bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model
 
Yazarlar (4)
Dr. Öğr. Üyesi Ali Burak ÖNCÜL Kastamonu Üniversitesi, Türkiye
Doç. Dr. Yüksel Çelik Karabük Üniversitesi, Türkiye
Prof. Dr. Mehmet Cengiz BALOĞLU Kastamonu Üniversitesi, Türkiye
Öğr. Gör. Dr. Necdet Mehmet ÜNEL Kastamonu Üniversitesi, Türkiye
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Journal of Bioinformatics and Computational Biology (Q4)
Dergi ISSN 0219-7200 Dergi Bilgileri (2022)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili Türkçe Basım Tarihi 05-2022
Kabul Tarihi – Yayınlanma Tarihi 25-07-2022
Cilt / Sayı / Sayfa 20 / 4 / 2250014–0 DOI 10.1142/S0219720022500147
Makale Linki http://dx.doi.org/10.1142/s0219720022500147
UAK Araştırma Alanları
Biyoenformatik
Özet
The basic helix loop helix (bHLH) superfamily is a large and diverse protein family that plays a role in various vital functions in nearly all animals and plants. The bHLH proteins form one of the largest families of transcription factors found in plants that act as homo- or heterodimers to regulate the expression of their target genes. The bHLH transcription factor is involved in many aspects of plant development and metabolism, including photomorphogenesis, light signal transduction, secondary metabolism, and stress response. The amount of molecular data has increased dramatically with the development of high-throughput techniques and wide use of bioinformatics techniques. The most efficient way to use this information is to store and analyze the data in a well-organized manner. In this study, all members of the bHLH superfamily in the plant kingdom were used to develop and implement a relational database. We have created a database called bHLHDB (www.bhlhdb.org) for the bHLH family members on which queries can be conducted based on the family or sequences information. The Hidden Markov Model (HMM), which is frequently used by researchers for the analysis of sequences, and the BLAST query were integrated into the database. In addition, the deep learning model was developed to predict the type of TF with only the protein sequence quickly, efficiently, and with 97.54% accuracy and 97.76% precision. We created a unique and next-generation database for bHLH transcription factors and made this database available to the world of science. We believe that the database will be a valuable tool in future studies of the bHLH family.
Anahtar Kelimeler
BHLH | blast | deep learning | hidden markov model | transcription factor
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BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Web of Science 2
Scopus 2
Google Scholar 4
bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model

Paylaş

İletişim
  • Kastamonu Üniversitesi Rektörlüğü, Kuzeykent Kampüsü, Merkez/Kastamonu
  • unis@kastamonu.edu.tr 03662801000
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