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agosto, 2015:

MetAMOS

General information

MetAMOS represents a focused effort to create automated, reproducible, traceable assembly & analysis infused with current best practices and state-of-the-art methods. MetAMOS for input can start with next-generation sequencing reads or assemblies, and as output, produces: assembly reports, genomic scaffolds, open-reading frames, variant motifs, taxonomic or functional annotations, Krona charts and HTML report. 1.5rc3 version.

How to use

To send a job to the queue system there is the

send_metamos

command where you answer a few questions to set up the job. Take into account that MetAMOS use a lot of RAM memory, about 1 GB per million reads.

More information

MetAMOS web page.

MetAMOS

Informazio orokorra

MetAMOS represents a focused effort to create automated, reproducible, traceable assembly & analysis infused with current best practices and state-of-the-art methods. MetAMOS for input can start with next-generation sequencing reads or assemblies, and as output, produces: assembly reports, genomic scaffolds, open-reading frames, variant motifs, taxonomic or functional annotations, Krona charts and HTML report. 1.5rc3 version.

Nola erabili

Koletara lanak bidaltzeko

send_metamos

komandoa erabili daiteke eta egiten dituen galderak erantzuten. Kontutan hartu MetAMOS memoria asko behar duela, gutxi gorabehera RAM GB bat milioi read bakoitzeko.

Informazio gehiago

MetAMOS web orrialdea.

MetAMOS

Información general

MetAMOS represents a focused effort to create automated, reproducible, traceable assembly & analysis infused with current best practices and state-of-the-art methods. MetAMOS for input can start with next-generation sequencing reads or assemblies, and as output, produces: assembly reports, genomic scaffolds, open-reading frames, variant motifs, taxonomic or functional annotations, Krona charts and HTML report. 1.5rc3 version.

Cómo usar

Para enviar trabajos a la cola se puede usar el comando

send_metamos

que realiza unas preguntas para configurar el cálculo. Hay que tener en cuenta que MetAMOS usa mucha memoria RAM, entorno a 1 GB por millón de reads.

Más información

Página web de MetAMOS.

QIIME

Information orokorra

QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. QIIME is designed to take users from raw sequencing data generated on the Illumina or other platforms through publication quality graphics and statistics. This includes demultiplexing and quality filtering, OTU picking, taxonomic assignment, and phylogenetic reconstruction, and diversity analyses and visualizations. QIIME has been applied to studies based on billions of sequences from tens of thousands of samples

Nola erabili

QIIME lanak bidaltzeko exekutatu

send_qiime

eta erantzun galdereí.

USEARCH

QIIME USEARCH paketea erabili dezake.

Informazio gehiago

QIIME home page.

USEARCH.

 

 

QIIME

General information

QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics pipeline for performing microbiome analysis from raw DNA sequencing data. QIIME is designed to take users from raw sequencing data generated on the Illumina or other platforms through publication quality graphics and statistics. This includes demultiplexing and quality filtering, OTU picking, taxonomic assignment, and phylogenetic reconstruction, and diversity analyses and visualizations. QIIME has been applied to studies based on billions of sequences from tens of thousands of samples

 How to use

To send QIIME jobs run the command

send_qiime

and answer the questions.

USEARCH

QIIME can use the USEARCH pakage.

More information

QIIME home page.

USEARCH.