Wheat and maize miRNAs are potential regulators of human genes expression

Rakhmetullina Aizhan1, Ivashchenko Anatoliy2, Pyrkova Anna31SRI of biology and biotechnology problems Al-Farabi Kazakh National University Almaty, Kazakhstan, zhanullina1994@gmail.com2SRI of biology and biotechnology problems Al-Farabi Kazakh National University Almaty, Kazakhstan, a.iavashchenko@gmail.com3SRI of biology and biotechnology problems Al-Farabi Kazakh National University Almaty, Kazakhstan, anna.pyrkova@kaznu.kz With food, a huge variety of biological material gets into the human digestive tract, which the body uses for life support. The variety of food material entering the gastrointestinal tract, especially at the molecular level, cannot be distinguished from endogenous metabolites and these exogenous compounds can significantly alter the body\’s metabolism. Such compounds include plant miRNAs, which are indistinguishable from endogenous human miRNAs in physicochemical properties. It is necessary to clarify the degree of influence of exogenous plant miRNAs on the expression of human genes, since it is not known in advance what consequences can occur when plant miRNAs enters the human body. A huge amount of research does not allow experiments with all human genes and all plant miRNAs, so we have studied the effect of wheat and maize miRNAs on human genes using computer methods. As a result of studying the binding of 125 tae-miRNAs and 325 zma-miRNAs to mRNAs of 17508 human genes it was revealed that 158 genes were targets for 52 tae-miRNAs and 51 genes for 11 zma-miRNAs. Binding sites in the mRNA of human genes were located in 5\’UTR, CDS, 3\’UTR.

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STUDY OF THE ROOT TRANSCRIPTOME OF BREAD WHEAT USING HIGH-THROUGHPUT RNA SEQUENCING (RNA-SEQ)

Poster (download) Alexandr Vikhorev1, Nikolay Shmakov2, Anastasia Glagoleva3, Elena Khlestkina4, Olesya Shoeva51Novosibirsk State University, vikhorev@bionet.nsc.ru2Institute of Cytology and Genetics SB RAS, shmakov@bionet.nsc.ru3Institute of Cytology and Genetics SB RAS, glagoleva@bionet.nsc.ru4All-Russian Institute of Plant Recources, khlest@bionet.nsc.ru5Institute of Cytology and Genetics SB RAS, olesya_ter@bionet.nsc.ru Bread wheat (Triticum aestivum L.) is the most important crop in the world. It provides about 20% of the total calories consumed by humans. For a long time, wheat selection was mainly based on phenotypic traits of the shoot, but the roots were given little attention. As a result, the root system of modern wheat varieties has weakened. Therefore, the study of genetic control of wheat roots development is an urgent issue. In this study, sequencing of RNA libraries from roots and coleoptile of Russian spring variety “Saratovskaya, 29” was performed. De novo transcriptome was assembled. 31,488 up-regulated, 35,851 down-regulated and 18,040 roots-specific transcripts were found. The subsequent analysis of genes with differential expression will allow choosing the candidate genes for development of wheat varieties with resistant root system.

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Application of neural networks to image recognition of wheat rust diseases

Poster (download) Mikhail Genaev11Mikhail Genaev ICG SB RAS, Kurchatov Genomic Center, Novosibirsk, Russia NSU, Novosibirsk, Russia, mag@bionet.nsc.ru Rust diseases of cereals are caused by pathogenic fungi and can significantly reduce plant productivity. Many cultures are subject to them. The disease is difficult to control on a large scale, so one of the most relevant approaches is crop monitoring, which helps to identify the disease at an early stage and make efforts to prevent its spread. One of the most effective methods of control is the identification of the disease from digital images that obtained by a smartphone camera. In this paper, we present a deep learning algorithm that uses a digital image of wheat plants to determine whether they are affected by a disease and, if so, what type: leaf rust or stem rust. The algorithm based on the convolution neural network of the densenet architecture. The resulting model demonstrates high accuracy of classification: the measure of accuracy F1 on the validation sample is 0.9, the AUC averaged over 3 classes is 0.98.

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