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Transcription Factor

Transcription Factors are proteins that regulate gene expression by binding to specific DNA sequences, known as transcription factor binding sites, within gene promoters or enhancers.
They play a crucial role in cellular processes such as development, differentiation, and response to external stimuli.
Transcription Factors can act as activators or repressors, enhancing or inhibiting the transcription of target genes.
Identifying and understanding Transcription Factors is essential for studying gene regulation and developing therapies for diseases associated with dysregulated gene expression.
PubCompare.ai can optimize your Transcription Factor research by using AI-driven comparisons to help you locate the best protocols from literature, pre-prints, and patents, making it easier to identify reproducible and reliable methods to advance your studies with ease.

Most cited protocols related to «Transcription Factor»

1
Automated ANNOVAR Variant Annotation
Cited 2490 times
While reading variants from input file, ANNOVAR scans the gene annotation database stored at local disk, and identifies intronic variants, exonic variants, intergenic variants, 5′/3′-UTR variants, splicing site variants and upstream/downstream variants (less than a threshold away from a transcript, by default 1 kb). For intergenic variants, the closest two genes and the distances to them are reported. For exonic variants, ANNOVAR scans annotated mRNA sequences to identify and report amino acid changes, as well as stop-gain or stop-loss mutations. ANNOVAR can also perform region-based annotations on many types of annotation tracks, such as the most conserved elements and the predicted transcription factor binding sites. These annotations must be downloaded by ANNOVAR, before they can be utilized. Finally, ANNOVAR can filter specific variants such as SNPs with >1% frequency in the 1000 Genomes Project, or non-synonymous SNPs with SIFT scores >0.05.
To automate the procedure of reducing large amounts of variants into a small subset of functionally important variants, a script (auto_annovar.pl) is provided in the ANNOVAR package. By default, auto_annovar.pl performs a multi-step procedure by executing ANNOVAR multiple times, each time with several different command line parameters, and generates a final output file containing the most likely causal variants and their corresponding candidate genes. For recessive diseases, this list can be further trimmed down to include genes with multiple variants that are predicted to be functionally important.
Wang K., Li M, & Hakonarson H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), e164.
Publication 2010
5' Untranslated Regions Amino Acids Binding Sites Exons Gene Annotation Genes Genetic Diversity Genome Introns Mutation Radionuclide Imaging RNA, Messenger Single Nucleotide Polymorphism Transcription Factor
2
Extensive Genomic Variant Annotation Pipeline
Cited 637 times
We used the Ensembl Variant Effect Predictor (VEP, Ensembl Gene annotation v68)16 (link) to obtain gene model annotation for single nucleotide and indel variants. For single nucleotide variants within coding sequence, we also obtained SIFT7 (link) and PolyPhen-26 (link) scores from VEP. We combined output lines describing MotifFeatures with the other annotation lines, reformatted it to a pure tabular format and reduced the different Consequence output values to 17 levels and implemented a four-level hierarchy in case of overlapping annotations (see Supplementary Note). To the 6 VEP input derived columns (chromosome, start, reference allele, alternative allele, variant type: SNV/INS/DEL, length) and 26 actual VEP output derived columns, we added 56 columns providing diverse annotations (e.g. mapability scores and segmental duplication annotation as distributed by UCSC51 (link),52 (link); PhastCons and phyloP conservation scores53 (link) for three multi-species alignments9 (link) excluding the human reference sequence in score calculation; GERP++ single-nucleotides scores, element scores and p-values54 (link), also defined from alignments with the human reference excluded; background selection score40 (link),55 (link); expression value, H3K27 acetylation, H3K4 methylation, H3K4 trimethylation, nucleosome occupancy and open chromatin tracks provided for ENCODE cell lines in the UCSC super tracks52 (link); genomic segment type assignment from Segway56 (link); predicted transcription factor binding sites and motifs11 (link); overlapping ENCODE ChIP-seq transcription factors11 (link), 1000 Genome variant14 (link) and Exome Sequencing Project57 (link) variant status and frequencies, Grantham scores20 (link) associated with a reported amino acid substitution). The Supplementary Note provides a full description and Supplementary Table 1 lists all columns of the obtained annotation matrix.
Kircher M., Witten D.M., Jain P., O’Roak B.J., Cooper G.M, & Shendure J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature genetics, 46(3), 310-315.
Publication 2014
Acetylation Alleles Amino Acid Substitution Binding Sites Cell Lines Chromatin Chromatin Immunoprecipitation Sequencing Chromosomes Gene Annotation Genome Homo sapiens INDEL Mutation Methylation Nucleosomes Nucleotides Open Reading Frames Segmental Duplications, Genomic Transcription, Genetic Transcription Factor
3
Genome-wide ChIP-Seq Data Analysis
Cited 552 times

