In the biological research community, collecting information is time-consuming and laborious for scholars. Due to inconsistent terms in various databases, machine retrieval is very difficult, which has become an urgent problem.

The emergence of the GO project is like a ray of light in the darkness.

In research in the field of biology, we often encounter situations where we have to invest a lot of time and energy in order to find information in related fields.

At different research institutions, biologists may need to search for the required information in numerous biological databases.

In some large biological research institutions in the United States, researchers often need to spend hours, sometimes even days, to find the specific biological data they need.

The terminology between databases is very different, and the terms in American and European databases often do not match, which makes it difficult for machines to effectively search according to unified standards when searching.

This situation seriously hinders the progress of biological research.

Biologists are often so busy gathering complex information that they have relatively little time to devote to core research.

The GO project aims to solve these problems.

It builds classification category and hierarchical structure models based on the intrinsic characteristics or specific attributes of terms.

During the construction process, the system will draw on annotation information from SWISS - PROT, PIR, NCBI, CGAP, EC and other databases.

The specifications here are not unique. This flexibility allows the database to accept a wider range of biological data.

In the field of cell research, consulting the descriptions of multiple databases can provide a more comprehensive grasp of information about various functions and structures of cells.

The GO project includes several key components, such as GO ID, which is a unique identifier and belongs to the CC part.

There are optional identifiers such as secondary IDs. When merging terms, if multiple terms have the same meaning, an auxiliary secondary ID will be generated. All these related IDs are retained to prevent information loss.

Synonyms are also part of its elements.

GO has a reduced version of the ontology, which contains a subset of the entire GO terms.

The multi-level nature of this structure allows GO to cope with the different needs of various biological information organization and processing.

GO is not only a tool, but also plays a crucial role in connecting genes and functions.

In many emerging fields of gene function research, this unified description system facilitates biologists to quickly find the function of target genes. Moreover, as biological research continues to advance, this framework can be updated in time to keep pace.

It has become convenient and fast to find gene functional annotations in massive data, which greatly reduces the duplication of work by biologists, thereby accelerating the birth of new results.

In the field of cancer genetic research, the amount of data is huge. Previously, biologists needed to spend a lot of time searching for and comparing functional annotations of genes in multiple databases. But now, with the help of the GO project, they can quickly locate the required information, and their efficiency has been significantly improved.

The GO annotation link is crucial, as it is responsible for associating gene products with GO terms.

The specific operation relies heavily on database comparison and other technologies, with the purpose of associating genes of a certain species with corresponding GO terms, and ultimately constructing a corresponding list of genes and GO terms.

In research in the field of plant genes, by comparing plant genes with GO terms, we can clearly understand the specific roles of specific genes in plant growth, disease resistance, etc.

This annotation method plays an important role in many biological research scenarios.

When studying the genetic characteristics of animal genes, constructing the association between genes and GO terms can help us deeply analyze the functional transmission of genes in the genetic process and the formation of new functions.

One of the common uses of GO is the interpretation of large-scale molecular biology experiments.

In this so-called "omnipotent experiment", different omics groups bring together biomolecules, aiming to gain a deeper understanding of the structure, function and change patterns of organisms.

In large-scale experiments on gene function identification, researchers can use GO's classification and annotation tools to quickly make preliminary functional classifications of the many genes involved in the experiment and determine research directions accordingly.

In a large-scale research experiment on the special gene functions of marine organisms, using GO technology, we can quickly screen out among many genes which ones may be related to the stress resistance of marine organisms in deep-sea pressure environments.

GO enrichment analysis plays a unique and important role.

Statistically significant similarities or differences can be observed in alternately controlled experimental settings. In addition, it is possible to gain insight into genes important in specific biological processes and identify gene groups that exhibit similar expression patterns under specific conditions.

When studying diseases, if you need to explore the changes in gene expression of a disease under the influence of various environmental factors, you can quickly identify core genes by using GO enrichment analysis.

In the process of exploring how certain organisms adapt to specific environments, we can use GO enrichment analysis to discover gene sets that show similar expression patterns in the same environment. This discovery is crucial for a deeper understanding of organisms' adaptive strategies.

GO can accurately describe the biochemical or catalytic properties of proteins or single and multiple gene products (such as RNA) and complexes.

In order to distinguish names and functions, the word "activity" is deliberately used to describe enzymes, receptors, transporters, etc. to avoid confusion.

To study complex biochemical processes in organisms, with the help of GO's function, the specific role that each substance plays in the reaction can be accurately identified.

When studying the pathogenesis of diabetes, we used GO technology to accurately determine the biochemical effects of proteins, RNA and other substances secreted by pancreatic islet cells, thus being able to more deeply reveal the molecular operating mechanism behind the physiological process of this disease.

GO is like a guide map in Cell City, marking the location of each point, that is, one-to-one correspondence between the products of genes and specific regions or structures of cells, thus helping us understand the specific environment in which they function.

The accurate identification of gene products can help reveal the development of diseases, mainly because many diseases are caused by gene products functioning in inappropriate locations.

In the study of neurodegenerative diseases, scientists have discovered that the products of certain genes are misplaced in neuron cells, which will cause abnormal cell function and promote the development of the disease.

GO includes all biological activities at all levels from the behavior of a single molecule to the entire organism, covering various processes, interactions, and regulatory mechanisms involved in gene products.

Despite the diversity of processes and pathways in biology, and the fact that GO does not currently provide a description of all the dynamics and dependencies required for a complete pathway, GO remains of significant value in the broad study of how gene products work together.

When studying tumor growth and spread, GO technology helps reveal how gene products work together to promote the rapid reproduction and movement of tumor cells, which can help provide inspiration for innovative treatment strategies.