Prediction Of Transcription Factor Binding Sites Research Articles

Long noncoding RNA LINC00165-induced by STAT3 exerts oncogenic properties via interaction with Polycomb Repressive Complex 2 to promote EMT in gastric cancer

Long non-coding RNAs (lncRNAs) are a crucial member of the non-coding RNA family, and increasing evidence demonstrates that lncRNAs participate in the initiation and progression of cancers. Our study aimed to explore the role of the lncRNA LINC00165 in gastric cancer (GC) development. In the present study, our results showed that LINC00165 was upregulated in GC tissues and cell lines and high expression of LINC00165 was correlated with tumor-node-metastasis stage, invasion depth, and overall survival of GC patients. Functional assays showed that LINC00165 knockdown inhibited the proliferation, migration and invasion of GC cells and LINC00165 overexpression stimulated their proliferation, migration and invasion. Online transcription factor binding site prediction analysis showed that there were STAT3 binding sites in the LINC00165 promoter region. Furthermore, luciferase reporter and chromatin immunoprecipitation assays provided evidence that STAT3 could bind directly to the region between −547 bp to −537 bp (ATGTTGGGAAA) of LINC00165 promoter and activate its transcription. Then GC cells were partitioned into nuclear and cytoplasmic fractions and we found that LINC00165 was localized preferentially to the nucleus. Mechanistically, we revealed that LINC00165 functioned as a scaffold for interaction with Polycomb Repressive Complex 2 to promote the epithelial-mesenchymal transition in GC cells. Taken together, our study revealed that LINC00165 was involved in the progression and poor prognosis of GC as an oncogenic role. Therefore, LINC00165 might become a new potential target for GC chemotherapy and further predict prognosis of patients.

Recurrent Neural Network for Predicting Transcription Factor Binding Sites

It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on k-mer features or convolutional neural networks, have been proposed to identify TF binding sites from DNA sequences. Although these methods have good performance, the context information that relates to TF binding sites is still lacking. Research indicates that standard recurrent neural networks (RNN) and its variants have better performance in time-series data compared with other models. In this study, we propose a model, named KEGRU, to identify TF binding sites by combining Bidirectional Gated Recurrent Unit (GRU) network with k-mer embedding. Firstly, DNA sequences are divided into k-mer sequences with a specified length and stride window. And then, we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm. Thirdly, we construct a deep bidirectional GRU model for feature learning and classification. Experimental results have shown that our method has better performance compared with some state-of-the-art methods. Additional experiments about embedding strategy show that k-mer embedding will be helpful to enhance model performance. The robustness of KEGRU is proved by experiments with different k-mer length, stride window and embedding vector dimension.

Predicting transcription factor binding using ensemble random forest models.

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).

AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors.

The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

Probe Efficient Feature Representation of Gapped K-mer Frequency Vectors from Sequences Using Deep Neural Networks.

Gapped k-mers frequency vectors (gkm-fv) has been presented for extracting sequence features. Coupled with support vector machine (gkm-SVM), gkm-fvs have been used to achieve effective sequence-based predictions. However, the huge computation of a large kernel matrix prevents it from using large amount of data. It is unclear how to combine gkm-fvs with other data sources in the context of string kernel. On the other hand, the high dimensionality, colinearity, and sparsity of gkm-fvs hinder the use of many traditional machine learning methods without a kernel trick. Therefore, we proposed a flexible and scalable framework gkm-DNN to achieve feature representation from high-dimensional gkm-fvs using deep neural networks (DNN). We first proposed a more concise version of gkm-fvs, which significantly reduce the dimension of gkm-fvs. Then, we implemented an efficient method to calculate the gkm-fv of a given sequence at the first time. Finally, we adopted a DNN model with gkm-fvs as inputs to achieve efficient feature representation and a prediction task. Here, we took the transcription factor binding site prediction as an illustrative application and applied gkm-DNN onto 467 small and 69 big human ENCODE ChIP-seq datasets to demonstrate its performance and compared it with the state-of-the-art method gkm-SVM.

