شناسایی نشانگرهای بیوشیمیایی برتر مرتبط با تحمل به شوری در گندم با استفاده از داده-کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، بخش اگرواکولوژی، دانشکده کشاورزی و منابع طبیعی داراب، دانشگاه شیراز، ایران

2 نویسنده مسئول، دانشیار، بخش زراعت و اصلاح نباتات، دانشکده کشاورزی، دانشگاه شیراز، ایران

چکیده

هدف: شناسایی مسیرهای موثر در ایجاد تحمل نسبت به تنش شوری یکی از مباحث جذاب در علوم گیاهی است که در این راستا روش­های جدید داده­کاوی نگرش جدیدی برای محققان ایجاد کرده است. در این پژوهش، الگوریتم­های انتخاب ویژگی (feature selection) و درخت تصمیم­گیری (decision tree, DT) به منظور شناسایی نشانگرهای بیوشیمیایی در تحمل به شوری استفاده شد.
 مواد و روش­ها: در این راستا، به منظور بررسی برخی نشانگرهای بیوشیمیایی از جمله میزان پروتئین، آنزیم­های آنتی­اکسیدانی سوپراکسید دیسموتاز، پراکسیداز، کاتالاز،‌ آسکوربات پراکسیداز، پرولین، میزان سدیم و پتاسیم در ارقام گندم و خویشاوند وحشی آنAegilops crassa ، آزمایشی به صورت فاکتوریل در قالب طرح کاملاً تصادفی با سه تکرار انجام شد. فاکتورهای آزمایشی شامل ژنوتیپ (ارقام گندم زراعی ارگ (مقاوم به شوری) و الموت (حساس به شوری)، و یک خویشاوند وحشی (Ae. crassa)) و تنش شوری (کلرید سدیم  mM150 و mM 0) بودند.
نتایج: نتایج این دو راهکار نشان داد که ترکیب سلسله مراتبی پرولین، سوپراکسید دیسموتاز و آسکوربات‌ پراکسیداز می­تواند به عنوان نشانگرهای بیوشیمیایی برای انتخاب ژنوتیپ متحمل به تنش شوری استفاده گردد. دو درخت تصمیم­گیری دارای بیشترین کارایی در پیش­بینی ژنوتیپ حساس و متحمل بودند. این درخت­های تصمیم­گیری عبارت بودند از مدل درخت تصمیم­گیری Ratio DT Parallel Gain با کارایی 5/97 درصد که روی پایگاه داده­ای Info Gain Ratio اجرا شد و دیگری DT Gain Ratio با کارایی 67/91 درصد که روی پایگاه داده­ای  Ruleاجرا شد.
نتیجه گیری: در مجموع، نتایج حاصله بیانگر آن بود که تلفیق مطالعات بیوانفورماتیک و آزمایشگاهی می­تواند منجر به شناسایی مسیرهای مرتبط با تنش شوری و فهم بهتر مکانیسم­های کنترل کننده تحمل به تنش شود.

کلیدواژه‌ها


عنوان مقاله [English]

Identification of Superior Biochemical Markers Linked to Salinity Tolerance in Wheat Using Data Mining

نویسندگان [English]

  • Zahra Zinati 1
  • Abbas Alemzadeh 2
1 Assistant Professor, College of Agriculture and Natural Resources of Darab, Shiraz, Iran.
2 Associate Professor, Crop Production and Plant Breeding Department, School of Agriculture, Shiraz University, Shiraz, Iran.
چکیده [English]

Objective
The recognition of pathways involved in salt tolerance mechanisms is one of the interesting topics in plant sciences and the new data-mining techniques provide a new insight for researchers.  In this research, various attribute weighting and decision tree (DT) algorithms were executed to discover biochemical markers linked to salinity tolerance.
 
Materials and methods
In this regard, to assess some biochemical markers such as protein, superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, proline, sodium and potassium in wheat and its wild relative Aegilops crassa, a factorial experiment was conducted in a completely randomized design with three replications. The factors in the study were genotypes (Arg (salt tolerance) and Alamout (salt sensitive), and a wild relative (Ae. crassa)) and salinity (sodium chloride 0 mM and 150 mM).
 
Results
According to these approaches, proline, superoxide dismutase and ascorbate peroxidase can be used as biochemical markers to screen wheat genotypes for salt tolerance. Feature selection and decision tree results showed that hierarchy combination of proline, superoxide dismutase and ascorbate peroxidase can be used as biochemical markers for selection of salinity tolerant wheat. The DT Parallel Gain Ratio model when run on the Info Gain Ratiodataset and the DT Gain Ratiomodel when run on the Ruledataset were the best model in distinguishing sensitive and tolerant genotypes with 97.5% and 91.67% performances, respectively.
 
Conclusions
Overall, the results showed that combining bioinformatics, and laboratory studies can lead to identifying pathways associated with salinity and provide a better understanding of the mechanisms underpinning stress tolerance.

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