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Status |
Public on Oct 09, 2020 |
Title |
DeepTFactor, a deep learning-based tool for the identification of transcription factors |
Organism |
Escherichia coli |
Experiment type |
Genome binding/occupancy profiling by high throughput sequencing
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Summary |
We report the development of a deep learning-based tool, DeepTFactor, that predicts whether a protein of question is a transcription factor. DeepTFactor uses a convolutional neural network to extract features of protein sequences. We characterized the genome-wide binding sites of three TFs (i.e., YqhC, YiaU, and YahB), which are predicted by DeepTFactor
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Overall design |
Identification of genome-wide bindings for uncharacterized transcription factors in E. coli K-12 MG1655, using ChIP-exo technology
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Contributor(s) |
Kim G, Gao Y, Palsson B, Lee S |
Citation(s) |
33372147 |
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Submission date |
Sep 28, 2020 |
Last update date |
Jan 11, 2021 |
Contact name |
Ye Gao |
E-mail(s) |
yeg002@ucsd.edu
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Organization name |
UCSD
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Street address |
9500 Gilman Dr.
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City |
La Jolla |
ZIP/Postal code |
92093 |
Country |
USA |
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Platforms (1) |
GPL18133 |
Illumina HiSeq 2500 (Escherichia coli) |
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Samples (6)
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Relations |
BioProject |
PRJNA666186 |
SRA |
SRP285666 |