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"href": "installation.html#prerequisites",
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"title": "1 Installation of the OBITools",
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"section": "1.2 Prerequisites",
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"text": "1.2 Prerequisites\nThe OBITools4 are developped using the GO programming language, we stick to the latest version of the language, today the \\(1.21.4\\). If you want to download and compile the sources yourself, you first need to install the corresponding compiler on your system. Some parts of the soft are also written in C, therefore a recent C compiler is also requested, GCC on Linux or Windows, the Developer Tools on Mac.\nWhatever the installation you decide for, you will have to ensure that a C compiler is available on your system."
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"text": "1.2 Prerequisites\nThe OBITools4 are developped using the GO programming language, we stick to the latest version of the language, today the 1.21.41.21.4. If you want to download and compile the sources yourself, you first need to install the corresponding compiler on your system. Some parts of the soft are also written in C, therefore a recent C compiler is also requested, GCC on Linux or Windows, the Developer Tools on Mac.\nWhatever the installation you decide for, you will have to ensure that a C compiler is available on your system."
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"text": "1.4 Compilation from sources"
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"href": "formats.html#the-dna-sequence-data",
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"title": "2 File formats usable with OBITools",
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"section": "",
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"text": "3 The DNA sequence data\nSequences can be stored following various format. OBITools knows some of them. The central formats for sequence files manipulated by OBITools scripts are the FASTA and FASTQ format. OBITools extends the both these formats by specifying a syntax to include in the definition line data qualifying the sequence. All file formats use the IUPAC code for encoding nucleotides.\nMoreover these two formats that can be used as input and output formats, OBITools4 can read the following format :"
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"section": "2.1 The DNA sequence data",
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"text": "2.1 The DNA sequence data\nSequences can be stored following various format. OBITools knows some of them. The central formats for sequence files manipulated by OBITools scripts are the FASTA and FASTQ format. OBITools extends the both these formats by specifying a syntax to include in the definition line data qualifying the sequence. All file formats use the IUPAC code for encoding nucleotides.\nMoreover these two formats that can be used as input and output formats, OBITools4 can read the following format :\n\nEBML flat file format (use by ENA)\nGenbank flat file format\necoPCR output files\n\n\n2.1.1 The IUPAC Code\nThe International Union of Pure and Applied Chemistry (IUPAC) defined the standard code for representing protein or DNA sequences.\n\n\n\nCode\nNucleotide\n\n\n\n\nA\nAdenine\n\n\nC\nCytosine\n\n\nG\nGuanine\n\n\nT\nThymine\n\n\nU\nUracil\n\n\nR\nPurine (A or G)\n\n\nY\nPyrimidine (C, T, or U)\n\n\nM\nC or A\n\n\nK\nT, U, or G\n\n\nW\nT, U, or A\n\n\nS\nC or G\n\n\nB\nC, T, U, or G (not A)\n\n\nD\nA, T, U, or G (not C)\n\n\nH\nA, T, U, or C (not G)\n\n\nV\nA, C, or G (not T, not U)\n\n\nN\nAny base (A, C, G, T, or U)\n\n\n\n\n\n2.1.2 The FASTA sequence format\nThe FASTA format is certainly the most widely used sequence file format. This is certainly due to its great simplicity. It was originally created for the Lipman and Pearson FASTA program. OBITools use in more of the classical FASTA format an extended version of this format where structured data are included in the title line.\nIn FASTA format a sequence is represented by a title line beginning with a > character and the sequences by itself following the IUPAC code. The sequence is usually split other severals lines of the same length (expect for the last one)\n>my_sequence this is my pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\nThis is no special format for the title line excepting that this line should be unique. Usually the first word following the > character is considered as the sequence identifier. The end of the title line corresponding to a description of the sequence. Several sequences can be concatenated in a same file. The description of the next sequence is just pasted at the end of the record of the previous one\n>sequence_A this is my first pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n>sequence_B this is my second pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n>sequence_C this is my third pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n\n2.1.2.1 File extensions\nThere is no standard file extension for a FASTA file, but .fa and .fasta, are commonly used.