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This repository contains code used in the multiomic analyses of faecal microbiota from four families with several cases of T1DM ( __MuSt__ ).

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to build a search data base for [proteomics](proteomics-data-base.md) from predicted proteins and their variants:
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  * [rename4proteomics.pl](rename4proteomics.pl)
  * [trypsinStartEnd.pl](trypsinStartEnd.pl)
  * [variant_annotateRepairedTab.pl](variant_annotateRepairedTab.pl)
  * [variants_annotateTab4Stats.pl](variants_annotateTab4Stats.pl)
  * [variants_locateType.pl](variants_locateType.pl)
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to parse [functional annotations](functional-annotations.md) of gene predictions (some including [coverage](calculating-coverage.md)):
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  * [150310_MUST_hmmBestAll.py](150310_MUST_hmmBestAll.py)
  * [150705_MUST_hmmParse.py](150705_MUST_hmmParse.py)
  * [150705_MUST_hmmParsePfam.py](150705_MUST_hmmParsePfam.py)
  * [consolidate_hmmscan_results.pl](consolidate_hmmscan_results.pl)
  * [consolidate_hmmscan_results_justKEGG.pl](consolidate_hmmscan_results_justKEGG.pl)
  * [150705_MUST_keggParseNW.py](150705_MUST_keggParseNW.py)
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  * ko2des_clean.txt - pretty big text [file](ko2des_clean.txt)
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  * [calculateCoverageAndGaps2.pl](calculateCoverageAndGaps2.pl)
  * [150322_bestHmmReadParse.py](150322_bestHmmReadParse.py)
  * [150415_bestHmmAveCovParse.py](150415_bestHmmAveCovParse.py)
  * [150630_keggReadParse.py](150630_keggReadParse.py)
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to annotate phylogenetic [marker genes with the taxonomy](annotate-phylogenetic-marker-genes.md) of the best hit from the mOTU database:
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  * [fastaExtractWithCoordBase1.pl](fastaExtractWithCoordBase1.pl)
  * [getValidGenes1.R](getValidGenes1.R)
  * [getValidGenes2new.R](getValidGenes2new.R)
  * [getHitPhylogenyNew.R](getHitPhylogenyNew.R)
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to parse taxonomy of [MG-RAST annotations](taxonomic-MG-RAST-annotations.md) of genes:
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  * [parse_taxbrowser_MGRAST.py](parse_taxbrowser_MGRAST.py)
  * [MGRASTgeneLevelTax.R](MGRASTgeneLevelTax.R)
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to automatically [cluster](automatic-clustering.md) contigs based on nucleotide signature (BH-SNE maps), DNA coverage and essential genes:
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  * [autoCluster.R](autoCluster.R)
  * [fastaExtractCutRibosomal1000.pl](fastaExtractCutRibosomal1000.pl)
  * [makeWSvarAnnoCorrect.R](makeWSvarAnnoCorrect.R)
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to gather contig clusters by [related phylogenetic marker genes in a phylogenetic tree](phylogenetic-marker-genes-trees.md):
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  * [150819_MUST_tree.R](150819_MUST_tree.R)
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  * [150816_getPhyloMarkers.R](150816_getPhyloMarkers.R)
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to [reconstruct](reconstructed-KO-network.md) a metabolic network from KOs and analyse it:
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  * [140630_MUST_NW.R](140630_MUST_NW.R)
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  * the above script needs [file](150705_KOs_in_NW.tsv) 150705_KOs_in_NW.tsv
  * [runHeinz.sh](runHeinz.sh)
  * [plotModules_omicLevels.R](plotModules_omicLevels.R)
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to feed a [mongo database](mongo-database.md) with all the data from MuSt and retrieve some of the data:
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  * [150928_mongifyMust.py](150928_mongifyMust.py)
  * [151020_funOIMongoWS.R](151020_funOIMongoWS.R)
  * [150928_MUST_relatedClusterWSFromMongo.R](150928_MUST_relatedClusterWSFromMongo.R)
  * [eukaryoticGenesMongo.R](eukaryoticGenesMongo.R)
  * [virusGenesMongo.R](virusGenesMongo.R)
  * [getNWexprMongoAllSamples.R](getNWexprMongoAllSamples.R)
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