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  "Title": "Accurate, high-resolution sample inference from amplicon\nsequencing data",
  "Description": "The dada2 package infers exact amplicon sequence variants\n(ASVs) from high-throughput amplicon sequencing data, replacing\nthe coarser and less accurate OTU clustering approach. The\ndada2 pipeline takes as input demultiplexed fastq files, and\noutputs the sequence variants and their sample-wise abundances\nafter removing substitution and chimera errors. Taxonomic\nclassification is available via a native implementation of the\nRDP naive Bayesian classifier, and species-level assignment to\n16S rRNA gene fragments by exact matching.",
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  "Maintainer": "Benjamin Callahan <benjamin.j.callahan@gmail.com>",
  "Author": "Benjamin Callahan <benjamin.j.callahan@gmail.com>, Paul\nMcMurdie, Susan Holmes",
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        "dada2"
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      "title": "High resolution sample inference from amplicon data.",
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      "title": "The object class returned by 'dada'",
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      "title": "A class representing dereplicated sequences",
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      "title": "Read in and dereplicate a fastq file.",
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      "topics": [
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      "title": "An empirical error matrix.",
      "topics": [
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      "title": "Filter and trim a fastq file.",
      "topics": [
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    {
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      "title": "Filters and trims paired forward and reverse fastq files.",
      "topics": [
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      ]
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      "title": "Filter and trim fastq file(s).",
      "topics": [
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    {
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      "title": "Get DADA options",
      "topics": [
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    {
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      "title": "Extract already computed error rates.",
      "topics": [
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    },
    {
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      "title": "Get vector of sequences from input object.",
      "topics": [
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    {
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      "title": "Get the uniques-vector from the input object.",
      "topics": [
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    {
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      "title": "Inflates an error rate matrix by a specified factor, while accounting for saturation.",
      "topics": [
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    {
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      "title": "Determine if input sequence is a bimera of putative parent sequences.",
      "topics": [
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    },
    {
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      "title": "Identify bimeras from collections of unique sequences.",
      "topics": [
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    },
    {
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      "title": "Identify bimeras in a sequence table.",
      "topics": [
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    {
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      "title": "Determine if input sequence(s) match the phiX genome.",
      "topics": [
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    },
    {
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      "title": "Identify sequences that are identical to a more abundant sequence up to an overall shift.",
      "topics": [
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    {
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      "title": "Learns the error rates from an input list, or vector, of file names or a list of 'derep-class' objects.",
      "topics": [
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    {
      "page": "loessErrfun",
      "title": "Use a loess fit to estimate error rates from transition counts.",
      "topics": [
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    },
    {
      "page": "makeBinnedQualErrfun",
      "title": "Create a function that uses a piecewise linear fit to estimate error rates from transition counts derived from binned quality score data. The binned quality scores are defined in the argument to this function call.",
      "topics": [
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    {
      "page": "makeSequenceTable",
      "title": "Construct a sample-by-sequence observation matrix.",
      "topics": [
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    },
    {
      "page": "mergePairs",
      "title": "Merge denoised forward and reverse reads.",
      "topics": [
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    },
    {
      "page": "mergeSequenceTables",
      "title": "Merge two or more sample-by-sequence observation matrices.",
      "topics": [
        "mergeSequenceTables"
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    },
    {
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      "title": "Deactivate renaming of dada-class objects.",
      "topics": [
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    },
    {
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      "title": "Deactivate renaming of derep-class objects.",
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    {
      "page": "noqualErrfun",
      "title": "Estimate error rates for each type of transition while ignoring quality scores.",
      "topics": [
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    {
      "page": "nwalign",
      "title": "Needleman-Wunsch alignment.",
      "topics": [
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      "title": "Hamming distance after Needlman-Wunsch alignment.",
      "topics": [
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    {
      "page": "PacBioErrfun",
      "title": "Estimate error rates from transition counts in PacBio CCS data.",
      "topics": [
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    {
      "page": "plotComplexity",
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