\n\n\n\n2.1.3 The FASTQ sequence format1\nThe FASTQ format is a text file format for storing both biological sequences (only nucleic acid sequences) and the associated sequence quality scores. Every nucleotide of the sequence and its associated quality score are each encoded by a single ASCII character. This format was originally developed by the Wellcome Trust Sanger Institute to link a FASTA sequence file to the corresponding quality data, but is now became the de facto standard for storing results from high-throughput sequencers (Cock et al. 2010).\nOBITools considers that a FASTQ file uses four lines to encode a sequence record.\n\nLine 1 begins with a ‘@’ character and is followed by a sequence identifier and an optional description (like a FASTA title line).\nLine 2 is the sequence letters, in upper or lower case, but OBITools only write sequences as lower cases.\nLine 3 begins with a ‘+’ character and is optionally followed by the same sequence identifier (and any description) again.\nLine 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence.\n\nA FASTQ file looks like this:\n@SEQ_ID\nGATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT\n+\n!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65\nThe character ‘!’ represents the lowest quality while ‘~’ is the highest. Here are the quality value characters in left-to-right increasing order of quality (ASCII):\n!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]\n^_`abcdefghijklmnopqrstuvwxyz{|}~\nIf the original Sanger FASTQ files also allowed the sequence and quality strings to be wrapped (split over multiple lines), it is not supported by OBITools as it make parsing complicated due to the unfortunate choice of “@” and “+” as markers (these characters can also occur in the quality string).\n\nSequence quality Line truncated
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"href": "formats.html#the-taxonomy-files",
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"title": "2 File formats usable with OBITools",
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"section": "3.1 The IUPAC Code",
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"text": "3.1 The IUPAC Code\nThe International Union of Pure and Applied Chemistry (IUPAC) defined the standard code for representing protein or DNA sequences.\n\n\n\nCode\nNucleotide\n\n\n\n\nA\nAdenine\n\n\nC\nCytosine\n\n\nG\nGuanine\n\n\nT\nThymine\n\n\nU\nUracil\n\n\nR\nPurine (A or G)\n\n\nY\nPyrimidine (C, T, or U)\n\n\nM\nC or A\n\n\nK\nT, U, or G\n\n\nW\nT, U, or A\n\n\nS\nC or G\n\n\nB\nC, T, U, or G (not A)\n\n\nD\nA, T, U, or G (not C)\n\n\nH\nA, T, U, or C (not G)\n\n\nV\nA, C, or G (not T, not U)\n\n\nN\nAny base (A, C, G, T, or U)"
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"section": "2.2 The taxonomy files",
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"text": "2.2 The taxonomy files\nMany OBITools are able to take into account taxonomic data. This is done by specifying a directory containing all :doc:NCBI taxonomy dump files <./taxdump>."
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"href": "formats.html#the-sample-description-file",
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"title": "2 File formats usable with OBITools",
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"section": "3.2 The FASTA sequence format",
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"text": "3.2 The FASTA sequence format\nThe FASTA format is certainly the most widely used sequence file format. This is certainly due to its great simplicity. It was originally created for the Lipman and Pearson FASTA program. OBITools use in more of the classical FASTA format an extended version of this format where structured data are included in the title line.\nIn FASTA format a sequence is represented by a title line beginning with a > character and the sequences by itself following the IUPAC code. The sequence is usually split other severals lines of the same length (expect for the last one)\n>my_sequence this is my pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\nThis is no special format for the title line excepting that this line should be unique. Usually the first word following the > character is considered as the sequence identifier. The end of the title line corresponding to a description of the sequence. Several sequences can be concatenated in a same file. The description of the next sequence is just pasted at the end of the record of the previous one\n>sequence_A this is my first pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n>sequence_B this is my second pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n>sequence_C this is my third pretty sequence\nACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGT\nGTGCTGACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTACGTTGCAGTGTTT\nAACGACGTTGCAGTACGTTGCAGT\n\n3.2.1 File extensions\nThere is no standard file extension for a FASTA file, but .fa and .fasta, are commonly used."
