Bioinformatics clouds for big data manipulation

Dai, Lin; Gao, Xin; Guo, Yan; Xiao, Jingfa; Zhang, Zhang

doi:10.1186/1745-6150-7-43

Cited by 137 publications

(78 citation statements)

References 43 publications

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“…Усовершенствование вычислительных методов, зада-ча которых связать omics-данные с медицинскими дан-ными, расшифровать механизмы, лежащие в основе генетических вариаций, разработать четкие персонали-зированные и профилактические стратегии, а также поиск потенциальных биомаркеров для лечения заболе-вания, крайне необходимо. Чтобы достигнуть этого, нужно способствовать укреплению междисциплинарных связей по всему миру и организации совместных работ биоло-гов, врачей, кураторов, биоинформатиков, IT-разра-ботчиков и т. д. Кроме того, высокопроизводительные вычис лительные технологии нужны, чтобы использовать их для ускорения работы инструментов биоинформатики, которые часто используются для работы с большим объ-емом информации [58].…”

Section: проблемы персонализированной медициныunclassified

Personalized Medicine in Cardiology: State, Problems and Prospects

2017

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ФАРМАКОГЕНЕТИКАВозможность владеть генетической информацией позволила начать внедрение персонализированной медицины в клиническую практику. Генетические иссле-дования начали показывать, что некоторые лекарства в определенных условиях очень эффективны для одних пациентов и неэффективны, а иногда даже опасны -для других. Поэтому на стыке фармакологии и генетики воз-никла фармакогенетика -наука, изучающая роль генети-ческих факторов в формировании фармакологического ответа организма человека на лекарственное средство [1]. Это может дать возможность врачу персонализиро-ванно выбрать как само лекарство, так и дозу у конкрет-ного больного и обеспечить максимальную эффектив-ность и безопасность препарата.Роль наследственности в формировании индивиду-ального ответа на лекарственные средства была известна давно, понимание механизмов влияния гене-тических факторов на эффективность и безопасность фармакотерапии стало возможным лишь с развитием методов молекулярной биологии и реализацией меж-дународной программы «Геном человека». Последние данные, имеющиеся о строении генома человека, явно показывают, что существует множество вариаций генов, которые объясняют различия в абсорбции пре-парата, распределении, метаболизме, выведении и в итоге ответную реакцию организма на фармацевтиче-ский препарат. Такие предварительные успехи дали понять, что подобное исследование позволит получить такую медицину, которая окажется и индивидуальной, и точной. Personalized medicine appeared about three decades ago. Even then many scientists, researchers, doctors were interested into it. However, only after the full opening of the human genome in 2001, personalized medicine could to give the opportunity for some pathologies (oncology, rheumatology, cardiology) to choose the most effective treatment with minimal adverse drug reactions for a specific patient. The opportunity to own genetic information gives the chance to suspect, to predict, to expect the onset of the disease. Personalized medicine is able in some cases to say exactly whether the drug can work for the patient, moving us to «treat not a disease, but the patient.» But doctors to this day when determining risk factors focus on individual characteristics of the patient, such as age, gender, body weight, co-morbidities, and based on that prescribe medications, correct dose and in case of ineffectiveness change the treatment regimen. Apparently they don't believe in personalized medicine, afraid of it and I don't want to associate with it, because as many think on the basis of their experience and knowledge they can decide which drug and what dose to prescribe. Also, there are a number of reasons related to privacy, security, problem of training, which will soon need to be addressed.

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Section: проблемы персонализированной медициныunclassified

Personalized Medicine in Cardiology: State, Problems and Prospects

2017

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“…Clouds have been widely used in bioinformatics [47], [48], [49], [50], some of which have already been mentioned above. Cloud computing provides resources, data storage and processing as a service on a "pay as you go" basis, using a Service Level Agreement (SLA) [51] protocol.…”

Section: Cloudmentioning

confidence: 99%

Biomarker discovery, high performance and cloud computing: A comprehensive review

Blayney

Haberland

Lightbody

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Abstract-The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

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“…The development of tools supporting NGS data analysis with cloud computing has recently become popular in the open-source community [8]. Currently, there are many pipelines and workflows that support cloud computing ( Table 2).…”

Section: Open-source Toolsmentioning

confidence: 99%

“…A promising solution to address this computational challenge is cloud computing [7][8][9][10], where CPU, memory, and storage are accessible in the form of virtual machines (VMs). In recent years, cloud computing has spread very rapidly for the supply of IT resources (hardware and software) of different nature, and is emerging as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing for Next-Generation Sequencing Data Analysis

Zhao¹,

Watrous²,

Zhang³

et al. 2017

Cloud Computing - Architecture and Applications

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High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.

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Bioinformatics clouds for big data manipulation

Cited by 137 publications

References 43 publications

Personalized Medicine in Cardiology: State, Problems and Prospects

Personalized Medicine in Cardiology: State, Problems and Prospects

Biomarker discovery, high performance and cloud computing: A comprehensive review

Cloud Computing for Next-Generation Sequencing Data Analysis

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