with greater variability. We compared PePr with commonly used and recently proposed approaches on eight TF datasets and show that PePr uniquely identifies consistent regions with enriched read counts, high motif occurrence rate and known characteristics of TF binding based on visual inspection. For histone modification data with broadly enriched regions, PePr identified differential regions that are consistent within groups and outperformed other methods in scaling False Discovery Rate (FDR) analysis. http://code.google.com/p/pepr-chip-seq/.

Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets

BackgroundThe global effort to annotate the non-coding portion of the human genome relies heavily on chromatin immunoprecipitation data generated with high-throughput DNA sequencing (ChIP-seq). ChIP-seq is generally successful in detailing the segments of the genome bound by the immunoprecipitated transcription factor (TF), however almost all datasets contain genomic regions devoid of the canonical motif for the TF. It remains to be determined if these regions are related to the immunoprecipitated TF or whether, despite the use of controls, there is a portion of peaks that can be attributed to other causes.ResultsAnalyses across hundreds of ChIP-seq datasets generated for sequence-specific DNA binding TFs reveal a small set of TF binding profiles for which predicted TF binding site motifs are repeatedly observed to be significantly enriched. Grouping related binding profiles, the set includes: CTCF-like, ETS-like, JUN-like, and THAP11 profiles. These frequently enriched profiles are termed ‘zingers’ to highlight their unanticipated enrichment in datasets for which they were not the targeted TF, and their potential impact on the interpretation and analysis of TF ChIP-seq data. Peaks with zinger motifs and lacking the ChIPped TF’s motif are observed to compose up to 45% of a ChIP-seq dataset. There is substantial overlap of zinger motif containing regions between diverse TF datasets, suggesting a mechanism that is not TF-specific for the recovery of these regions.ConclusionsBased on the zinger regions proximity to cohesin-bound segments, a loading station model is proposed. Further study of zingers will advance understanding of gene regulation.

Reconstructing a Genotype-Phenotype Map In Chronic Lymphocytic Leukemia

CLL exhibits great variability in genetic alterations and clinical outcome across patients. Gene expression can serve as an intermediate measurement to provide insight into the cellular circuitry linking genotype to phenotype. To explore this approach, we developed methods to statistically assess the associations between somatic mutations, transcriptional programs, and clinical outcome.First, in a cohort of 229 CLL patients, we associated IGHV-mutation status as well as 7 recurrent (>5% of CLLs) somatic alterations with 44 modules of co-expressed genes that were derived by clustering 3719 variably expressed genes measured by Affymetrix arrays. The somatic mutation genotypes were identified by whole exome sequencing (N=130) and/or SNP6 arrays (N=229). By permutation testing, we determined that 21 modules were associated with IGHV mutation status (P<0.001). After controlling for IGHV mutation status, we identified mutated genotypes associated with independent expression modules (trisomy 12 [n=10], ATM/del11q [n=12], SF3B1 [n=6], TP53/del17p [n=5], del13q [n=2], MYD88 alone [n=1], NOTCH1 [n=0] and some modules associated with more than one genotype (P<0.001)). 63% of these candidate genotype-module associations were validated using an independent dataset of matched WES and gene expression data from 82 patients (Puente et al, Nat Gen, 2011) (P<0.1; I.e. of the validated 63%, fewer than 3 are expected to be false positives).Second, having identified transcriptional modules associated with distinct genotypes, we sought to understand the functions of these modules and to infer the regulators of these programs. To associate modules with potential functional phenotypes, we performed gene enrichment analysis, and found multiple modules associated with inflammatory signatures, DNA repair and MYC targets. We then populated the intermediary layer between genotype and module with candidate transcription factors (TFs) by integrating curated TF datasets with TF expression, motif analysis and module expression. For example, CREB and ATF were candidate TF regulators in modules associated with SF3B1 and ATM mutations, respectively; while MYC- and NFkB-related TFs were candidate regulators of modules associated with both trisomy 12 or MYD88 mutations. We also identified several candidates of cellular convergence, where multiple genotypes lead to activation of the same transcriptional program. For example, the transcription factor EBF1, an important B cell regulator, was nominated as a candidate regulator in 8 of 44 modules which were associated with differing genotypes, suggesting the importance of EBF1 in mediating the genotype-phenotype relationship.Third, to complete the map from genotype to phenotype, we linked module expression with a clinical outcome. Using elastic-net Cox regression, we identified 2 modules associated with longer and 6 with shorter time from sample acquisition to treatment or death. Many of these modules were associated with well-established prognostic indicators (6 with IGHV status, 1 with P53/del17p, and 1 with SF3B1 status P<.001). One module lacking statistical association with any genotype was enriched with a stem cell (P=5-11) and with immune system activation (P=2-6) gene-sets. We used the expression-based Cox-regression index to classify patients into high and low risk subgroups (logrank P=6.1-9). Ongoing work seeks to assess the predictive power of our gene-expression signature in relation to traditional prognostic measures, as well as to further annotate the outcome-associated modules.In summary, this analysis serves as a proof of principle for a ‘genotype-phenotype map' for CLL linking somatic alterations, gene expression programs, and clinical outcome. Our inferred CLL networks generate testable hypotheses that explain how genotypes affect the cellular circuitry of CLL cells that are currently being tested through functional gain/loss of function experiments. Disclosures:Brown:Pharmacyclics, Genentech, Celgene, Emergent, Onyx, Sanofi Aventis, Vertex, Avila, Novartis: Consultancy; Genzyme, Celgene: Research Funding.