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"title": "2 File formats usable with OBITools",
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"section": "3.3 The FASTQ sequence format1",
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"text": "3.3 The FASTQ sequence format1\nThe FASTQ format is a text file format for storing both biological sequences (only nucleic acid sequences) and the associated sequence quality scores. Every nucleotide of the sequence and its associated quality score are each encoded by a single ASCII character. This format was originally developed by the Wellcome Trust Sanger Institute to link a FASTA sequence file to the corresponding quality data, but is now became the de facto standard for storing results from high-throughput sequencers (Cock et al. 2010).\nOBITools considers that a FASTQ file uses four lines per sequence.\n\nLine 1 begins with a ‘@’ character and is followed by a sequence identifier and an optional description (like a FASTA title line).\nLine 2 is the sequence letters, in upper or lower case, but OBITools only write sequences as lower cases.\nLine 3 begins with a ‘+’ character and is optionally followed by the same sequence identifier (and any description) again.\nLine 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence.\n\nA FASTQ file looks like this:\n@SEQ_ID\nGATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT\n+\n!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65\nThe character ‘!’ represents the lowest quality while ‘~’ is the highest. Here are the quality value characters in left-to-right increasing order of quality (ASCII):\n!\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]\n^_`abcdefghijklmnopqrstuvwxyz{|}~\nIf the original Sanger FASTQ files also allowed the sequence and quality strings to be wrapped (split over multiple lines), it is not supported by OBITools as it make parsing complicated due to the unfortunate choice of “@” and “+” as markers (these characters can also occur in the quality string).\n\nSequence quality scores\nThe Phred quality value Q is an integer mapping of p (i.e., the probability that the corresponding base call is incorrect). Two different equations have been in use. The first is the standard Sanger variant to assess reliability of a base call, otherwise known as Phred quality score:\n\\[\nQ_\\text{sanger} = -10 \\, \\log_{10} p\n\\]\nThe Solexa pipeline (i.e., the software delivered with the Illumina Genome Analyzer) earlier used a different mapping, encoding the odds \\(\\mathbf{p}/(1-\\mathbf{p})\\) instead of the probability \\(\\mathbf{p}\\):\n\\[\nQ_\\text{solexa-prior to v.1.3} = -10 \\; \\log_{10} \\frac{p}{1-p}\n\\]\nAlthough both mappings are asymptotically identical at higher quality values, they differ at lower quality levels (i.e., approximately \\(\\mathbf{p} > 0.05\\), or equivalently, \\(\\mathbf{Q} < 13\\)).\n\n\n\nFigure 3.1: Relationship between Q and p using the Sanger (red) and Solexa (black) equations (described above). The vertical dotted line indicates \\(\\mathbf{p}= 0.05\\), or equivalently, \\(Q = 13\\).\n\n\n\nEncoding\nThe FASTQ format had differente way of encoding the Phred quality score along the time. Here a breif history of these changes is presented. OBITools\n\nSanger format can encode a Phred quality score from 0 to 93 using ASCII 33 to 126 (although in raw read data the Phred quality score rarely exceeds 60, higher scores are possible in assemblies or read maps).\nSolexa/Illumina 1.0 format can encode a Solexa/Illumina quality score from -5 to 62 using ASCII 59 to 126 (although in raw read data Solexa scores from -5 to 40 only are expected)\nStarting with Illumina 1.3 and before Illumina 1.8, the format encoded a Phred quality score from 0 to 62 using ASCII 64 to 126 (although in raw read data Phred scores from 0 to 40 only are expected).\nStarting in Illumina 1.5 and before Illumina 1.8, the Phred scores 0 to 2 have a slightly different meaning. The values 0 and 1 are no longer used and the value 2, encoded by ASCII 66 “B”.\n\n\nSequencing Control Software, Version 2.6, (Catalog # SY-960-2601, Part # 15009921 Rev. A, November 2009, page 30) states the following: If a read ends with a segment of mostly low quality (Q15 or below), then all of the quality values in the segment are replaced with a value of 2 (encoded as the letter B in Illumina’s text-based encoding of quality scores)… This Q2 indicator does not predict a specific error rate, but rather indicates that a specific final portion of the read should not be used in further analyses. Also, the quality score encoded as “B” letter may occur internally within reads at least as late as pipeline version 1.6, as shown in the following example:\n\n@HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1\nTTAATTGGTAAATAAATCTCCTAATAGCTTAGATNTTACCTTNNNNNNNNNNTAGTTTCTTGAGA\nTTTGTTGGGGGAGACATTTTTGTGATTGCCTTGAT\n+HWI-EAS209_0006_FC706VJ:5:58:5894:21141#ATCACG/1\nefcfffffcfeefffcffffffddf`feed]`]_Ba_^__[YBBBBBBBBBBRTT\\]][ dddd`\nddd^dddadd^BBBBBBBBBBBBBBBBBBBBBBBB\nAn alternative interpretation of this ASCII encoding has been proposed. ALine truncated
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"section": "2.3 The sample description file",
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"text": "2.3 The sample description file\nA key file for OBITools4 is the file describing all samples (PCR) analyzed in the processed sequencing library file. This file, often called the ngsfilter file, is a tab separated values (TSV) file. The format of this file is exactly identical to that used in OBITools2 and OBITools4.\n{tsv, .smaller} wolf_diet 13a_F730603 aattaac TTAGATACCCCACTATGC TAGAACAGGCTCCTCTAG F wolf_diet 15a_F730814 gaagtag TTAGATACCCCACTATGC TAGAACAGGCTCCTCTAG F wolf_diet 26a_F040644 gaatatc TTAGATACCCCACTATGC TAGAACAGGCTCCTCTAG F wolf_diet 29a_F260619 gcctcct TTAGATACCCCACTATGC TAGAACAGGCTCCTCTAG F\nAt least six columns must be present in every line of the file.\n\nThe first column contains the name of the experience:\nAn experiment name groups a set of sample together. Sequences belonging to the experiment are tagged with an attribute experiment containing the name of the experiment in their annotation.\nThe second column contains the sample identifier in the experiment\nThe sample identifier must be unique in the experiment. The obimultiplex and obitagpcr commands add to all the sequences bellonging to the same sample an attribute sample containing the sample identifier\nThe third column contains description of the tag used to identify sequences corresponding to this sample\nThe fourth column contains the forward primer sequence\nThe fifth column contains the reverse primer sequence\nThe sixth column must always contain the character F (full length)\n\n\n\n\n\nCock, Peter JA, Christopher J Fields, Naohisa Goto, Michael L Heuer, and Peter M Rice. 2010. “The Sanger FASTQ File Format for Sequences with Quality Scores, and the Solexa/Illumina FASTQ Variants.” Nucleic Acids Research 38 (6): 1767–71."
