DOC_ID : T11-0001 Doc_ID: A08-0001NCBIBioProj33317scikit0.24.1Editor: AnitaReviewer: Description 參考QIIME2 forum上的文章，使用RESCRIPt下載來自NCBI Genbank的序列和分類，並訓練適用於QIIME2分析的分類器。 Ref : https://forum.qiime2.org/t/using-rescript-to-compile-sequence-databases-and-taxonomy-classifiers-from-ncbi-genbank/15947 Source Download URL : – File size : ncbi-refseqs-unfiltered.qza 198KB ncbi-refseqs-taxonomy-unfiltered.qza 19.1KB Genome assemble version : BioProj33317 Detail information : 使用RESCRIPt下載來自NCBI Genbank的序列和分類，並訓練適用於QIIME2分析的classifiers，請確認已完成qiime2 standard analysis environment的安裝 #Activate standard analysis environmentconda activate qiime2 #移動到/work/使用者帳號資料夾cd /work/u5777333/ #創建放置參考序列及相應的分類法文件的資料夾mkdir -p NCBIclassifier/BioProject_33317 #移動至資料夾cd NCBIclassifier/BioProject_33317 #安裝RESCRIPtconda install -c conda-forge -c bioconda -c qiime2 -c defaults xmltodict pip install git+https://github.com/bokulich-lab/RESCRIPt.git # 使用RESCRIPt下載來自NCBI Genbank的序列和分類資料qiime rescript get-ncbi-data \ –p-query ‘33317[BioProject]’ \ –o-sequences ncbi-refseqs-unfiltered.qza \ –o-taxonomy ncbi-refseqs-taxonomy-unfiltered.qza #Filter unusually short 16S rRNA gene sequencesqiime rescript filter-seqs-length-by-taxon \ –i-sequences ncbi-refseqs-unfiltered.qza \ –i-taxonomy ncbi-refseqs-taxonomy-unfiltered.qza \ –p-labels Archaea Bacteria \ –p-min-lens 900 1200 \ –o-filtered-seqs ncbi-refseqs.qza \ –o-discarded-seqs ncbi-refseqs-tooshort.qza #using the –m-ids-to-keep-file parameter to only […]
DOC_ID : T11-0001 Doc_ID: A08-0001NCBIBioProj33175scikit0.24.1Editor: AnitaReviewer: Description 參考QIIME2 forum上的文章，使用RESCRIPt下載來自NCBI Genbank的序列和分類，並訓練適用於QIIME2分析的分類器。 Ref : https://forum.qiime2.org/t/using-rescript-to-compile-sequence-databases-and-taxonomy-classifiers-from-ncbi-genbank/15947 Source Download URL : – File size : ncbi-refseqs-unfiltered.qza 4.64MB ncbi-refseqs-taxonomy-unfiltered.qza 377KB Genome assemble version : BioProj33175 Detail information : 使用RESCRIPt下載來自NCBI Genbank的序列和分類，並訓練適用於QIIME2分析的classifiers，請確認已完成qiime2 standard analysis environment的安裝 #Activate standard analysis environmentconda activate qiime2 #移動到/work/使用者帳號資料夾cd /work/u5777333/ #創建放置參考序列及相應的分類法文件的資料夾mkdir -p NCBIclassifier/BioProject_33175 #移動至資料夾cd NCBIclassifier/BioProject_33175 #安裝RESCRIPtconda install -c conda-forge -c bioconda -c qiime2 -c defaults xmltodict pip install git+https://github.com/bokulich-lab/RESCRIPt.git # 使用RESCRIPt下載來自NCBI Genbank的序列和分類資料qiime rescript get-ncbi-data \ –p-query ‘33175[BioProject]’ \ –o-sequences ncbi-refseqs-unfiltered.qza \ –o-taxonomy ncbi-refseqs-taxonomy-unfiltered.qza #Filter unusually short 16S rRNA gene sequencesqiime rescript filter-seqs-length-by-taxon \ –i-sequences ncbi-refseqs-unfiltered.qza \ –i-taxonomy ncbi-refseqs-taxonomy-unfiltered.qza \ –p-labels Archaea Bacteria \ –p-min-lens 900 1200 \ –o-filtered-seqs ncbi-refseqs.qza \ –o-discarded-seqs ncbi-refseqs-tooshort.qza #using the –m-ids-to-keep-file parameter to only […]
Genome Reference:NCBIBioProj33175scikit0.24.1
DOC_ID : T11-0001 Doc_ID: A08-0001RDPtraset18scikit0.24.1Editor: AnitaReviewer: Description 利用RDP Classifier提供的包含RDP 分類器的參考序列和相應的分類法文件，透過q2-feature-classifier 工具來訓練適用於QIIME2分析的分類器。 