TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors

Chawla, Konika; Tripathi, Sushil; Thommesen, Liv; Lægreid, Astrid; Kuiper, Martin

doi:10.1093/bioinformatics/btt432

Cited by 95 publications

(98 citation statements)

References 15 publications

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“…The top ten of each list can be found in Figures 1E and S1I, and the full list is in Table S3. We then selected only those genes that have been reported to function as transcription factors using the TFcheckpoint database (Table S4) (Chawla et al., 2013). Several genes and transcription factors found here have never been reported as markers for specific cell types of the human pancreas (Figure 1E).…”

Section: Resultsmentioning

confidence: 99%

A Single-Cell Transcriptome Atlas of the Human Pancreas

et al. 2016

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SummaryTo understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.

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Section: Resultsmentioning

confidence: 99%

A Single-Cell Transcriptome Atlas of the Human Pancreas

et al. 2016

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“…Documented information about the functional interaction of DbTFs with their target genes is added from: a) the PAZAR database [40], an open source framework that serves as an umbrella to bring together datasets pertaining to transcription factors and regulatory sequence annotations; b) the Human Transcriptional Regulation Interactions (HTRI) database [41], an open-access database that serves as a repository for experimentally verified human transcription factor - target gene interactions; c) TFactS [42], a database that catalogs curated transcription factor - target gene interactions; and d) TFcheckpoint [43], a database that compiles curated information on human, rat and mouse DbTF candidates from many different database resources. As described above (step 1), entities from these resources are filtered based on the ID mapping file provided by UniprotKB.…”

Section: Methodsmentioning

confidence: 99%

Finding gene regulatory network candidates using the gene expression knowledge base

et al. 2014

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BackgroundNetwork-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.ResultsWe have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.ConclusionsSemantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.

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“…Transcription factors (TFs) play an important role in the gene regulatory network. We used 2,020 TF genes defined in TFcheckpoint database [27], in which TF information is collected from 9 different resources followed by manual inspections for sequence-specific DNA-binding RNA polymerase II TF.…”

Section: Housekeeping Transcription Factor and Tumor-associated Genesmentioning

confidence: 99%

“…Further, we categorized genes that have possible sponge effect (>5 miRNA binding sites in the 3ʹUTR) into housekeeping (HK), tumor-associated (tumor suppressors or oncogenes, TA), and transcription factor (TF). Based on 3,804 HK [26], 932 TA [28], and 1,020 TF genes [27] curated in public databases (see Methods), the ceRNA networks consist of 3-fold more HK genes than TA or TF genes (Fig. 3A for normal and S. Fig.…”

Section: Housekeeping Genes Keep Er+ and Er-normal Cerna Network To mentioning

confidence: 99%

3′-UTR shortening contributes to subtype-specific cancer growth by breaking stable ceRNA crosstalk of housekeeping genes

Fan

Kim

Bai

et al. 2019

Preprint

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ABSTRACTShortening of 3′UTRs (3′US) through alternative polyadenylation (APA) is a post-transcriptional mechanism that regulate expression of hundreds of genes in human cancers. In breast cancer, different subtypes of tumor samples, such as estrogen receptor positive and negative (ER+ and ER-), are characterized by distinct molecular mechanisms, suggesting possible differences in the post-transcriptional regulation between the subtype tumors. In this study, based on the profound tumorigenic role of 3′US interacting with competing-endogenous RNA (ceRNA) network (3′US-ceRNA effect), we hypothesize that the 3′US-ceRNA effect drives subtype-specific tumor growth. However, we found that the subtypes are available in different sample size, biasing the ceRNA network size and disabling the fair comparison of the 3′US-ceRNA effect. Using normalized Laplacian Matrix Eigenvalue Distribution, we addressed this bias and built the tumor ceRNA networks comparable between the subtypes. Based on the comparison, we identified a novel role of housekeeping (HK) genes as stable and strong miRNA sponges (sponge HK genes) that synchronize the ceRNA networks of normal samples (adjacent to ER+ and ER- tumor samples). We further found that distinct 3′US events in the ER- tumor break the stable sponge effect of HK genes in a subtype-specific fashion, especially in association with the aggressive and metastatic phenotypes. Knockdown of NUDT21, a master 3′-UTR regulator in HeLa cells, confirmed the causal role of 3′US-ceRNA effect repressing HK genes for tumor growth. In this study, we identified 3′US-ceRNA effect on the sponge HK genes for subtype-specific growth of ER- tumors.

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TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors

Abstract: The TFcheckpoint database is freely available at www.tfcheckpoint.org

Cited by 95 publications

References 15 publications

A Single-Cell Transcriptome Atlas of the Human Pancreas

A Single-Cell Transcriptome Atlas of the Human Pancreas

Finding gene regulatory network candidates using the gene expression knowledge base

3′-UTR shortening contributes to subtype-specific cancer growth by breaking stable ceRNA crosstalk of housekeeping genes

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