| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
05/10/2020 |
|
Data da última atualização: |
06/10/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
MUDADU, M. de A.; ZERLOTINI NETO, A. |
|
Afiliação: |
MAURICIO DE ALVARENGA MUDADU, CNPTIA; ADHEMAR ZERLOTINI NETO, CNPTIA. |
|
Título: |
Machado: open source genomics data integration framework. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
GigaScience, v. 9, n. 9, p. 1-16, Sept. 2020. |
|
DOI: |
10.1093/gigascience/giaa097 |
|
Idioma: |
Inglês |
|
Notas: |
Na publicação: Adhemar Zerlotini. |
|
Conteúdo: |
Abstract. Background: Genome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it. Findings: We have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters. Conclusion: Machado aims to be a modern object-relational framework that uses the latest Python libraries to produce an effective open source resource for genomics research. MenosAbstract. Background: Genome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it. Findings: We have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters... Mostrar Tudo |
|
Palavras-Chave: |
Chado; Dados genômicos; Multiomics. |
|
Thesagro: |
Base de Dados. |
|
Thesaurus Nal: |
Genomics; Python. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216418/1/AP-Machado-2020.pdf
|
|
Marc: |
LEADER 02322naa a2200229 a 4500
001 2125289
005 2020-10-06
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1093/gigascience/giaa097$2DOI
100 1 $aMUDADU, M. de A.
245 $aMachado$bopen source genomics data integration framework.$h[electronic resource]
260 $c2020
500 $aNa publicação: Adhemar Zerlotini.
520 $aAbstract. Background: Genome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it. Findings: We have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters. Conclusion: Machado aims to be a modern object-relational framework that uses the latest Python libraries to produce an effective open source resource for genomics research.
650 $aGenomics
650 $aPython
650 $aBase de Dados
653 $aChado
653 $aDados genômicos
653 $aMultiomics
700 1 $aZERLOTINI NETO, A.
773 $tGigaScience$gv. 9, n. 9, p. 1-16, Sept. 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 108 | |
| Registros recuperados : 108 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