Promoter expression of HERV-K (HML-2) provirus-derived sequences is related to LTR sequence variation and polymorphic transcription factor binding sites

BackgroundIncreased transcription of the human endogenous retrovirus group HERV-K (HML-2) is often seen during disease. Although the mechanism of its tissue-specific activation is unclear, research shows that LTR CpG hypomethylation alone is not sufficient to induce its promoter activity and that the transcriptional milieu of a malignant cell contributes, at least partly, to differential HML-2 expression.ResultsWe analyzed the relationship between LTR sequence variation and promoter expression patterns in human breast cancer cell lines, finding them to be positively correlated. In particular, two proviruses (3q12.3 and 11p15.4) displayed increased activity in almost all tumorigenic cell lines sampled. Using a transcription factor binding site prediction algorithm, we identified two unique binding sites in each 5′ LTR that appeared to be associated with inducing promoter activity during neoplasia. Genomic analysis of the homologous proviruses in several non-human primates indicated post-integration genetic drift in two transcription factor binding sites, away from the ancestral sequence and towards the active form. Based on the sequences of 2504 individuals from the 1000 Genomes Project, the active form of the 11p15.4 site was found to be polymorphic within the human population, with an allele frequency of 51%, whereas the activating mutation in the 3q12.3 provirus was fixed in humans but not present in the orthologous provirus in chimpanzees or gorillas.ConclusionsThese data suggest that stage-specific transcription factors at least partly contribute to LTR promoter activity during transformation and that, in some cases, transcription factor binding site polymorphisms may be responsible for the differential HML-2 expression often seen between individuals.

MANTA2, update of the Mongo database for the analysis of transcription factor binding site alterations

Interpreting the functional impact of noncoding variants is an ongoing challenge in the field of genome analysis. With most noncoding variants associated with complex traits and disease residing in regulatory regions, altered transcription factor (TF) binding has been proposed as a mechanism of action. It is therefore imperative to develop methods that predict the impact of noncoding variants at TF binding sites (TFBSs). Here, we describe the update of our MANTA database that stores: 1) TFBS predictions in the human genome, and 2) the potential impact on TF binding for all possible single nucleotide variants (SNVs) at these TFBSs. TFBSs were predicted by combining experimental ChIP-seq data from ReMap and computational position weight matrices (PWMs) derived from JASPAR. Impact of SNVs at these TFBSs was assessed by means of PWM scores computed on the alternate alleles. The updated database, MANTA2, provides the scientific community with a critical map of TFBSs and SNV impact scores to improve the interpretation of noncoding variants in the human genome.

Base-pair resolution detection of transcription factor binding site by deep deconvolutional network.

Transcription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise TF binding sites (TFBSs) is essential for understanding the detailed mechanisms of TF-mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair resolution prediction of TFBS. In this paper, we propose DeepSNR, a Deep Learning algorithm for predicting TF binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of 'DeepBind' and we trained the entire model using TF-specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search-based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data. DeepSNR is available open source in the GitHub repository (https://github.com/sirajulsalekin/DeepSNR). Supplementary data are available at Bioinformatics online.

Investigation of single and synergic effects of NLRC5 and PD-L1 variants on the risk of colorectal cancer.

Constitutive activation of interferon signaling pathways has been reported in colorectal cancer (CRC), leading to a strong CD8+ T cell response through stimulation of NLRC5 expression. Primed CD8+ T cell expansion, however, may be negatively regulated by PD-L1 expression. Additionally, aberrant PD-L1 expression enables cancer cells to escape the immune attack. Our study aimed to select potential regulatory variants in the NLRC5 and PD-L1 genes by using several online in silico tools, such as UCSC browser, HaploReg, Regulome DB, Gtex Portal, microRNA and transcription factor binding site prediction tools and to investigate their influence on CRC risk in a Czech cohort of 1424 CRC patients and 1114 healthy controls. Logistic regression analysis adjusted for age and gender reported a moderate association between rectal cancer risk and two NLRC5 SNPs, rs1684575 T>G (OR: 1.60, 95% CI: 1.13–2.27, recessive model) and rs3751710 (OR: 0.70, 95% CI: 0.51–0.96, dominant model). Given that a combination of genetic variants, rather than a single polymorphism, may explain better the genetic etiology of CRC, we studied the interplay between the variants within NLRC5, PD-L1 and the previously genotyped IFNGR1 and IFNGR2 variants, to evaluate their involvement in the risk of CRC development. Overall we obtained 18 pair-wise interactions within and between the NLRC5 ad PD-L1 genes and 6 more when IFNGR variants were added. Thirteen out of the 24 interactions were below the threshold for the FDR calculated and controlled at an arbitrary level q*<0.10. Furthermore, the interaction IFNGR2 rs1059293 C>T—NLRC5 rs289747 G>A (P<0.0001) remained statistically significant even after Bonferroni correction. Our data suggest that not only a single genetic variant but also an interaction between two or more variants within genes involved in immune regulation may play important roles in the onset of CRC, providing therefore novel biological information, which could eventually improve CRC risk management but also PD-1-based immunotherapy in CRC.

