Literature DB >> 15706513

Systematic DNA-binding domain classification of transcription factors.

Philip Stegmaier1, Alexander E Kel, Edgar Wingender.   

Abstract

Based on the manual annotation of transcription factors stored in the TRANSFAC database, we developed a library of hidden Markov models (HMM) to represent their DNA-binding domains and used it for a comprehensive classification. The models constructed were applied on the UniProt/Swiss-Prot database, leading to a systematic classification of further DNA-binding protein entries. The HMM library obtained can be used to classify any newly discovered transcription factor according to its DNA-binding domain and, thus, to generate hypotheses about its DNA-binding specificity.

Mesh:

Substances:

Year:  2004        PMID: 15706513

Source DB:  PubMed          Journal:  Genome Inform        ISSN: 0919-9454


  22 in total

1.  Template-based structure prediction and classification of transcription factors in Arabidopsis thaliana.

Authors:  Tao Lu; Yuedong Yang; Bo Yao; Song Liu; Yaoqi Zhou; Chi Zhang
Journal:  Protein Sci       Date:  2012-05-01       Impact factor: 6.725

2.  Integrative content-driven concepts for bioinformatics "beyond the cell".

Authors:  Edgar Wingender; Torsten Crass; Jennifer D Hogan; Alexander E Kel; Olga V Kel-Margoulis; Anatolij P Potapov
Journal:  J Biosci       Date:  2007-01       Impact factor: 1.826

3.  Intrinsic disorder in transcription factors.

Authors:  Jiangang Liu; Narayanan B Perumal; Christopher J Oldfield; Eric W Su; Vladimir N Uversky; A Keith Dunker
Journal:  Biochemistry       Date:  2006-06-06       Impact factor: 3.162

4.  Identification of specific DNA binding residues in the TCP family of transcription factors in Arabidopsis.

Authors:  Pooja Aggarwal; Mainak Das Gupta; Agnel Praveen Joseph; Nirmalya Chatterjee; N Srinivasan; Utpal Nath
Journal:  Plant Cell       Date:  2010-04-02       Impact factor: 11.277

5.  Evolutionary rates at codon sites may be used to align sequences and infer protein domain function.

Authors:  Pierre M Durand; Scott Hazelhurst; Theresa L Coetzer
Journal:  BMC Bioinformatics       Date:  2010-03-24       Impact factor: 3.169

6.  Predicting DNA-binding specificities of eukaryotic transcription factors.

Authors:  Adrian Schröder; Johannes Eichner; Jochen Supper; Jonas Eichner; Dierk Wanke; Carsten Henneges; Andreas Zell
Journal:  PLoS One       Date:  2010-11-30       Impact factor: 3.240

7.  A negative selection heuristic to predict new transcriptional targets.

Authors:  Luigi Cerulo; Vincenzo Paduano; Pietro Zoppoli; Michele Ceccarelli
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

8.  Exploiting nucleotide composition to engineer promoters.

Authors:  Manfred G Grabherr; Jens Pontiller; Evan Mauceli; Wolfgang Ernst; Martina Baumann; Tara Biagi; Ross Swofford; Pamela Russell; Michael C Zody; Federica Di Palma; Kerstin Lindblad-Toh; Reingard M Grabherr
Journal:  PLoS One       Date:  2011-05-18       Impact factor: 3.240

9.  OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif.

Authors:  Amar Drawid; Nupur Gupta; Vijayalakshmi H Nagaraj; Céline Gélinas; Anirvan M Sengupta
Journal:  BMC Bioinformatics       Date:  2009-07-07       Impact factor: 3.169

10.  A discriminative approach for unsupervised clustering of DNA sequence motifs.

Authors:  Philip Stegmaier; Alexander Kel; Edgar Wingender; Jürgen Borlak
Journal:  PLoS Comput Biol       Date:  2013-03-21       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.