TFBSshape: a motif database for DNA shape features of transcription factor binding sites

Transcription factor binding sites (TFBSs) are most commonly characterized by the nucleotide preferences at each position of the DNA target. Whereas these sequence motifs are quite accurate descriptions of DNA binding specificities of transcription factors (TFs), proteins recognize DNA as a three-dimensional object. DNA structural features refine the description of TF binding specificities and provide mechanistic insights into protein–DNA recognition. Existing motif databases contain extensive nucleotide sequences identified in binding experiments based on their selection by a TF. To utilize DNA shape information when analysing the DNA binding specificities of TFs, we developed a new tool, the TFBSshape database (available at http://rohslab.cmb.usc.edu/TFBSshape/), for calculating DNA structural features from nucleotide sequences provided by motif databases. The TFBSshape database can be used to generate heat maps and quantitative data for DNA structural features (i.e., minor groove width, roll, propeller twist and helix twist) for 739 TF datasets from 23 different species derived from the motif databases JASPAR and UniPROBE. As demonstrated for the basic helix-loop-helix and homeodomain TF families, our TFBSshape database can be used to compare, qualitatively and quantitatively, the DNA binding specificities of closely related TFs and, thus, uncover differential DNA binding specificities that are not apparent from nucleotide sequence alone.

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data

BackgroundFinding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM.ResultsSensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually.ConclusionsWe show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.

LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.

Evaluation of algorithm performance in ChIP-seq peak detection.

Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.

Intrinsic Disorder in Transcription Factors

Intrinsic disorder (ID) is highly abundant in eukaryotes, which reflect the greater need for disorder-associated signaling and transcriptional regulation in nucleated cells. Although several well-characterized examples of intrinsically disordered proteins in transcriptional regulation have been reported, no systematic analysis has been reported so far. To test for the general prevalence of intrinsic disorder in transcriptional regulation, we used the predictor of natural disorder regions (PONDR) to analyze the abundance of intrinsic disorder in three transcription factor datasets and two control sets. This analysis revealed that from 94.13 to 82.63% of transcription factors possess extended regions of intrinsic disorder, relative to 54.51 and 18.64% of the proteins in two control datasets, which indicates the significant prevalence of intrinsic disorder in transcription factors. This propensity of transcription factors to intrinsic disorder was confirmed by cumulative distribution function analysis and charge-hydropathy plots. The amino acid composition analysis showed that all three transcription factor datasets were substantially depleted in order-promoting residues and significantly enriched in disorder-promoting residues. Our analysis of the distribution of disorder within the transcription factor datasets revealed that (a) the AT-hooks and basic regions of transcription factor DNA-binding domains are highly disordered; (b) the degree of disorder in transcription factor activation regions is much higher than that in DNA-binding domains; (c) the degree of disorder is significantly higher in eukaryotic transcription factors than in prokaryotic transcription factors; and (d) the level of alpha-MoRF (molecular recognition feature) prediction is much higher in transcription factors. Overall, our data reflected the fact that eukaryotes with well-developed gene transcription machinery require transcription factor flexibility to be more efficient.