با همکاری مشترک دانشگاه پیام نور و انجمن بیوتکنولوژی جمهوری اسلامی ایران

نوع مقاله : مروری

نویسندگان

1 گروه ژنومیکس، پژوهشکده بیوتکنولوژی شمال غرب و غرب کشور، پژوهشگاه بیوتکنولوژی کشاورزی ایران، سازمان تحقیقات، آموزش و ترویج کشاورزی، تبریز، ایران.

2 گروه بیوتکنولوژی کشاورزی، دانشکده کشاورزی، دانشگاه شهید مدنی آذربایجان، ایران

3 گروه بیوتکنولوژی کشاورزی، دانشکده کشاورزی، دانشگاه شهید مدنی آذربایجان، ایران.

چکیده

پروتئین‌های گروه دهیدرین (Dehydrin: DHN)، گروهی از پروتئین‌های مهم دخیل در پاسخ به تنش‌های غیر زیستی مانند سرما و خشکی در گیاهان هستند. این پروتئین‌ها به گروهی از پروتئین‌های محافظت‌کننده از سایر پروتئین‌ها به نام type II Late embryogenesis abundant تعلق دارند. با توجه به اهمیت پروتئین‌های گروه دهیدرین در گیاهان، در این تحقیق روابط تکاملی این گروه در گیاهان مختلف موردبررسی قرار گرفت. بدین منظور توالی‌های پروتئین‌های دهیدرین گیاهان مختلف از سایت NCBI استخراج و هم‌ردیف گردید. نتایج وجود نواحی حفاظت‌شده ازجمله موتیف K و S که به ترتیب در واکنش با دیگر پروتئین‌های تحت تنش و محافظت از آن‌ها و همچنین انتقال پروتئین‌های گروه دهیدرین از سیتوپلاسم به هسته نقش دارند را در بین ژن‌های مورد بررسی نشان داد. درخت فیلوژنی بر پایه نواحی حفاظت‌شده و با روش Neighbor Joining رسم گردید و توالی خطی و درصد اسیدآمینه‌های موجود در ساختار این پروتئین‌ها به‌همراه توالی مکمل ژنومی آن‌ها نیز مورد بررسی قرار گرفت. نتایج نشان داد که پروتئین‌های گروه دهیدرین دولپه‌ای و تک‌لپه‌ای‌ها به دو گروه مجزا تفکیک‌شده و در هر گروه نیز بر اساس نزدیکی و دوری جنس‌های مختلف گیاهی در کلاسترهای مجزا قرار گرفتند. همچنین گیاهان تک و دولپه ازلحاظ توالی خطی اسیدآمینه و درصد آن‌ها هم اختلاف بالایی را نشان دادند. از طرفی، بررسی توالی ژنومی این گیاهان نشان‌دهنده وجود ساختارهای حفاظت‌شده و مشابه بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Harnessing machine learning approach for phenotyping and deciphering the plants biotic and abiotic stresses responsive molecular mechanisms

نویسندگان [English]

  • Bahman Panahi 1
  • Bentolhoda Ghavidel 2
  • Pouya Shahgoli 3

1 Department of Genomics, Branch for Northwest & West region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran.

2 Department of Biotechnology, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran

3 Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

چکیده [English]

Machine learning plays a crucial role in identifying specific stressors that impact plant species and provides a comprehensive understanding of the challenges plants face in natural environments. The use of machine learning algorithms has significantly enhanced our ability to classify and differentiate the types of stress. There are two main methodologies in machine learning: supervised learning and unsupervised learning. In supervised learning, the model is trained using input-output data pairs, while unsupervised learning involves training the model without access to output labels. Unsupervised learning is primarily used for data exploration and dimension reduction. This detailed classification helps us better understand the distinct characteristics associated with different stressors and provides a more nuanced view of the plant stress landscape. Machine learning also enables the quantitative assessment of stress intensity and extent, allowing for an accurate evaluation of its impact on plant health and productivity. This quantitative approach helps researchers measure the true extent of stressors and their effects on the overall health of plant ecosystems. By employing advanced algorithms, machine learning can make predictions about future occurrences of stress and their potential consequences on plant ecosystems. This foresight strengthens preventive measures for sustainable agricultural practices, as researchers and practitioners can anticipate and mitigate potential threats to plant health. The purpose of this review is to provide a comprehensive understanding of the applications and concepts of machine learning in uncovering the complexity of plant stress phenotyping and elucidating the involved molecular mechanisms.

کلیدواژه‌ها [English]

  • Machine learning
  • classification
  • model
  • phenotype
  • stress
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