INFORMATION INFRASTRUCTURE FOR SUPPORTING BAIKAL MICROBIOME RESEARCH

Рассмотрена проблема построения исследовательской среды для обработки данных секвенирования нового поколения (NGS – Next Generation Sequencing). Среда включает облачное хранилище данных (DaaS) и вычислительные службы (SaaS и PaaS), а также службы визуализации и интеграции данных. Осуществляется интеграция технологий с открытым исходным кодом для поддержки MiSeq SOP (стандартная операционная процедура), которая позволяет специалистам в предметной области – биологам независимо от программистов самостоятельно обрабатывать данные. Для реализации интеграции конструируются формальные модели SOP, позволяющие автоматически порождать исходный код компонентов среды. Технология преобразования основана на принципах архитектуры, управляемой моделями (Model driven architecture), и логическом выводе структур производных моделей и модулей. Представлены текущие результаты и задачи на ближайшую перспективу. A problem of construction of a research environment for Next Generation Sequencing data processing is considered. The environment comprises cloud data storage (DaaS) and computational services (SaaS and PaaS), as well as visualization, and data integration services. We are integrating existing open-source technologies to support MiSeq SOP (standard operational procedure), which is to allow domain specialists, biologists, to process data independently. For the realization of the integration, formal models of the SOP are constructed, automatically processed (transformed) into source code of new components. The technique of the transformation is based on Model Driven Architecture principles and logical inference of the derived models and the code. The current results are presented and discussed.

Download Full-text

Faculty Opinions recommendation of VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718272765.793499663 ◽

2014 ◽

Author(s):

Gary Bader ◽

Mohamed Helmy

Keyword(s):

Next Generation Sequencing ◽

Network Analysis ◽

Next Generation Sequencing Data ◽

Cancer Genes ◽

Next Generation ◽

Sequencing Data ◽

Generation Sequencing

Download Full-text

Faculty Opinions recommendation of Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727775566.793536095 ◽

2017 ◽

Author(s):

Steve Pereira

Keyword(s):

Pancreatic Cancer ◽

Next Generation Sequencing ◽

Precision Medicine ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Assisted Analysis ◽

Generation Sequencing

Download Full-text

NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab174 ◽

2021 ◽

Author(s):

Anne Krogh Nøhr ◽

Kristian Hanghøj ◽

Genis Garcia Erill ◽

Zilong Li ◽

Ida Moltke ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Research ◽

Likelihood Estimation ◽

Software Tool ◽

Estimation Methods ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Ngs Data ◽

Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

Download Full-text

recoup: flexible and versatile signal visualization from next generation sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03902-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Panagiotis Moulos

Keyword(s):

Next Generation Sequencing ◽

Next Generation Sequencing Data ◽

Special Focus ◽

Next Generation ◽

Sequencing Data ◽

User Friendliness ◽

Computational Environment ◽

Level Data ◽

Data Signal ◽

Generation Sequencing

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

Download Full-text