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市场调查报告书
商品编码
1423574
到 2030 年的基因预测工具市场预测:按组件、应用、最终用户和地区进行的全球分析Gene Prediction Tools Market Forecasts to 2030 - Global Analysis By Component (Software and Service), Application, End User and By Geography |
根据Stratistics MRC预测,2023年全球基因预测工具市场规模将达到1,273亿美元,预计2030年将达到4,641亿美元,预测期内复合年增长率为20.3%。
基因预测工具是一种计算演算法,用于识别和註释 DNA 序列中潜在的蛋白质转录区。这些工具使用统计模型、隐马可夫模型或机器学习技术来分析基因序列并预测基因的位置、编码区、外显子-内含子边界和调控元件。这些工具有助于了解基因结构,促进各种生物体的基因註释、功能分析和基因组註释,为基因组学和分子生物学研究做出巨大贡献。
根据 2021 年生物资讯简报,一个名为 TSSFinder 的程式被用来说明真核物种的启动子序列。使用基于线性链条件随机场(LCCRF)的随机模型的转录起始位点(TSS)预测方法是TSSFinder,其最初在文献中公开。
个人化医疗需求不断成长
对个人化医疗日益增长的需求正在推动基因预测工具的进步。这些工具使用遗传资讯来预测个体患某些疾病的可能性、对某些药物的反应以及潜在的副作用。遗传预测工具透过分析个体的遗传资料并识别与疾病和药物代谢相关的突变来帮助客製化治疗计划。
资料品质和复杂性
由于基因组序列变异,基因预测工具面临资料品质挑战,导致预测错误。基因结构的复杂性,包括选择性剪接和重复基因,增加了影响准确性的复杂性。此外,大量可用的基因组资料带来了计算挑战,需要强大的演算法来处理不同的基因组特征。因此,资料品质的限制和复杂性阻碍了准确的遗传预测,影响了生物学解释和下游分析。
扩大生物资讯学的采用
生物资讯学在遗传预测工具的进步中发挥着至关重要的作用,并正在推动其广泛普及。生物资讯学利用计算演算法和生物资料,实现准确的基因辨识、註释和功能分析。其整合采用序列分析、机器学习和统计方法来优化预测模型并破解遗传密码。对生物资讯学的日益依赖确保了各个生物研究领域基因预测工具的准确性、扩充性和可访问性的提高。
严格的监管审批流程
基因预测工具严格的监管审批流程有几个缺点。它通常会导致更长的开发时间并延迟基因研究和临床应用的重要创新。高监管障碍限制了可近性和可负担性,并阻止小型研究团体和公司提供新颖的解决方案。此外,如此严格的流程可能会因为偏向既定工具而扼杀创新,阻碍更有效和突破性技术的出现。
遗传预测工具在识别病毒基因、突变和功能成分方面发挥了重要作用。研究人员使用这些工具不仅可以识别潜在的候选疫苗,还可以评估其安全性和有效性。然而,随着疫情蔓延,由于资源重新分配、封锁和研究活动中断,许多与 COVID-19 无关的研究计划被迫取消或推迟。因此,基因预测仪器在COVID-19以外的研究领域的接受和应用已经放缓。
软体部分预计将在预测期内成为最大的部分
预计软体部门将成为预测期内最大的部门。这些工具使用复杂的演算法来分析基因组资料并准确预测基因位置、结构和功能。该市场的主要参与者提供用户友好的介面,并确保研究人员和临床医生的可访问性。软体提高基因预测准确性的能力极大地促进了基因组研究和个人化医疗的进步,并正在推动市场扩张。随着基因组资料的不断增加,对高效可靠的基因预测工具的需求预计将进一步推动市场成长。
诊断领域预计在预测期内复合年增长率最高
诊断领域预计在预测期内复合年增长率最高。这些工具在识别与疾病相关的遗传变异和支持个人化医疗方面发挥着重要作用。对准确、高效的诊断解决方案的需求正在刺激基因预测工具的创新,创造一个竞争激烈的市场形势。主要企业专注于提高诊断准确性、扩展应用程式和增强用户友好介面,为整体市场的成长和发展做出贡献。
预计北美在预测期内将占据最大的市场占有率。人们对基因研究的兴趣和投资不断增长,特别是在个人化医疗、癌症研究和农业等领域,正在推动对复杂基因预测工具的需求。该地区的生物技术和製药公司正在大力投资与基因编辑、治疗和诊断相关的研发,进一步推动市场成长。
预计亚太地区在预测期内将维持最高的复合年增长率。有几个因素促成了这种扩张,包括基因组研究和开发活动的活性化、生物技术和製药领域投资的增加以及个人化医疗意识的增强。各国透过对生命科学研究的大量投资为这个市场做出了重大贡献。这些国家拥有充满活力的生物技术产业和进行广泛基因组研究的学术机构。
According to Stratistics MRC, the Global Gene Prediction Tools Market is accounted for $127.3 billion in 2023 and is expected to reach $464.1 billion by 2030 growing at a CAGR of 20.3% during the forecast period. Gene prediction tools are computational algorithms used to identify and annotate potential protein-coding regions within DNA sequences. They analyze genetic sequences, employing statistical models, hidden Markov models, or machine learning techniques to predict the locations of genes, coding regions, exon-intron boundaries, and regulatory elements. These tools aid in understanding genetic structures, facilitating gene annotation, functional analysis, and genome annotation in various organisms, contributing significantly to genomics and molecular biology research.
