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Title: Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Carnegie Institution for Science, Stanford, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1618549
Alternate Identifier(s):
OSTI ID: 1393512
Grant/Contract Number:  
BER-65472; SC0008769
Resource Type:
Published Article
Journal Name:
BMC Genomics
Additional Journal Information:
Journal Name: BMC Genomics Journal Volume: 18 Journal Issue: 1; Journal ID: ISSN 1471-2164
Publisher:
Springer Science + Business Media
Country of Publication:
United Kingdom
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; Genes with unknown function; Transcriptional regulators; Coactivators; Polycomb repressive complex 2

Citation Formats

Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, and Rhee, Seung Y. Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction. United Kingdom: N. p., 2017. Web. doi:10.1186/s12864-017-3853-9.
Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, & Rhee, Seung Y. Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction. United Kingdom. doi:10.1186/s12864-017-3853-9.
Bossi, Flavia, Fan, Jue, Xiao, Jun, Chandra, Lilyana, Shen, Max, Dorone, Yanniv, Wagner, Doris, and Rhee, Seung Y. Mon . "Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction". United Kingdom. doi:10.1186/s12864-017-3853-9.
@article{osti_1618549,
title = {Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction},
author = {Bossi, Flavia and Fan, Jue and Xiao, Jun and Chandra, Lilyana and Shen, Max and Dorone, Yanniv and Wagner, Doris and Rhee, Seung Y.},
abstractNote = {},
doi = {10.1186/s12864-017-3853-9},
journal = {BMC Genomics},
number = 1,
volume = 18,
place = {United Kingdom},
year = {2017},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1186/s12864-017-3853-9

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Cited by: 1 work
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Figures / Tables:

Fig. 1 Fig. 1: Feature-based prediction pipeline to identify novel transcriptional regulator families. a Pipeline work flow: First, Arabidopsis protein families were filtered based on their size and the GO annotations of their members. Then, uncharacterized families with more than 2 members were filtered based on subcellular localization patterns using Yloc [98],more » percentage of disordered residues using Predisorder [99], and the ability of at least one member to activate transcription of a reporter gene in yeast (autoactivation) [22, 103–107]. Numbers in the Venn diagram represent the number of families with most members being nuclear localized (blue), high percentage of disordered residues (green) and autoactivation in yeast (red). Families that met all criteria (intersection of the Venn diagram) were considered as candidate regulator families. b-j Proportion of proteins predicted to contain nuclear localization signal (NLS) (b-d), distribution of the percentage of disordered amino acid residues (e-g), and proportion of proteins with autoactivation activity (h-j) in the background (white), TFs (dark gray), and predicted regulators (light gray). The background corresponds to all proteins in Arabidopsis (b,e,h), fruit fly (c, f, i), or human (d, g, j) genomes or the set of proteins that were tested for autoactivation in yeast (h, i, j). * = p-value <0.0001, chi-square test with Yates correction (b-d and h-j) or t-test (e-g)« less

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