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Publication 2013
Base Pairing Chromatin Immunoprecipitation Sequencing Genes Genome Genome, Human Macrophage-1 Antigen Mus Transcription Factor
4
ChIP-Seq Analysis for Enhancer Identification
Cited 283 times

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Publication 2013
Base Pairing Cells Chromatin Immunoprecipitation Sequencing Enhanced S-Cone Syndrome Genes Genome Genome, Human Homo sapiens Macrophage-1 Antigen Mus Transcription Factor
5
Transcription Factor Target Identification via ChIP-seq and Co-expression Analysis
Cited 245 times
A gold standard for transcription factor targets were obtained from the study by Chen et al. (2008 (link)). The article defined an association score between each gene and a transcription factor that varied from 0 to 1. As scores showed a clear bimodal distribution, we considered all genes with a score >0.6 to be targets of the corresponding transcription factor.
In the MEM webtool (http://biit.cs.ut.ee/mem), we selected 12 datasets on mouse Affymetrix platform 430 2.0. Since the ChIP-seq study was performed on mouse embryonic stem (ES) cells, we selected only the datasets that mentioned ES cell in their description. The list of datasets is available in the Supplementary Material.
We queried each of the transcription factors, for which we had binding information, separately. The MEM webtool performed a similarity search on each dataset using correlation distance between the transcription factor and other genes. The resulting lists of correlated genes were used in aggregation and assessing the AUC.
To combine the data from ChIP-seq with co-expression queries, we translated all the results into Ensembl gene identifiers using g:Convert (Reimand et al., 2007 (link)).
Kolde R., Laur S., Adler P, & Vilo J. (2012). Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, 28(4), 573-580.
Publication 2012
Chromatin Immunoprecipitation Sequencing Embryonic Stem Cells Genes Gold Mouse Embryonic Stem Cells Mus Transcription Factor

Most recents protocols related to «Transcription Factor»

1
Candidate Gene Screening for Tobacco Mutants
Not available on PMC !

Example 5

Three tobacco lines, FC401 wild type (Wt); FC40-M207 mutant line fourth generation (M4) and FC401-M544 mutant line fourth generation (M4) were used for candidate gene screening. Low anatabine traits were confirmed for the two tobacco mutant lines (M207 and M544) in root and leaf before screening (see FIG. 3).

RNA was extracted from root tissues of wild type (Wt) FC401, M207 and M544 with RNeasy Plus Mini kit from Quiagen Inc. following the manufacturer's protocol. cDNA libraries were prepared from the RNAs using In-Fusion® SMARTer® Directional cDNA Library Construction Kit from Clontech Inc. cDNA libraries were diluted to 100 ng/μl and used as the template for candidate gene PCR screening.

PCR amplifications were performed in 50 μl final volumes that contained 50-100 ng of template DNA (i.e., the cDNA library) and 0.2 μM of primers (Fisher Scientific) using the Platinum® Taq DNA Polymerase High Fidelity kit (Life Technology Inc.). Thermocycling conditions included a 5 min incubation at 94° C.; followed by 34 cycles of 30 seconds at 94° C., 30 seconds at 58° C., 1 min 30 seconds at 68° C.; with a final reaction step of 68° C. for 7 mins. The PCR products were evaluated by agarose gel electrophoresis, and desired bands were gel purified and sequenced using an ABI 3730 DNA Analyzer (ABI).

51 candidate genes (listed in Table 4) were cloned from F401, Wt, M207 and M544 lines, and sequenced for single nucleotide polymorphism (SNP) detection.

TABLE 4
Listing of Candidate Genes for Screening
Quinolinate Synthase A-1Pathogenesis related protein 1
Allene oxide synthaseAllene oxide cyclase
ET861088.1 Methyl esteraseFH733463.1 TGACG-sequence specific transcription factor
FH129193.1 Aquaporin-TransportFH297656.1 Universal stress protein
Universal stress protein Tabacum sequenceFH077657.1 Scarecrow-like protein
FH864888.1 EIN3-binding F-box proteinFH029529.1 4,5 DOPA dioxygenase
FI010668.1 Ethylene-responsive transcription EB430189 Carboxylesterase
factor
DW001704 Glutathione S transferaseEB683763 Bifunctional inhibitor/lipid transfer protein/seed
storage 2S albumin
DW002318 Serine/threonine protein kinaseDW004086 Superoxide dismutase
DW001733 Lipid transfer protein DIRIDW001944 Protein phosphatase 2C
DW002033EB683763 Bifunctional inhibitor/lipid transfer protein/seed
storage 2S albumin
DW002318 Serine/threonine protein kinaseDW002576 Glycosyl hydrolase of unknown function DUF1680
EB683279EB683763
EB683951FG141784 (FAD Oxidoreductase)
BBLa-Tabacum sequencesBBLb
BBLeBBLd
PdrlPdr2
Pdr3Pdr5a
Pdr5bNtMATEl
NtMATE2NtMATE3
WRKY8EIG-I24
WRKY3WRKY9
EIG-E17AJ748263.1 QPT2 quinolinate phosphoribosyltransferase
AJ748262.1 QPT1

