scholarly journals Polyglot Programming in Applications Used for Genetic Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Robert M. Nowak
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Data Processing ◽  
Programming Languages ◽  
High Performance ◽  
Genetic Data ◽  
Web Browser ◽  
Data Process ◽  
Genetic Data Analysis

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

scholarly journals DolphinNext: A graphical user interface for creating, deploying and executing Nextflow pipelines

2019 ◽  
Author(s):  
Onur Yukselen ◽  
Osman Turkyilmaz ◽  
Ahmet Rasit Ozturk ◽  
Manuel Garber ◽  
Alper Kucukural
Keyword(s):  
User Interface ◽  
Data Processing ◽  
Programming Languages ◽  
High Throughput ◽  
High Performance ◽  
Scientific Discovery ◽  
Biological Research ◽  
Wide Range ◽  
The Face ◽  
Economic Constraints

ABSTRACTThe emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) are transforming biological research. The dramatic increase in the volume of data makes analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for a highly-parallel, flexible, portable, and reproducible data processing frameworks. Flexibility ensures that pipelines can support a variety of applications without requiring one-off modifications. Portability ensures that users can leverage computationally available resources and work within economic constraints. Reproducibility warrants credibility to the results and is particularly challenging in the face of the sheer volume of data and the complexity of processing pipelines that vary widely between users.Several platforms currently exist for the design and execution of complex pipelines (e.g. Galaxy, GenePattern, GeneProf). Unfortunately, these platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, Nextflow was implemented to simplify portable, scalable, and reproducible scientific pipelines using containers. We have used Nextflow capabilities as leverage and developed a user interface, DolphinNext, for creating, deploying, and executing complex Nextflow pipelines for high throughput data processing. The guiding principle of DolphinNext is to facilitate the building and deployment of complex pipelines using a modular approach implemented in a graphical interface. DolphinNext provides: 1. A drag and drop user interface that abstracts pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. A user interface to monitor pipeline execution that allows the re-initiation of pipelines at intermediate steps 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Seamless portability to distributed computational environments such as high-performance clusters or cloud computing environments.

Genetic Data Analysis

1991 ◽  
Vol 84 (6) ◽  
pp. 639-639
Author(s):  
Louis C. Rutledge
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis

Genetic Data Analysis

2021 ◽  
pp. 358-372
Author(s):  
M. Shamila ◽  
Amit Kumar Tyagi
Keyword(s):  
Data Analysis ◽  
Association Studies ◽  
Single Gene ◽  
Genetic Data ◽  
Building Blocks ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Human Beings ◽  
Genome Wide ◽  
Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

Genetic data analysis II

1997 ◽  
Vol 13 (9) ◽  
pp. 379 ◽  
Author(s):  
Jurg Ott
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis

Genetic Data Analysis II.

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 392 ◽  
Author(s):  
B. S. Weir
Keyword(s):  
Data Analysis ◽  
Genetic Data ◽  
Genetic Data Analysis
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