bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model

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dc.authoridUNEL, Necdet Mehmet/0000-0002-7522-9278
dc.authoridOncul, Ali Burak/0000-0001-9612-1787
dc.contributor.authorOncul, Ali Burak
dc.contributor.authorCelik, Yuksel
dc.contributor.authorUnel, Necdet Mehmet
dc.contributor.authorBaloglu, Mehmet Cengiz
dc.date.accessioned2024-09-29T16:04:50Z
dc.date.available2024-09-29T16:04:50Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.1142/S0219720022500147
dc.identifier.issn0219-7200
dc.identifier.issn1757-6334
dc.identifier.issue4en_US
dc.identifier.pmid35881019en_US
dc.identifier.scopus2-s2.0-85135311849en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1142/S0219720022500147
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6350
dc.identifier.volume20en_US
dc.identifier.wosWOS:000848586900001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.relation.ispartofJournal of Bioinformatics and Computational Biologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBHLHen_US
dc.subjectdeep learningen_US
dc.subjecttranscription factoren_US
dc.subjecthidden markov modelen_US
dc.subjectblasten_US
dc.titlebHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning modelen_US
dc.typeArticleen_US

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