TFClass: expanding the classification of human transcription factors to their mammalian orthologs.

TFClass is a resource that classifies eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs), available online at http://tfclass.bioinf.med.uni-goettingen.de. The classification scheme of TFClass was originally derived for human TFs and is expanded here to the whole taxonomic class of mammalia. Combining information from different resources, checking manually the retrieved mammalian TFs sequences and applying extensive phylogenetic analyses, >39 000 TFs from up to 41 mammalian species were assigned to the Superclasses, Classes, Families and Subfamilies of TFClass. As a result, TFClass now provides the corresponding sequence collection in FASTA format, sequence logos and phylogenetic trees at different classification levels, predicted TF binding sites for human, mouse, dog and cow genomes as well as links to several external databases. In particular, all those TFs that are also documented in the TRANSFAC® database (FACTOR table) have been linked and can be freely accessed. TRANSFAC® FACTOR can also be queried through an own search interface.

Genome-wide prediction of DNase I hypersensitivity using gene expression

We evaluate the feasibility of using a biological sample’s transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element’s neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome–transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

083 iRHOM2-p63 pathway mediates survival and redox balance in keratinocytes

The palmoplantar keratodermas are characterised by different patterns of hyperproliferative thickening of the palms and soles which are often painful. We have described dominant “gain of function” mutations in RHBDF2, the gene encoding inactive rhomboid protein 2 (iRHOM2), associated with the inherited syndrome Tylosis (Palmoplantar keratoderma) with Oesophageal Cancer (TOC). Recently, we have shown that genetic depletion of iRHOM2 leads to a much thinner mammalian footpad plus decreases keratinocyte hyperproliferation and migration. As little is known about the transcriptional regulation of iRHOM2, its promoter sequence was assessed using transcription factor binding site prediction software (genomatix.gsf.de). Here, we report that iRHOM2 is a novel target gene of p63 and that both p63 and iRHOM2 can differentially regulate cellular stress associated signalling pathways in keratinocytes. In normal keratinocytes, p63 regulates positively iRHOM2 with iRHOM2 then antagonizing p63 expression whilst, in hyperproliferative keratinocytes, there is an auto-regulatory feedback loop occurring between p63 and iRHOM2. Using human TOC keratinocytes and irhom2−/− mice, we demonstrate that p63-iRHOM2 mediated signalling regulates cellular functions including inflammation, survival and oxidative defence. For example, we identify survivin, a member of the inhibitor of apoptosis (IAP) gene family, as a novel binding partner of iRHOM2 and as a p63 target gene. In addition to increased cell survival via survivin expression, TOC keratinocytes showed increased level of reactive oxygen species (ROS) compared to control cells. We show a role for iRHOM2 in the epidermal oxidative defence response is via its interaction with cytoglobin (Cygb), a reported p63 target gene. In summary our data supports therapeutic strategies targeting iRHOM2-p63 axis in hyperproliferative skin disease and dysplasia.

DbCoRC: a database of core transcriptional regulatory circuitries modeled by H3K27ac ChIP-seq signals.

Core transcription regulatory circuitry (CRC) is comprised of a small group of self-regulated transcription factors (TFs) and their interconnected regulatory loops. Studies from embryonic stem cells and other cellular models have revealed the elementary roles of CRCs in transcriptional control of cell identity and cellular fate. Systematic identification and subsequent archiving of CRCs across diverse cell types and tissues are needed to explore both cell/tissue type-specific and disease-associated transcriptional networks. Here, we present a comprehensive and interactive database (dbCoRC, http://dbcorc.cam-su.org) of CRC models which are computationally inferred from mapping of super-enhancer and prediction of TF binding sites. The current version of dbCoRC contains CRC models for 188 human and 50 murine cell lines/tissue samples. In companion with CRC models, this database also provides: (i) super enhancer, typical enhancer, and H3K27ac landscape for individual samples, (ii) putative binding sites of each core TF across the super-enhancer regions within CRC and (iii) expression of each core TF in normal or cancer cells/tissues. The dbCoRC will serve as a valuable resource for the scientific community to explore transcriptional control and regulatory circuitries in biological processes related to, but not limited to lineage specification, tissue homeostasis and tumorigenesis.

Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility

BackgroundComputational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events.ResultsWe assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types.ConclusionIntegrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal “rules”. A computational tool was developed to predict TF binding sites based on the universal “rules”.

An efficient algorithm for improving structure-based prediction of transcription factor binding sites

BackgroundGene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks. Previously we developed a structure-based method for prediction of transcription factor binding sites using an integrative energy function that combines a knowledge-based multibody potential and two atomic energy terms. While the method performs well, it is not computationally efficient due to the exponential increase in the number of binding sequences to be evaluated for longer binding sites. In this paper, we present an efficient pentamer algorithm by splitting DNA binding sequences into overlapping fragments along with a simplified integrative energy function for transcription factor binding site prediction.ResultsA DNA binding sequence is split into overlapping pentamers (5 base pairs) for calculating transcription factor-pentamer interaction energy. To combine the results from overlapping pentamer scores, we developed two methods, Kmer-Sum and PWM (Position Weight Matrix) stacking, for full-length binding motif prediction. Our results show that both Kmer-Sum and PWM stacking in the new pentamer approach along with a simplified integrative energy function improved transcription factor binding site prediction accuracy and dramatically reduced computation time, especially for longer binding sites.ConclusionOur new fragment-based pentamer algorithm and simplified energy function improve both efficiency and accuracy. To our knowledge, this is the first fragment-based method for structure-based transcription factor binding sites prediction.

COMMON PATHWAYS BETWEEN AGING AND ALZHEIMER’S DISEASE REVEALED BY SYSTEMS-BIOLOGICAL APPROACH

Alzheimer's disease (AD) is one of the neurodegenerative diseases occurring over 65 years old. Aging is a risk factor of AD. Systems-biological analysis is potential tool for integration of biological information, for example on the pathogenesis of diseases. In particular, gene expression and protein-protein interaction (PPI) analysis have been very powerful in finding target molecules for the treatment of diseases. To identify therapeutic target molecules and biological pathways of AD and brain aging process, we have done PPI network analysis of proteins using microarray big data. Bioinformatics approaches including microarray analysis, transcription factor binding site prediction, and protein–protein interaction (PPI) network analysis were used in this study. We identified about 300 genes that were significantly differently expressed in the brain tissues of patients with AD and normal aging process. To identify hub molecules, we tried PPI analysis. The PPI network was found to include 27 molecules, which were found to be related to ‘hematopoietic cell lineage’, ‘cytokine-cytokine receptor interaction’, and ‘Jak-STAT signaling pathway’. These pathways might be important in the progression of AD and brain aging. In particular, the Jak-STAT signaling pathway, which is known to play a critical role in AD development, was up-regulated in both AD and aging. SOCS3, IL4R, and CSF3R were up-regulated, which was found to be involved in the Jak-STAT signaling pathway. Furthermore, the current study on RNA-Seq analysis of aged hippocampus revealed that pro-inflammatory genes were upregulated in brain aging process. Cytokine-cytokine interaction and Jak-STAT signaling pathway play an important role in the common mechanisms underlying both AD and brain aging. These systems-biological approach is powerful tool for discovery of new key molecules involved in AD.

