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Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).
Background: Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter–enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability.
Results: We have extended our TEPIC framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer–promoter loops involving YY1 in different cell lines.
Conclusion: We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.
Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs).
Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups.
Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal.
Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697)
Specialized de novo assemblers for diverse datatypes have been developed and are in widespread use for the analyses of single-cell genomics, metagenomics and RNA-seq data. However, assembly of large sequencing datasets produced by modern technologies is challenging and computationally intensive. In-silico read normalization has been suggested as a computational strategy to reduce redundancy in read datasets, which leads to significant speedups and memory savings of assembly pipelines. Previously, we presented a set multi-cover optimization based approach, ORNA, where reads are reduced without losing important k-mer connectivity information, as used in assembly graphs. Here we propose extensions to ORNA, named ORNA-Q and ORNA-K, which consider a weighted set multi-cover optimization formulation for the in-silico read normalization problem. These novel formulations make use of the base quality scores obtained from sequencers (ORNA-Q) or k-mer abundances of reads (ORNA-K) to improve normalization further. We devise efficient heuristic algorithms for solving both formulations. In applications to human RNA-seq data, ORNA-Q and ORNA-K are shown to assemble more or equally many full length transcripts compared to other normalization methods at similar or higher read reduction values. The algorithm is implemented under the latest version of ORNA (v2.0, https://github.com/SchulzLab/ORNA).
The unicellular ciliate Paramecium contains a large vegetative macronucleus with several unusual characteristics, including an extremely high coding density and high polyploidy. As macronculear chromatin is devoid of heterochromatin, our study characterizes the functional epigenomic organization necessary for gene regulation and proper Pol II activity. Histone marks (H3K4me3, H3K9ac, H3K27me3) reveal no narrow peaks but broad domains along gene bodies, whereas intergenic regions are devoid of nucleosomes. Our data implicate H3K4me3 levels inside ORFs to be the main factor associated with gene expression, and H3K27me3 appears in association with H3K4me3 in plastic genes. Silent and lowly expressed genes show low nucleosome occupancy, suggesting that gene inactivation does not involve increased nucleosome occupancy and chromatin condensation. Because of a high occupancy of Pol II along highly expressed ORFs, transcriptional elongation appears to be quite different from that of other species. This is supported by missing heptameric repeats in the C-terminal domain of Pol II and a divergent elongation system. Our data imply that unoccupied DNA is the default state, whereas gene activation requires nucleosome recruitment together with broad domains of H3K4me3. In summary, gene activation and silencing in Paramecium run counter to the current understanding of chromatin biology.
Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimization steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
The transcription factor vitamin D receptor (VDR) is the high affinity nuclear target of the biologically active form of vitamin D3 (1,25(OH)2D3). In order to identify pure genomic transcriptional effects of 1,25(OH)2D3, we used VDR cistrome, transcriptome and open chromatin data, obtained from the human monocytic cell line THP-1, for a novel hierarchical analysis applying three bioinformatics approaches. We predicted 75.6% of all early 1,25(OH)2D3-responding (2.5 or 4 h) and 57.4% of the late differentially expressed genes (24 h) to be primary VDR target genes. VDR knockout led to a complete loss of 1,25(OH)2D3–induced genome-wide gene regulation. Thus, there was no indication of any VDR-independent non-genomic actions of 1,25(OH)2D3 modulating its transcriptional response. Among the predicted primary VDR target genes, 47 were coding for transcription factors and thus may mediate secondary 1,25(OH)2D3 responses. CEBPA and ETS1 ChIP-seq data and RNA-seq following CEBPA knockdown were used to validate the predicted regulation of secondary vitamin D target genes by both transcription factors. In conclusion, a directional network containing 47 partly novel primary VDR target transcription factors describes secondary responses in a highly complex vitamin D signaling cascade. The central transcription factor VDR is indispensable for all transcriptome-wide effects of the nuclear hormone.
Background: With the rise of single-cell RNA sequencing new bioinformatic tools have been developed to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we benchmarked several datasets with the most common alignment tools for single-cell RNA sequencing data. We evaluated differences in the whitelisting, gene quantification, overall performance, and potential variations in clustering or detection of differentially expressed genes. We compared the tools Cell Ranger version 6, STARsolo, Kallisto, Alevin, and Alevin-fry on 3 published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol.
Results: Striking differences were observed in the overall runtime of the mappers. Besides that, Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells; however, we observed an overrepresentation of cells with low gene content and unknown cell type. Conversely, Alevin rarely reported such low-content cells. Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 6, Alevin-fry, and Alevin produced similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artefacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes.
Conclusion: Overall, this study provides a detailed comparison of common single-cell RNA sequencing mappers and shows their specific properties on 10X Genomics data.
Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2.
KDEL receptors (KDELRs) represent transmembrane proteins of the secretory pathway which regulate the retention of soluble ER-residents as well as retrograde and anterograde vesicle trafficking. In addition, KDELRs are involved in the regulation of cellular stress response and ECM degradation. For a deeper insight into KDELR1 specific functions, we characterised a KDELR1-KO cell line (HAP1) through whole transcriptome analysis by comparing KDELR1-KO cells with its respective HAP1 wild-type. Our data indicate more than 300 significantly and differentially expressed genes whose gene products are mainly involved in developmental processes such as cell adhesion and ECM composition, pointing out to severe cellular disorders due to a loss of KDELR1. Impaired adhesion capacity of KDELR1-KO cells was further demonstrated through in vitro adhesion assays, while collagen- and/or laminin-coating nearly doubled the adhesion property of KDELR1-KO cells compared to wild-type, confirming a transcriptional adaptation to improve or restore the cellular adhesion capability. Perturbations within the secretory pathway were verified by an increased secretion of ER-resident PDI and decreased cell viability under ER stress conditions, suggesting KDELR1-KO cells to be severely impaired in maintaining cellular homeostasis.