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bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model      
Yazarlar
Dr. Öğr. Üyesi Ali Burak ÖNCÜL Dr. Öğr. Üyesi Ali Burak ÖNCÜL
Türkiye
Yüksel Çelik
Türkiye
Necdet Mehmet Ünel
Prof. Dr. Mehmet Cengiz BALOĞLU Prof. Dr. Mehmet Cengiz BALOĞLU
Kastamonu Üniversitesi, Türkiye
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
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı Journal of Bioinformatics and Computational Biology
Dergi ISSN 0219-7200
Dergi Tarandığı Indeksler SCI
Dergi Grubu Q4
Makale Dili Türkçe
Basım Tarihi 07-2022
Cilt No 20
Sayı 4
Doi Numarası 10.1142/S0219720022500147
Makale Linki http://dx.doi.org/10.1142/s0219720022500147