Ref : https://john-quensen.com/classifying/rdp-classifier-updated/ Source Download URL : https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/RDPClassifier_16S_trainsetNo18_QiimeFormat.zip/download File size : Ref_taxonomy.txt 2.61MB RefOTUs.fa 29.5MB Genome assemble version : bacterial and archaeal training set No.18 Detail information : 使用q2-feature-classifier version 2021.8來訓練classifiers，請確認已完成qiime2 standard analysis environment的安裝 #Activate standard analysis environment conda activate qiime2 #移動到/work/使用者帳號資料夾 cd /work/u5777333/ #創建放置參考序列及相應的分類法文件的資料夾mkdir RDPclassifier #移動至資料夾 cd RDPclassifier/ #下載RDP Classifier提供的資料，解壓縮後上傳到建立的資料夾內(參考序列須先轉為大寫的序列) #import data into QIIME 2 Artifactsqiime tools import –type ‘FeatureData[Sequence]’ –input-path RefOTUs_reUpper.fa –output-path RefOTUs.qza qiime tools import –type ‘FeatureData[Taxonomy]’ –input-format HeaderlessTSVTaxonomyFormat –input-path Ref_taxonomy.txt –output-path ref-taxonomy.qza #Extract reference reads qiime feature-classifier extract-reads –i-sequences RefOTUs.qza –p-f-primer GTGCCAGCMGCCGCGGTAA –p-r-primer GGACTACHVGGGTWTCTAAT –p-trunc-len 120 –p-min-length 100 –p-max-length 400 –o-reads […]
Genome Reference:RDPtraset18scikit0.24.1
DOC_ID : T11-0001 Doc_ID: A08-0001Greengenes138NBscikit0.24.1 Editor : Anita Reviewer: Description Taxonomic classifiers perform best when they are trained based on your specific sample preparation and sequencing parameters, including the primers that were used for amplification and the length of your sequence reads. Greengene taxonomy classifiers is developing and maintaining by QIIME2. Source Download URL : https://data.qiime2.org/2021.8/common/gg-13-8-99-515-806-nb-classifier.qza File size : 141MB Genome assemble version : Greengenes 13_8 99% OTUs from 515F/806R region of sequences Detail information : The classifier supply from QIIME2 officer web by Naive Bayes classifiers trained on Greengenes 13_8 99% OTUs from 515F/806R region of sequences. The classifier was trained using scikit-learn […]
Genome Reference:Greengenes138NBscikit0.24.1
DOC_ID : T11-0001 Doc_ID: A08-0001Silva138NBscikit0.24.1 Editor : Anita Reviewer: Description Taxonomic classifiers perform best when they are trained based on your specific sample preparation and sequencing parameters, including the primers that were used for amplification and the length of your sequence reads. Silva taxonomy classifiers is developing and maintaining by QIIME2. Source Download URL : https://data.qiime2.org/2021.8/common/silva-138-99-515-806-nb-classifier.qza File size : 141MB Genome assemble version : Silva 138 99% OTUs from 515F/806R region of sequences Detail information : The classifier supply from QIIME2 officer web by Naive Bayes classifiers trained on Silva 138 99% OTUs from 515F/806R region of sequences. The classifier was trained using scikit-learn 0.24.1, […]
Genome Reference:Silva138NBscikit0.24.1
DOC_ID : T11-0001 Doc_ID: A08-0001GRCm39en104Star275aEditor: MiraReviewer: Description RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. Build RSEM references using RefSeq, Ensembl, or GENCODE annotationsRefSeq and Ensembl are two frequently used annotations. For human and mouse, GENCODE annotaions are also available. Here, we show how to build RSEM references using Ensembl annotation. […]
Genome Reference:GRCm39en104Star2.7.5a
Doc_ID: A08-0001GRCm38en101Star275a Editor: Mira Reviewer : Anita Description RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. Build RSEM references using RefSeq, Ensembl, or GENCODE annotationsRefSeq and Ensembl are two frequently used annotations. For human and mouse, GENCODE annotaions are also available. In this, we show how to build RSEM references using […]
Genome Reference:GRCm38en101Star2.7.5a
DOC_ID : T11-0001 Doc_ID: A08-0001GRCh38en104Star275aEditor: MiraReviewer: hsujc Description RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. Build RSEM references using RefSeq, Ensembl, or GENCODE annotationsRefSeq and Ensembl are two frequently used annotations. For human and mouse, GENCODE annotaions are also available. Here, we show how to build RSEM references using Ensembl […]