vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-25-35vavilov-4551Research ArticleБИОИНФОРМАТИКАBIOINFORMATICSCropGene: программный комплекс анализа геномных и транскриптомных данных сельскохозяйственных растенийCropGene: a software package for the analysis of genomic and transcriptomic data of agricultural plantshttps://orcid.org/0000-0002-3011-6288ПронозинА. Ю.PronozinA. Yu.

Новосибирск

Novosibirsk

https://orcid.org/0009-0003-0918-8441КаретниковД. И.KaretnikovD.  I.

Новосибирск

Novosibirsk

https://orcid.org/0009-0001-6414-5562ШмаковН. А.ShmakovN. A.

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Novosibirsk

https://orcid.org/0000-0002-3477-2047БочарниковаМ. Е.BocharnikovaM. E.

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Novosibirsk

https://orcid.org/0000-0001-7969-8015АфонниковаС. Д.AfonnikovaS. D.

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Novosibirsk

https://orcid.org/0000-0001-9738-1409АфонниковД. А.AfonnikovD. A.

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Novosibirsk

https://orcid.org/0000-0001-6800-8787КолчановН. А.KolchanovN. A.

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Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАНРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RASRussian Federation202511042025292320329Copyright © Пронозин А.Ю., Каретников Д.И., Шмаков Н.А., Бочарникова М.Е., Афонникова С.Д., Афонников Д.А., Колчанов Н.А., 20252025Пронозин А.Ю., Каретников Д.И., Шмаков Н.А., Бочарникова М.Е., Афонникова С.Д., Афонников Д.А., Колчанов Н.А.Pronozin A.Y., Karetnikov D.I., Shmakov N.A., Bocharnikova M.E., Afonnikova S.D., Afonnikov D.A., Kolchanov N.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4551

В настоящее время селекция сельскохозяйственных растений все больше опирается на использование молекулярно-биологических данных о генетических последовательностях, что позволяет существенно ускорить селекционный процесс создания новых сортов растений за счет геномного редактирования. Эти данные имеют большой объем, разнообразны и требуют для анализа затрат большого количества ресурсов, как трудовых, так и вычислительных. Анализ данных с такими объемом и сложностью может быть эффективным лишь с применением современных методов биоинформатики, включающих алгоритмы идентификации генов, предсказания их функции, оценку влияния эффекта мутации на фенотип растений. Такой анализ в последнее время стал невозможным без использования интегрированных программных комплексов, решающих задачи разного уровня за счет выполнения вычислительных конвейеров. В статье описан программный комплекс CropGene, разработанный для комплексного анализа геномных и транскриптомных данных сельскохозяйственных растений. Система включает в себя несколько блоков биоинформатического анализа, таких как анализ вариаций генов, сборка геномов и транскриптомов, а также аннотация генов и белков. В комплексе реализованы новые методы анализа длинных некодирующих РНК, белковых доменов, поиска и анализа полиморфизмов и полногеномного исследования ассоциаций. В работе представлены примеры применения CropGene для анализа сельскохозяйственных организмов, таких как Solanum tuberosum, Zea mays. С помощью данного программного пакета найдены: генетические маркеры, объясняющие до 50 % изменчивости параметров окраски семян; потенциальные гены, которые могут стать перспективным материалом для получения сортов картофеля; более 100 тыс. новых длинных некодирующих РНК. Также обнаружены ортогруппы, доменная структура которых проявляет заметное сходство с доменной архитектурой характерных секретируемых фосфолипаз А2. Таким образом, CropGene представляет собой важный инструмент для ученых и практиков, работающих в области агробиотехнологий и генетики растений.

Currently, the breeding of agricultural plants is increasingly based on the use of molecular biological data on genetic sequences, which makes it possible to significantly accelerate the breeding process, create new plant varieties through genomic editing. These data have a large volume, variety and require a large amount of resources, both labor and computing, to analyze the costs. Data analysis of such volume and complexity can be effective only when using modern bioinformatics methods, which include algorithms for identifying genes, predicting their function, and evaluating the effect of mutation on plant phenotype. Such an analysis has recently become impossible without the use of integrated software systems that solve problems of different levels by executing computational pipelines. The paper describes the CropGene software package developed for the comprehensive analysis of genomic and transcriptomic data of agricultural plants. CropGene includes several blocks of bioinformatic analysis, such as analysis of gene variations, assembly of genomes and transcriptomes, as well as annotation of genes and proteins. CropGene implements new methods for analyzing long non-coding RNAs, protein domains, searching and analyzing polymorphisms, and genomewide association research. CropGene has a user-friendly interface and supports working with various types of data, which greatly simplifies its use for researchers who do not have deep knowledge in the field of bioinformatics. The paper provides examples of the use of CropGene for the analysis of agricultural organisms such as Solanum tuberosum and Zea mays. With CropGene, genetic markers have been identified that explain up to 50 % of the variability in seed color parameters; potential genes that may become promising material for producing potato varieties; more than 100 thousand new long non-coding RNAs. Orthogroups were also found, the domain structure of which shows a marked similarity with the domain architecture of characteristic secreted A2 phospholipases. Thus, CropGene is an important tool for scientists and practitioners working in the field of agrobiotechnology and plant genetics.

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The authors declare that there are no conflicts of interest present.