TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions

Schmeier, Sebastian; Alam, Tanvir; Essack, Magbubah; Bajic, Vladimir B.

doi:10.1093/nar/gkw1007

Cited by 71 publications

(76 citation statements)

References 57 publications

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“…Human transcription factors (TFs) were downloaded from TcoF-DB v2 44 . Lineage coordinated TF networks were constructed following Fletcher et al 45…”

Section: Lineage Coordinated Tf Networkmentioning

confidence: 99%

Integrated decoding hematopoiesis and leukemogenesis at single-cell resolution and its clinical implication

Qin

Pang

Hou

et al. 2020

Preprint

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BMMCs contain much more subpopulations and are more active (Extended Data Fig. 1g-1j). HSPCs form a single connected entity on tSNE projectionDue to the limited number of HSPCs in BMMCs (Fig. 1b), CD34+ cells, representing HSPCs, were enriched by fluorescence-activated cell sorting (FACS) for investigating hematopoiesis (Extended Data Fig. 2a). The HSPCs essentially formed a single connected entity extending in several directions on tSNE projection and were clustered into 27 clusters for better understanding of the hematopoietic process ( Fig. 1e). We inferred the cell type of each cluster by checking the expression of hematopoietic lineage specific genes ( Fig. 1e, Extended Data Fig. 2b), such as HSC (EMCN+, THY1+, MEG3+, HES1+, Lin-; cluster 1), megakaryocytic progenitors (MkP) (PF4+, GP9+; clusters 5, 7), early erythroid progenitors (EEP) (APOE+, CD36+, CA1+; clusters 8-11), neutrophil, monocyte and DC progenitors (CSF3R+, MPO+ and LYZ+; clusters 13-20) (Fig. 1f, Extended Data Fig. 2b), lymphoid progenitors (CD79A+, IGHM+, VPREB1+; clusters 21-27) ( Fig. 1F and Extended Data Fig. 2b)Cluster 12 highly expressed mast cell and basophil specific genes including HDC, TPSAB1 and MS4A2, as well as eosinophil specific genes including PRG2 and CPA3 ( Fig. 1f and Extended Data Fig. 2b), which could be recently reported Basophil/Eosinophil/Mast progenitors (Ba/Eo/MaP) 10,16 . We did not detect any cluster with gene expression patterns similar to common myeloid progenitor (CMP) (CD34+, CD38+, CD123+, CD45RA-, CD10-and Lin-), consistent with recent studies showing that CMP is a heterogeneous mixture of erythroid and myeloid primed progenitors 10,17,18 . Moreover, we observed multiple subpopulations within pre-defined MkP, EEP, GMP, Pro-B II and so on (Fig. 1e).The expression levels of many genes are gradually changing along the three EEP populations, among which the expression levels of HBA1, TFRC, GYPA, ALAS2, PLK1 and MKI67 gradually increased as the distance to HSC increase (Extended Data Fig. 2b, 2i, 2j). Overall, HSPCs contain a substantial higher fraction of cells in active cell cycles and cell states than that of other BMMCs (Extended Data Fig. 2c-2h). Interestingly, major early stem and progenitor cells (HSC, MPP, LMPP) are in resting phase while major later progenitors are in active proliferation (Extended Data Fig. 2e, 2f), potentially indicating the early progenitor constitutes the major cell pool for regulating hematopoiesis while later progenitors are in simple transitional states. Continuous hematopoietic lineages with hierarchical structureWe implemented Slingshot 19 and SPRING 20 on HSPCs to conduct pseudo-time inference.Pseudo-time ordering of HSPCs exhibits a tree-like structure in which HSC forms the root, from which seven lineages gradually emerged with a hierarchical structure ( Fig. 2a, 2b, 2c), essentially consistent with the cell lineages base on PCA projection (Extended Data Fig. 3a). The results are consistent with recent reports that hematopoiesis is a continuous process 8-10 , but with...

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“…Human transcription factors (TFs) were downloaded from TcoF-DB v2 44 . Lineage coordinated TF networks were constructed following Fletcher et al 45…”

Section: Lineage Coordinated Tf Networkmentioning

confidence: 99%

Integrated decoding hematopoiesis and leukemogenesis at single-cell resolution and its clinical implication

Qin

Pang

Hou

et al. 2020

Preprint

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“…Since the probability to find known motifs by chance is low, these matching motifs are likely to be true motifs, and may cooperate with the primary motifs in transcriptional regulation. 3M~3O and Table 2) (Schaefer et al 2011;Schmeier et al 2017) (see Table S4 for all known cooperative factors of the 21 target TFs), although Homer (3.5) outperformed ProSampler (2.7) in G1.…”

Section: Comparison Of the Programs For Identifying Cooperative Motifmentioning

confidence: 99%

“…5E and 5I), thus TFC4 is less likely to bind the motifs of the latter two TFs. Nonetheless, LEF1 is a cooperator of TCF4 (Schaefer et al 2011;Schmeier et al 2017), therefore, it is likely that physical interaction between TCF4 and LEF1 might result in the pulldown of the sequences in the SN subset. In the dataset GSM1505760 of SOX17 with the "0 1" pattern, where the primary motif of the target TF was not found (Fig.…”

Section: Patterns Of Motif Occurrence In Chip-seq Datasetsmentioning

confidence: 99%

“…An average of 20.1 known cooperative TFs have been documented in TcoF-DB (Schaefer et al 2011;Schmeier et al 2017) for the 21 target TFs (Table S4), ranging from 0 (for EOMES) to 103 (for CMYC). For the 20 target TFs with at least one known cooperative TF, ProSampler identified motifs of an average of 5.7 (27.0%) of the known cooperative TFs ( Table 4).…”