According to Briefings in Bioinformatics in 2021, a program called TSSFinder was used to describe the promoter sequences from Eukaryotic species. The initial transcription start site (TSS) prediction approach that uses a probabilistic model based on linear chain conditional random fields (LCCRFs) is TSSFinder, which was originally disclosed in the literature.
Rising demand for personalized medicines
The increasing demand for personalized medicine has been a driving force in the advancement of gene prediction tools. These tools utilize genetic information to predict an individual's likelihood of developing certain diseases, their response to particular medications, and potential adverse reactions. Gene prediction tools analyze an individual's genetic data to identify variations associated with diseases or drug metabolism, aiding in customizing treatment plans.
Data quality and complexity
Gene prediction tools face challenges with data quality due to variations in genome sequences, leading to errors in predictions. The complexity of gene structures, including alternative splicing and overlapping genes, adds intricacy, affecting accuracy. Moreover, the vast amount of available genomic data poses computational challenges, requiring robust algorithms to handle diverse genomic features. Consequently, limitations in data quality and complexities hinder precise gene predictions, impacting biological interpretations and downstream analyses.
Growing adoption of bioinformatics
Bioinformatics plays a pivotal role in advancing gene prediction tools, driving their widespread adoption. Leveraging computational algorithms and biological data, bioinformatics enables accurate gene identification, annotation, and functional analysis. Its integration optimizes predictive models, employing sequence analysis, machine learning, and statistical methods to decipher genetic codes. The growing reliance on bioinformatics ensures enhanced precision, scalability, and accessibility of Gene Prediction Tools in various biological research domains.
Stringent regulatory approval process
The stringent regulatory approval process for gene prediction tools poses several disadvantages. It often leads to prolonged development timelines, delaying crucial innovations in genetic research and clinical applications. High regulatory barriers limit accessibility and affordability, impeding smaller research groups or companies from contributing novel solutions. Additionally, these stringent processes may stifle innovation by favouring established tools, hindering the emergence of potentially more effective or groundbreaking technologies.
Gene prediction tools played a crucial role in identifying viral genes, mutations, and functional components. These tools were used by researchers to evaluate the safety and efficacy of possible vaccine candidates in addition to identifying them. However, as the epidemic spread, numerous research projects unrelated to COVID-19 had to be cancelled or postponed due to resource reallocation, lockdowns, and disruptions in research activity. Consequently, there was a deceleration in the acceptance and application of gene prediction instruments in research domains other than COVID-19.
The software segment is expected to be the largest during the forecast period
The software segment is expected to be the largest during the forecast period. These tools utilize sophisticated algorithms to analyze genomic data, accurately predicting gene locations, structures, and functions. Key players in this market offer user-friendly interfaces, ensuring accessibility for researchers and clinicians. The software's ability to enhance precision in gene prediction significantly contributes to advancements in genomics research and personalized medicine, driving the market's expansion. As genomic data continues to grow, the demand for efficient and reliable gene prediction tools is expected to further propel market growth.
The diagnostics segment is expected to have the highest CAGR during the forecast period
The diagnostics segment is expected to have the highest CAGR during the forecast period. These tools play a crucial role in identifying genetic variations associated with diseases, aiding in personalized medicine. The demand for accurate and efficient diagnostic solutions has spurred innovation in gene prediction tools, creating a competitive market landscape. Key players are focusing on improving diagnostic accuracy, expanding applications, and enhancing user-friendly interfaces, contributing to the overall growth and evolution of the market.
North America is projected to hold the largest market share during the forecast period. Growing interest and investment in genetic research, particularly in fields like personalized medicine, cancer research, and agriculture, have increased the demand for sophisticated gene prediction tools. Biotech and pharmaceutical companies in the region are investing heavily in research and development related to gene editing, therapeutics, and diagnostics, further propelling the market growth.
Asia Pacific is projected to hold the highest CAGR over the forecast period. Several factors were contributing to this expansion, including increased research and development activities in genomics, rising investments in biotechnology and pharmaceutical sectors, and growing awareness about personalized medicine. Countries were key contributors to this market due to their substantial investments in life sciences research. These nations had vibrant biotechnology industries and academic institutions conducting extensive genomic research.
Key players in the market
Some of the key players in Gene Prediction Tools market include AZoLifeSciences, Illumina, Inc., BGI Genomics, Geneious, Thermo Fisher Scientific, Inc., Genscript, Exiqon, Softberry Technologies, Qiagen NV, National Human Genome Research Institute, New England Biolabs, ERS Genomics, Horizon Discovery Ltd., Sangamo Therapeutics, Takara Bio Inc. and Merck KGaA.
In June 2023, Illumina, Inc. disclosed the AI software for predicting disease-causing genetic mutations in patients. Through this, the company has enhanced its product portfolio and revenue growth.
In March 2022, Illumina, Inc. announced the launch of TruSigh Oncology (TSO) Comprehensive (EU). It is an examination that assesses various tumor of a patient's malignancy.