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BMC Research Notes

, 8:82

First Online: 14 March 2015Received: 26 September 2014Accepted: 02 March 2015DOI: 10.1186-s13104-015-1039-6

Cite this article as: Bahrami, S., Ehsani, R. & Drabløs, F. BMC Res Notes 2015 8: 82. doi:10.1186-s13104-015-1039-6

Abstract

BackgroundTranscription factors are essential proteins for regulating gene expression. This regulation depends upon specific features of the transcription factors, including how they interact with DNA, how they interact with each other, and how they are post-translationally modified. Reliable information about key properties associated with transcription factors will therefore be useful for data analysis, in particular of data from high-throughput experiments.

ResultsWe have used an existing list of 1978 human proteins described as transcription factors to make a well-annotated data set, which includes information on Pfam domains, DNA-binding domains, post-translational modifications and protein–protein interactions. We have then used this data set for enrichment analysis. We have investigated correlations within this set of features, and between the features and more general protein properties. We have also used the data set to analyze previously published gene lists associated with cell differentiation, cancer, and tissue distribution.

ConclusionsThe study shows that well-annotated feature list for transcription factors is a useful resource for extensive data analysis; both of transcription factor properties in general and of properties associated with specific processes. However, the study also shows that such analyses are easily biased by incomplete coverage in experimental data, and by how gene sets are defined.

KeywordsTranscription factor DNA-binding domain Protein–protein interaction Post-translational modification Enrichment analysis AbbreviationsTFTranscription factor

DBDDNA-binding domain

PPIProtein–protein interaction

TFBSTranscription factor binding site

PTMPost-translational modifications

PWMPosition weight matrix

PFMPosition frequency matrix

HMMHidden Markov model

FNFalse negative

FPFalse positive

TPTrue positive

TNTrue negative

SnSensitivity

SpSpecificity

PPVPositive predictive value

MCCMatthews correlation coefficient

BpBase pair

SVMSupport vector machine

Shahram Bahrami and Rezvan Ehsani contributed equally to this work.

Electronic supplementary materialThe online version of this article doi:10.1186-s13104-015-1039-6 contains supplementary material, which is available to authorized users.

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Autor: Shahram Bahrami - Rezvan Ehsani - Finn Drabløs

Fuente: https://link.springer.com/







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