Section: Predicting Known and Novel Cooperative Motifsmentioning

confidence: 99%

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Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

Zhang

et al. 2018

Preprint

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The availability of a large volume of chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets for various transcription factors (TF) has provided an unprecedented opportunity to identify all functional TF binding motifs clustered in the enhancers in genomes. However, the progress has been largely hindered by the lack of a highly efficient and accurate tool that is fast enough to find not only the target motifs, but also cooperative motifs contained in very large ChIP-seq datasets with a binding peak length of typical enhancers (~1,000 bp). To circumvent this hurdle, we herein present an ultra-fast and highly accurate motif-finding algorithm, ProSampler, with automatic motif length detection. ProSampler first identifies significant k-mers in the dataset and combines highly similar significant k-mers to form preliminary motifs.ProSampler then merges preliminary motifs with subtle similarity using a novel graph-based Gibbs sampler to find core motifs. Finally, ProSampler extends the core motifs by applying a two-proportion z-test to the flanking positions to identify motifs longer than k. As the number of preliminary motifs is much smaller than that of kmers in a dataset, we greatly reduce the search space of the Gibbs sampler compared with conventional ones. By storing flanking sequences in a hash table, we avoid extensive IO and the necessity of examining all lengths of motifs in an interval. When evaluated on both synthetic and real ChIP-seq datasets, ProSampler runs orders of magnitude faster than the fastest existing tools while more accurately discovering primary motifs as well as cooperative motifs than do the best existing tools. Using ProSampler, we revealed previously unknown complex motif occurrence patterns in large ChIP-seq datasets, thereby providing insights into the mechanisms of cooperative TF binding for gene transcriptional regulation. Therefore, by allowing fast and accurate mining of the entire ChIP-seq datasets, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

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“…">Transcription factors (TFs)TFs (n = 387) in Ciona savignyi were downloaded from DBD (database of predicted transcription factors) (Charoensawan et al, 2010). TFs (n = 1506) in Mus musculus were downloaded from TcoF-DB v2 (Schmeier et al, 2017).…”

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confidence: 99%

A single-cell analysis of the molecular lineage of chordate embryogenesis

Zhang

Imai

et al. 2020

Preprint

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Highlights• Transcriptome profiles of 47 cell types across 8 stages in early chordate embryo • Bipotent mother in asymmetric division shows the default daughter fate • Modular structure of the notochord GRN beyond the known function of T • Invariant lineage and manual cell isolation provide truth to trajectory analysisWe also compared our results with a recently published high throughput single-cell analysis of C. intestinalis (Cao et al., 2019a). The published study used the 10x Genomics platform to sample the 110-cell stage at 26x coverage (equivalent of 26 embryos) and reported 14 cell types, compared to 16 cell types in our study from 3 embryos. Specifically, we resolved three tissue subtypes including the A-line nerve cord, B-line mesenchyme and B-line muscle. The separation of these cell types is supported by clear differences in the expression of 11~31 DEGs and 1~2 known markers ( Figure S5a-c). Meanwhile, our clustering did not resolve the A-early C. intestinalis embryo (Imai et al., 2006) that have clear homologs in C. savignyi (referred to as Imai genes below). We evaluated two aspects of our data: DEG calling and consistency of expressed sites. First, among the 37 genes that showed differential expression by in situ, 23, or 62%, are identified as DEGs in our data (Figure 3f, gene names in red). Among the 14 genes that did not show differential expression by in situ, 12, or 86%, are not defined as DEGs in our data. Second, we compared the consistency of expression sites between the two datasets. Taking each gene in each cell type as an expression site, only 8% of expression sites of our defined DEGs show discrepancy with those of in situ (Figure 3f, gene names in red). We then focused on 51 Imai genes at the 64-cell stage, among which 33 genes show largely consistent patterns ( Figure 3f and S10, HQ/ high-quality), including TTF-1, Brachyury, MyoD in the endoderm, notochord and muscle lineages. Slight differences also exist, such as for Lhx3, which is detected by scRNA-seq but not by in situ in B7.5, the TVC precursor. Similarly, Lhx3 is detected in A7.6 by scRNA-seq but not by in situ. We observed the opposite pattern as well, such as E(spl)/hairy-a being detected by in situ but not by scRNA-seq in B7.5. Interestingly, Lhx3 was reported to be required for TVC specification (Christiaen et al., 2009a), which supports the detection by scRNA-seq. For all stages, about 60% of the Imai genes show high degree of agreement between the two detection methods (Figure 3g). Among these high-quality genes, 78% display <10% unmatched sites between in situ and scRNA-seq ( Figure 3h). Orthogonal evidence, such as genetic analysis in the case of Lhx3 in B7.5, is needed to resolve the discrepancy. About 40% of the Imai genes show relatively large inconsistency (Figure 3f), which can be further divided into three situations. At 64-cell stage, 14 Imai genes showed predominantly false-positive sites (Figure 3f, LQ-1) in our data, which show cell-type specificity by in situ but were ubiquitously detected by scRNA-seq in almo...

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TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions

Cited by 71 publications

References 57 publications

Integrated decoding hematopoiesis and leukemogenesis at single-cell resolution and its clinical implication

Integrated decoding hematopoiesis and leukemogenesis at single-cell resolution and its clinical implication

Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

A single-cell analysis of the molecular lineage of chordate embryogenesis

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