Abstract 629: IFN-Gamma-Dependent Repression of TET2 in Allograft Vasculopathy Exacerbates VSMC Activation

Coronary allograft vasculopathy (CAV) occurs in 50% of heart transplant recipients at 10 years after surgery. Allograft failure secondary to CAV accounts for 30% of deaths in heart transplant recipients. CAV is characterized by concentric intimal hyperplasia (IH) in the graft vasculature that causes ischemic injury to the myocardium. T cell-derived INF-γ is a well-established driver of IH in CAV. The molecular mechanisms of IFN-γ-dependent phenotypic modulation of vascular smooth muscle cells (VSMCs) in CAV are not fully elucidated. Our group recently showed that TET methylcytosine dioxygenase 2 (TET2) is a master regulator of VSMC phenotype. We hypothesized that IFN-γ modulates TET2 expression and/or function in VSMCs in CAV. We found, using a minor histocompatibility mismatch aorta graft model of allograft vasculopathy, that TET2 expression was decreased in the graft neointima relative to aortic VSMCs prior to grafting. Lineage tracing studies showed that TET2 activity was decreased in donor VSMC-derived cells present in the neointima. Nuclear localization of phospho-STAT1 correlated with TET2 repression in these cells suggesting that IFN-γ signaling may mediate repression of TET2. Further, we found that IFN-y was sufficient to repress TET2 expression in human and mouse VSMCs in vitro. TET2 repression by IFN-γ was not repressed by rapamycin, an mTOR inhibitor known to be effective against CAV but which has significant side effects. We hypothesized that IFN-γ signaling directly represses TET2. IFN-γ rapidly induced activation of STAT1 in VSMCs in vitro. Transcription factor binding site prediction suggested potential STAT1 binding sites on the TET2 promoter in both human and mouse. ChIP-PCR confirmed that STAT1 occupancy occurs at the TET2 promoter in response to IFN-γ. Studies are ongoing to determine if STAT1 directly negatively regulates TET2 transcription in response to IFN-γ, and if TET2 overexpression can rescue the effect of IFN-γ on VSMC activation and IH in in vivo models of allograft vasculopathy. Since IFN-y-dependent repression of TET2 is not reversed by rapamycin, elucidating this pathway represents an opportunity for developing complementary therapies to mTOR inhibition that may result in improved outcomes and fewer side effects.

An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data

Transcription factors are proteins that bind to specific DNA sequences and play important roles in controlling the expression levels of their target genes. Hence, prediction of transcription factor binding sites (TFBSs) provides a solid foundation for inferring gene regulatory mechanisms and building regulatory networks for a genome. Chromatin immunoprecipitation sequencing (ChIP-seq) technology can generate large-scale experimental data for such protein-DNA interactions, providing an unprecedented opportunity to identify TFBSs (a.k.a. cis-regulatory motifs). The bottleneck, however, is the lack of robust mathematical models, as well as efficient computational methods for TFBS prediction to make effective use of massive ChIP-seq data sets in the public domain. The purpose of this study is to review existing motif-finding methods for ChIP-seq data from an algorithmic perspective and provide new computational insight into this field. The state-of-the-art methods were shown through summarizing eight representative motif-finding algorithms along with corresponding challenges, and introducing some important relative functions according to specific biological demands, including discriminative motif finding and cofactor motifs analysis. Finally, potential directions and plans for ChIP-seq-based motif-finding tools were showcased in support of future algorithm development.

RETRACTED: Long Noncoding RNA LINC00673 Is Activated by SP1 and Exerts Oncogenic Properties by Interacting with LSD1 and EZH2 in Gastric Cancer

Long noncoding RNAs (lncRNAs) have emerged as important regulators in a variety of human diseases, including cancers. However, the biological function of these molecules and the mechanisms responsible for their alteration in gastric cancer (GC) are not fully understood. In this study, we found that lncRNA LINC00673 is significantly upregulated in gastric cancer. Knockdown of LINC00673 inhibited cell proliferation and invasion and induced cell apoptosis, whereas LINC00673 overexpression had the opposite effect. Online transcription factor binding site prediction analysis showed that there are SP1 binding sites in the LINC00673 promoter region. Next, luciferase reporter and chromatin immunoprecipitation (ChIP) assays provided evidence that SP1 could bind directly to the LINC00673 promoter region and activate its transcription. Moreover, mechanistic investigation showed that CADM4, KLF2, and LATS2 might be the underlying targets of LINC00673 in GC cells, and RNA immunoprecipitation, RNA pull-down, and ChIP assays showed that LINC00673 can interact with EZH2 and LSD1, thereby repressing KLF2 and LATS2 expression. Taken together, these findings show that SP1-activated LINC00673 exerts an oncogenic function that promotes GC development and progression, at least in part, by functioning as a scaffold for LSD1 and EZH2 and repressing KLF2 and LATS2 expression.