US11873500B2. Genetic locus imparting a low anatabine trait in tobacco and methods of using (2024-01-16). ALTRIA CLIENT SERVICES LLC [US]. Inventors: Chengalrayan Kudithipudi [US], Alec J. Hayes [US], Robert Frank Hart [US], Yanxin Shen [US], Jesse Frederick [US], Dongmei Xu [US].
Full text: Click here
Patent 2024
Albumins allene oxide cyclase allene oxide synthase Amino Acid Sequence anatabine Carboxylesterase cDNA Library Dioxygenases Dopa Electrophoresis, Agar Gel Esterases Ethylenes Genes Glutathione S-Transferase Heat Shock Proteins Histocompatibility Testing Hydrolase lipid transfer protein Neoplasm Metastasis Nicotiana Nicotinate-nucleotide pyrophosphorylase (carboxylating) NOS1 protein, human Oligonucleotide Primers Oxidoreductase pathogenesis Plant Leaves Plant Roots Platinum Protein-Serine-Threonine Kinases Protein-Threonine Phosphatase Protein Kinases protein methylesterase Protein Phosphatase Protein Phosphatase 2C Proteins Quinolinate RNA Single Nucleotide Polymorphism Superoxide Dismutase Synapsin I Taq Polymerase Transcription, Genetic Transcription Factor Transfer Factor Water Channel
2
Quantifying IL-2 Effects on T Cell Activation
Not available on PMC !

Example 7

Impact of IL-2 signalling on Teff responses is characterised in a T cell activation assay, in which intracellular granzyme B (GrB) upregulation and proliferation are examined. Previously frozen primary human Pan T cells (Stemcell Technologies) are labelled with eFluor450 cell proliferation dye (Invitrogen) according to manufacturer's recommendation, and added to 96-U-bottom well plates at 1×105 cells/well in RPMI 1640 (Life Technologies) containing 10% FBS (Sigma), 2 mM L-Glutamine (Life Technologies) and 10,000 U/ml Pen-Strep (Sigma). The cells are then treated with 10 μg/ml anti-CD25 antibodies or control antibodies followed by Human T-Activator CD3/CD28 (20:1 cell to bead ratio; Gibco) and incubated for 72 hrs in a 37° C., 5% CO2 humidified incubator. To assess T cell activation, cells are stained with the eBioscience Fixable Viability Dye efluor780 (Invitrogen), followed by fluorochrome labelled antibodies for surface T cell markers (CD3-PerCP-Cy5.5 clone UCHT1 Biolegend, CD4-BV510 clone SK3 BD Bioscience, CD8-Alexa Fluor 700 clone RPA-T8 Invitrogen, CD45RA-PE-Cy7 clone HI100 Invitrogen, CD25-BUV737 clone 2A3 BD Bioscience) and then fixed and permeabilized with the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set (Invitrogen) before staining for intracellular GrB and intranuclear FoxP3 (Granzyme B-PE clone GB11 BD Bioscience, FoxP3-APC clone 236A/E7). Samples are acquired on the Fortessa LSR X20 Flow Cytometer (BD Bioscience) and analysed using the BD FACSDIVA software. Doublets are excluded using FCS-H versus FCS-A, and lymphocytes defined using SSC-A versus FCS-A parameters. CD4+ and CD8+ T cell subsets gated from the live CD3+ lymphocytes are assessed using a GrB-PE-A versus proliferation eFluor450-A plot. Results are presented as percentage of proliferating GrB positive cells from the whole CD4+ T cell population. Graphs and statistical analysis is performed using GraphPad Prism v7. (results not shown)

