vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/VJGB-23-104vavilov-3992Research ArticleТЕХНИЧЕСКАЯ БИОИНФОРМАТИКАINDUSTRIAL BIOINFORMATICSЛабораторные информационные системы для управления исследовательскими работами в биологииLaboratory information systems for research management in biologyhttps://orcid.org/0000-0002-1102-0934МухинА. М.MukhinA. M.

Новосибирск

Novosibirsk

mukhin@bionet.nsc.ruhttps://orcid.org/0000-0002-5711-7539КазанцевФ. В.KazantsevF. V.

Новосибирск

Novosibirsk

https://orcid.org/0000-0003-3138-381XЛашинС. А.LashinS. A.

Новосибирск

Novosibirsk

Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Курчатовский геномный центр ИЦиГ СО РАН; Новосибирский национальный исследовательский государственный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Kurchatov Genomic Center of ICG SB RAS; Novosibirsk State UniversityRussian Federation202312122023277898-­905Copyright © Мухин А.М., Казанцев Ф.В., Лашин С.А., 20232023Мухин А.М., Казанцев Ф.В., Лашин С.А.Mukhin A.M., Kazantsev F.V., Lashin S.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/3992

Современная исследовательская работа в биологии нередко требует усилий одной или нескольких групп исследователей. Часто это группы специалистов из смежных областей, которые генерируют и обмениваются данными разных форматов и размеров. Без применения современных подходов автоматизации работы и версионирования данных (когда данные от разных сотрудников сохраняются в разные моменты времени) коллективная работа быстро переходит в неуправляемый хаос. В настоящем обзоре приведен ряд информационных систем, предназначенных для решения озвученных задач. Их применение для организации научной деятельности позволяет управлять потоком действий и данных, добиваясь работы всех участников с актуальной информацией, и решением вопроса воспроизводимости как экспериментальных, так и вычислительных результатов. Описаны методики по организации потоков данных в рамках работы коллектива, принципы по организации метаданных и онтологий. Рассмотрены информационные системы Trello, Git, Redmine, SEEK, OpenBIS и Galaxy. Описана их функциональность и сфера использования. Выбирая те или иные инструменты, важно понимать цель внедрения, определить набор задач, которые они должны решать, и исходя из этого формулировать требования и отслеживать применение рекомендаций на местах. 

Задачи по созданию структуры онтологий, метаданных, схем хранения данных и программных систем являются ключевыми для коллектива, который решился на проведение работ по автоматизации оборота данных. Не всегда возможно внедрить такие системы целиком, но все же следует стремиться к этому через поэтапное внедрение принципов по организации данных и задач с освоением отдельных программных инструментов. Следует отметить, что системы Trello, Git и Redmine проще в использовании, настройке и поддержке для малых исследовательских групп. В то же время SEEK, OpenBIS и Galaxy более специфичные, их применение целесообразно в случае, если возможностей простых систем уже недостаточно.

Modern investigations in biology often require the efforts of one or more groups of researchers. Often these are groups of specialists from various scientific fields who generate and share data of different formats and sizes. Without modern approaches to work automation and data versioning (where data from different collaborators are stored at different points in time), teamwork quickly devolves into unmanageable confusion. In this review, we present a number of information systems designed to solve these problems. Their application to the organization of scientific activity helps to manage the flow of actions and data, allowing all participants to work with relevant information and solving the issue of reproducibility of both experimental and computational results. The article describes methods for organizing data flows within a team, principles for organizing metadata and ontologies. The information systems Trello, Git, Redmine, SEEK, OpenBIS and Galaxy are considered. Their functionality and scope of use are described. Before using any tools, it is important to understand the purpose of implementation, to define the set of tasks they should solve, and, based on this, to formulate requirements and finally to monitor the application of recommendations in the field. The tasks of creating a framework of ontologies, metadata, data warehousing schemas and software systems are key for a team that has decided to undertake work to automate data circulation. It is not always possible to implement such systems in their entirety, but one should still strive to do so through a step­by­step introduction of principles for organizing data and tasks with the mastery of individual software tools. It is worth noting that Trello, Git, and Redmine are easier to use, customize, and support for small research groups. At the same time, SEEK, OpenBIS, and Galaxy are more specific and their use is advisable if the capabilities of simple systems are no longer sufficient. 

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