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author matthias
date Fri, 08 Mar 2019 06:36:53 -0500
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1 TODO
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2 ====
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6 If we make a monolithic tool:
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8 * implement sanity checks between important compute intensive steps (user definable criteria, abort if violated)
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10 If we keep separate tools:
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12 - make Rdata data types specific (like xmcs https://github.com/workflow4metabolomics/xcms/tree/dev/datatypes)
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13 * alternatively the data set types could be derived from tabular and the Rdata could be attached via
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14 `.extra_files_path` this way the user could have some intermediate output that he could look at.
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17 In both cases:
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19 * allow input of single end data, single pair, single pair in separate data sets, ...
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20 * add mergePairsByID functionality to mergePairs tool
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23 Datatypes:
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24 ==========
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26 **derep-class**: list w 3 members
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27 - uniques: Named integer vector. Named by the unique sequence, valued by abundance.
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28 • quals: Numeric matrix of average quality scores by position for each unique. Uniques are
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29 rows, positions are cols.
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30 * map: Integer vector of length the number of reads, and value the index (in uniques) of the
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31 unique to which that read was assigned.
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33 **learnErrorsOutput**: A named list with three entries
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34 - err_out: A numeric matrix with the learned error rates.
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35 - err_in: The initialization error rates (unimportant).
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36 - trans: A feature table of observed transitions for each type (eg. A->C) and quality score.
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38 **dada-class**: A multi-item List with the following named values...
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39 • denoised: Integer vector, named by sequence valued by abundance, of the denoised sequences.
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40 • clustering: An informative data.frame containing information on each cluster.
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41 • sequence: A character vector of each denoised sequence. Identical to names(denoised).
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42 • quality: The average quality scores for each cluster (row) by position (col).
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43 • map: Integer vector that maps the unique (index of derep.unique) to the denoised sequence (index of dada.denoised).
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44 • birth_subs: A data.frame containing the substitutions at the birth of each new cluster.
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45 • trans: The matrix of transitions by type (row), eg. A2A, A2C..., and quality score (col)
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46 observed in the final output of the dada algorithm.
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47 • err_in: The err matrix used for this invocation of dada.
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48 • err_out: The err matrix estimated from the output of dada. NULL if err_function not provided.
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49 • opts: A list of the dada_opts used for this invocation of dada.
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50 • call: The function call used for this invocation of dada.
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52 **uniques**: derep, dada, mergepairs(or data frame w sequenc and abundance columns)
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54 **mergepairs**:
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56 data.frame(s) has a row for each unique pairing of forward/reverse denoised sequences, and the following columns:
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57 • abundance: Number of reads corresponding to this forward/reverse combination.
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58 • sequence: The merged sequence.
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59 • forward: The index of the forward denoised sequence.
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60 • reverse: The index of the reverse denoised sequence.
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61 • nmatch: Number of matches nts in the overlap region.
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62 • nmismatch: Number of mismatches in the overlap region.
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63 • nindel: Number of indels in the overlap region.
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64 • prefer: The sequence used for the overlap region. 1=forward; 2=reverse.
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65 • accept: TRUE if overlap between forward and reverse denoised sequences was at least minOverlap and had at most maxMismatch differences. FALSE otherwise.
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66 • ...: Additional columns specified in propagateCol
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70 Tools:
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71 ======
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73 • Quality filtering
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75 filterAndTrim IO=(fastq -> fastq)
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77 • Dereplication
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78
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79 derepFastq (fastq -> derep-class object)
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80
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81 • Learn error rates
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82
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83 learnErrors + plotErrors
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84 - in: input list, or vector, of file names (or a list of derep-class objects WHY .. learning should be done on full data)
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85 - out: named list w entries
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86 - \$err\_out: A numeric matrix with the learned error rates.
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87 - \$err\_in: The initialization error rates (unimportant).
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88 - \$trans: A feature table of observed transitions for each type (eg. A->C) and quality score
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89
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90 • Sample Inference (dada)
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91 in: (list of) derep-class object
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92 out: (list of) dada-class object
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93
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94 • Chimera Removal
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95
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96 removeBimeraDenovo
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97
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98 in: A uniques-vector or any object that can be coerced into one with getUniques.
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99 out: A uniques vector, or an object of matching class if a data.frame or sequence table is provided
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100
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101 • Merging of Paired Reads
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102
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103 mergePairs
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104 in: 2x dada-class object(s), 2x derep-class object(s)
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105 out: A data.frame, or a list of data.frames.
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106 - The return data.frame(s) has a row for each unique pairing of forward/reverse denoised sequences,
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107 - cols
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108 - \$abundance: Number of reads corresponding to this forward/reverse combination.
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109 - \$sequence: The merged sequence.
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110 - \$forward: The index of the forward denoised sequence.
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111 - \$reverse: The index of the reverse denoised sequence.
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112 - \$nmatch: Number of matches nts in the overlap region.
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113 - \$nmismatch: Number of mismatches in the overlap region.
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114 - \$nindel: Number of indels in the overlap region.
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115 - \$prefer: The sequence used for the overlap region. 1=forward; 2=reverse.
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116 - \$accept: TRUE if overlap between forward and reverse denoised sequences was at least minOverlap and had at most maxMismatch differences. FALSE otherwise.
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117 - \$...: Additional columns specified in propagateCol.
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118
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119 • Taxonomic Classification (assignTaxonomy, assignSpecies)
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120
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121 * Other
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122
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123 makeSequenceTable
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124 in A list of the samples to include in the sequence table. Samples can be provided in any format that can be processed by getUniques
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125 out Named integer matrix (row for each sample, column for each unique sequence)
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126
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127 mergeSequenceTables
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128
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129 uniquesToFasta
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130 in: A uniques-vector or any object that can be coerced into one with getUniques.
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131
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132 getSequences
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133
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134 extracts the sequences from several different data objects: including including dada-class
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135 and derep-class objects, as well as data.frame objects that have both \$sequence and \$abun-
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136 dance columns.
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137
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138 getUniques
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139
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140 extracts the uniques-vector from several different data objects, including dada-class
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141 and derep-class objects, as well as data.frame objects that have both \$sequence and \$abundance
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142 columns
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143
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144 plotQualityProfile
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145
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146 seqComplexity
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147
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148 setDadaOpt(...)