Cutevariant: a GUI-based desktop application to explore genetics variations

AbstractCutevariant is a user-friendly GUI based desktop application for genomic research designed to search for variations in DNA samples collected in annotated files and encoded in the Variant Calling Format. The application imports data into a local relational database wherefrom complex filter-queries can be built either from the intuitive GUI or using a Domain Specific Language (DSL). Cutevariant provides more features than any existing applications without compromising on performance. The plugin based architecture provides highly customizable features. Cutevariant is distributed as a multiplatform client-side software under an open source licence and is available at https://github.com/labsquare/Cutevariant. It has been designed from the beginning to be easily adopted by IT-agnostic end-users.

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NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

10.1101/367755 ◽

2018 ◽

Cited By ~ 2

Author(s):

Luis Pedro Coelho ◽

Renato Alves ◽

Paulo Monteiro ◽

Jaime Huerta-Cepas ◽

Ana Teresa Freitas ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Domain Specific Language ◽

Next Generation ◽

Specific Language ◽

Sequence Processing ◽

Domain Specific ◽

Link Type ◽

Fast Processing ◽

User Friendly

AbstractNGLess is a domain specific language for describing next-generation sequence processing pipelines. It was developed with the goal of enabling user-friendly computational reproducibility.Using this framework, we developed NG-meta-profiler, a fast profiler for metagenomes which performs sequence preprocessing, mapping to bundled databases, filtering of the mapping results, and profiling (taxonomic and functional). It is significantly faster than either MOCAT2 or htseq-count and (as it builds on NGLess) its results are perfectly reproducible. These pipelines can easily be customized and extended with other tools.NGLess and NG-meta-profiler are open source software (under the liberal MIT licence) and can be downloaded from http://ngless.embl.de or installed through bioconda.

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Analisis dan Perancangan Domain Specific Language untuk Data Generator pada Relational Database

JUMANJI (Jurnal Masyarakat Informatika Unjani) ◽

10.26874/jumanji.v3i01.52 ◽

2019 ◽

Vol 3 (01) ◽

pp. 64

Author(s):

Vania Natali ◽

Pascal Alfadian

Keyword(s):

Relational Database ◽

Domain Specific Language ◽

Specific Language ◽

Domain Specific ◽

Data Generator ◽

Data Definition

Data dummy seringkali diperlukan dalam proses belajar basis data, pengujian perangkat lunak, pengujian performansi basis data, dan lain-lain. Proses pembuatan data dummy seringkali memerlukan waktu yang tidak sedikit. Penelitian ini bertujuan untuk merancang Domain Specific Language (DSL) yang dapat digunakan dalam kakas generator data. DSL yang dikembangkan didasarkan pada Data Definition Language (DDL) dari basis data relasional dengan spesifikasi pada sintaks create table. Kontribusi penelitian ini adalah DSL dan grammar yang dapat digunakan untuk melakukan proses parsing DDL yang akan menjadi bagian dari kakas generator data.

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ПРОСТОРНО - ВРЕМЕНСКИ ТИПОВИ И АНАЛИЗЕ ПОДАТАКА У BIG DATA ПАРАДИГМИ

AГГ+ ◽

10.7251/aggplus1503066a ◽

2015 ◽

Vol 1 (3) ◽

Author(s):

Младен Амовић ◽

Миро Говедарица ◽

Владимир Пајић ◽

Славко Васиљевић

Keyword(s):

Big Data ◽

Open Source ◽

Apache Spark ◽

Domain Specific Language ◽

Specific Language ◽

Domain Specific

Модел за управљање великим серијама просторно-временских података имплементиран је на Apache Spark open-source платформи за складиштење и обраду великих серија података на дистрибуираним рачунарским системима формираним од комерцијално доступних радних станица. Алгоритми за обраду просторно-временских података су дефинисани према правилима Spark SQL програмског модела, а релационе операције на DataFrame-овима (специјализованим системом оквира података) коришћењем специфичног језика домена (domain – specific – language → DSL). Увођењем просторно-временских типова података омогућава се стандардизован приступ у Big Data парадигми.

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