US11873341B2. Anti-CD25 for tumour specific cell depletion (2024-01-16). Tusk Therapeutics Ltd. [GB], Cancer Research Technology Limited [GB]. Inventors: Anne Goubier [GB], Beatriz Goyenechea Corzo [GB], Josephine Salimu [GB], Kevin Moulder [GB], Pascal Merchiers [GB], Sergio Quezada [GB].
Full text: Click here
Patent 2024
Anti-Antibodies Antibodies Biological Assay Buffers CD4 Positive T Lymphocytes Cell Proliferation Cells Clone Cells CY5.5 cyanine dye Eragrostis Fluorescent Dyes Freezing Glutamine GZMB protein, human Homo sapiens IL2RA protein, human Lymphocyte prisma Protoplasm Stem Cells Streptococcal Infections T-Lymphocyte T-Lymphocyte Subsets Transcriptional Activation Transcription Factor
3
Exosome-Mediated Tbx18 Delivery
Not available on PMC !

Example 18

The binding of CIBN and CRY2 in cells expressing CIBN-EGFP-CD9 and Tbx18-mCherry-Cry2 at 488 nm wavelength blue light, and the loading of Tbx18 within the exosome is evaluated.

For the massive production of Tbx18-loaded exosomes, cells stably expressing CIBN-EGFP-CD9 gene and Tbx18-mCherry-CRY2 gene are established, and exosomes are isolated and purified by Tangential Flow Filtration (TFF) method from culture supernatant.

Functional analysis of Tbx18-loaded exosomes is performed in target cells:

Target cells are treated with the Tbx18-loaded exosomes to show the functional activity.

Animal models are administered with the Tbx18-loaded exosomes by i.p. or i.v. to show therapeutic effect.

US11872193B2. Compositions containing protein loaded exosome and methods for preparing and delivering the same (2024-01-16). ILIAS BIOLOGICS INC. [KR]. Inventors: Chulhee Choi [KR], Nambin Yim [KR], Won Do Heo [KR], Seung-Wook Ryu [KR], Hojun Choi [KR], Kyungsun Choi [KR].
Full text: Click here
Patent 2024
Animal Model Cells Exosomes Filtration Genes Light Therapeutic Effect Transcription Factor
4
Tobacco RAX Genes in Axillary Bud Formation
Not available on PMC !

Example 28

As transcription factors, REGULATOR OF AXILLARY MERISTEMS (RAX) play an important role in the formation of branch meristems. In tobacco, there are two RAX genes: RAX1 (SEQ ID NOs: 75 and 76) and RAX2 (SEQ ID NOs: 77 and 78).

RAX1 (SEQ ID NO: 75) and RAX2 (SEQ ID NO: 77) are knocked out in separate tobacco lines. The knockout mutant of RAX1 show the mislocalization of axillary buds in leaf axil (see FIG. 28, upper right panel), but after topping the axillary buds demonstrate normal growth characteristics and phenotype in the mislocalized position (see FIG. 28, upper panel left). However, the knockout mutant of RAX2 delays axillary bud outgrowth for approximately two weeks after topping (see FIG. 28, lower panels). Thus, both RAX1 and RAX2 are functionally related to axillary formation and axillary bud out-growth.

US11879130B2. Compositions and methods for producing tobacco plants and products having reduced or eliminated suckers (2024-01-23). ALTRIA CLIENT SERVICES LLC [US]. Inventors: Dongmei Xu [US], Jesse Frederick [US], Chengalrayan Kudithipudi [US], Yanxin Shen [US], James Strickland [US], Jaemo Yang [US].
Full text: Click here
Patent 2024
Axilla AXIN2 protein, human Genes Meristem Nicotiana Phenotype Plant Leaves Transcription Factor
5
Inhibition of STAT3 Signaling in Multiple Myeloma
Not available on PMC !

Example 3

Human multiple myeloma cancer cells are known to undergo increased cell division through IL-6-triggered STAT3 signaling. Numerous studies have shown that let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibits the activity of transcription factor Signal Transducer and Activator of Transcription 3 (STAT3). Human multiple myeloma cells MM.1S were incubated for 48 hrs daily with 10 μg/ml modified miRNA as indicated and expression of the STAT3 target genes was analyzed by RT-PCR. As shown in FIGS. 2C, 4C, 6C, 8C and 10C, incubation with PS-modified let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibited expression of STAT3 target genes, for example, oncogenic Bcl-xL and/or IL-6 genes.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].
Full text: Click here
Patent 2024
Cells Division, Cell DNA, Single-Stranded Figs Gene Expression Genes Homo sapiens Malignant Neoplasms MicroRNAs Multiple Myeloma Oligonucleotides Oncogenes Reverse Transcriptase Polymerase Chain Reaction STAT3 Protein Transcription Factor

Top products related to «Transcription Factor»

1
Foxp3 transcription factor staining buffer set by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, France, Austria
The Foxp3/Transcription Factor Staining Buffer Set is a laboratory product designed for the detection and analysis of intracellular transcription factors, such as Foxp3, using flow cytometry. The set includes the necessary buffers and reagents to facilitate the fixation, permeabilization, and staining of cells for the purpose of intracellular protein detection and quantification.
2
Lsrfortessa by BD
Sourced in United States, United Kingdom, Germany, France, Canada, Australia, Belgium, China, Uruguay, Japan, Sweden, Switzerland, Cameroon
The LSRFortessa is a flow cytometer designed for multiparameter analysis of cells and other particles. It features a compact design and offers a range of configurations to meet various research needs. The LSRFortessa provides high-resolution data acquisition and analysis capabilities.
3
Ionomycin by Merck Group
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Ionomycin is a laboratory reagent used in cell biology research. It functions as a calcium ionophore, facilitating the transport of calcium ions across cell membranes. Ionomycin is commonly used to study calcium-dependent signaling pathways and cellular processes.
4
Transcription factor buffer set by BD
Sourced in United States, Canada
The Transcription Factor Buffer Set is a collection of buffers designed for the study of transcription factors. The set includes buffers formulated to maintain the stability and activity of transcription factors during various experimental procedures.
5
Facscanto 2 by BD
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The FACSCanto II is a flow cytometer instrument designed for multi-parameter analysis of single cells. It features a solid-state diode laser and up to four fluorescence detectors for simultaneous measurement of multiple cellular parameters.
6
True nuclear transcription factor buffer set by BioLegend
Sourced in United States, Germany
The True-Nuclear Transcription Factor Buffer Set is a collection of reagents designed to extract and prepare nuclear proteins from cells for transcription factor analysis. The set includes a Nuclear Extraction Buffer and a Binding Buffer, which are used to isolate and prepare nuclear extracts for downstream applications such as electrophoretic mobility shift assays (EMSAs) and transcription factor DNA-binding studies.
7
Lsrii flow cytometer by BD
Sourced in United States, Germany, United Kingdom, Canada, France, Australia, Switzerland, Uruguay
The BD LSRII flow cytometer is a multi-parameter instrument designed for advanced flow cytometry applications. It features a modular design that allows for customization to meet specific research needs. The LSRII utilizes laser excitation and sensitive detectors to analyze the physical and fluorescent properties of individual cells or particles passing through a fluid stream.
8
Golgiplug by BD
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GolgiPlug is a laboratory product designed to inhibit protein transport from the Golgi apparatus to the cell surface. It functions by blocking the secretory pathway, preventing the release of proteins from the Golgi complex. GolgiPlug is intended for use in cell biology research applications.
9
Phorbol myristate acetate (pma) by Merck Group
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The PMA is a versatile laboratory equipment designed for precision measurement and analysis. It functions as a sensitive pressure transducer, accurately measuring and monitoring pressure levels in various applications. The PMA provides reliable and consistent data for research and testing purposes.
10
Ebioscience foxp3 transcription factor staining buffer set by Thermo Fisher Scientific
Sourced in United States
The EBioscience Foxp3/Transcription Factor Staining Buffer Set is a laboratory product designed for the detection and analysis of transcription factors, such as Foxp3, using flow cytometry. The set includes buffers for fixation, permeabilization, and staining of cellular samples.

More about "Transcription Factor"

Transcription factors (TFs) are crucial regulators that control gene expression by binding to specific DNA sequences, known as transcription factor binding sites, within gene promoters or enhancers.
These proteins play a pivotal role in diverse cellular processes such as development, differentiation, and response to external stimuli.
TFs can function as either activators, enhancing transcription, or repressors, inhibiting the transcription of target genes.
Understanding and identifying TFs is essential for studying gene regulation and developing therapies for diseases associated with dysregulated gene expression.
Techniques like flow cytometry, using instruments like the LSRFortessa or FACSCanto II, and staining buffers like the Foxp3/Transcription Factor Staining Buffer Set or True-Nuclear Transcription Factor Buffer Set, can be employed to analyze and quantify TFs.
Additionally, chemical stimuli such as Ionomycin and PMA can be used to activate TFs and study their dynamics.
Optimizing your TF research can be achieved through AI-driven comparisons, like those offered by PubCompare.ai.
This tool can help you locate the best protocols from literature, preprints, and patents, making it easier to identify reproducible and reliable methods to advance your studies with ease.
Experiecne the power of AI-driven research optimization today and unlock new insights in the complex world of transcription factor biology.