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@@ -81,14 +74,14 @@
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"href": "tutorial.html#step-by-step-analysis",
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"title": "3 OBITools V4 Tutorial",
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"section": "3.2 Step by step analysis",
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"text": "3.2 Step by step analysis\n\n3.2.1 Recover full sequence reads from forward and reverse partial reads\nWhen using the result of a paired-end sequencing assay with supposedly overlapping forward and reverse reads, the first step is to recover the assembled sequence.\nThe forward and reverse reads of the same fragment are at the same line position in the two fastq files obtained after sequencing. Based on these two files, the assembly of the forward and reverse reads is done with the obipairing utility that aligns the two reads and returns the reconstructed sequence.\nIn our case, the command is:\n\nobipairing --min-identity=0.8 \\\n --min-overlap=10 \\\n -F wolf_data/wolf_F.fastq \\\n -R wolf_data/wolf_R.fastq \\\n > results/wolf.fastq \n\nThe --min-identity and --min-overlap options allow discarding sequences with low alignment quality. If after the aligment, the overlaping parts of the reads is shorter than 10 base pairs or the similarity over this aligned region is below 80% of identity, in the output file, the forward and reverse reads are not aligned but concatenated, and the value of the mode attribute in the sequence header is set to joined instead of alignment.\n\n\n3.2.2 Remove unaligned sequence records\nUnaligned sequences (:pymode=joined) cannot be used. The following command allows removing them from the dataset:\n\nobigrep -p 'annotations.mode != \"join\"' \\\n results/wolf.fastq > results/wolf.ali.fastq\n\nThe -p requires a go like expression. annotations.mode != \"join\" means that if the value of the mode annotation of a sequence is different from join, the corresponding sequence record will be kept.\nThe first sequence record of wolf.ali.fastq can be obtained using the following command line:\n\nhead -n 4 results/wolf.ali.fastq\n\nThe folling piece of code appears on thew window of tour terminal.\n@HELIUM_000100422_612GNAAXX:7:108:5640:3823#0/1 {\"ali_dir\":\"left\",\"ali_length\":62,\"mode\":\"alignment\",\"pairing_mismatches\":{\"(T:26)->(G:13)\":62,\"(T:34)->(G:18)\":48},\"score\":484,\"score_norm\":0.968,\"seq_a_single\":46,\"seq_ab_match\":60,\"seq_b_single\":46}\nccgcctcctttagataccccactatgcttagccctaaacacaagtaattaatataacaaaattgttcgccagagtactaccggcaatagcttaaaactcaaaggacttggcggtgctttatacccttctagaggagcctgttctaaggaggcgg\n+\nCCCCCCCBCCCCCCCCCCCCCCCCCCCCCCBCCCCCBCCCCCCC<CcCccbe[`F`accXV<TA\\RYU\\\\ee_e[XZ[XEEEEEEEEEE?EEEEEEEEEEDEEEEEEECCCCCCCCCCCCCCCCCCCCCCCACCCCCACCCCCCCCCCCCCCCC\n\n\n3.2.3 Assign each sequence record to the corresponding sample/marker combination\nEach sequence record is assigned to its corresponding sample and marker using the data provided in a text file (here wolf_diet_ngsfilter.txt). This text file contains one line per sample, with the name of the experiment (several experiments can be included in the same file), the name of the tags (for example: aattaac if the same tag has been used on each extremity of the PCR products, or aattaac:gaagtag if the tags were different), the sequence of the forward primer, the sequence of the reverse primer, the letter T or F for sample identification using the forward primer and tag only or using both primers and both tags, respectively (see obimultiplex for details).\n\nobimultiplex -t wolf_data/wolf_diet_ngsfilter.txt \\\n -u results/unidentified.fastq \\\n results/wolf.ali.fastq \\\n > results/wolf.ali.assigned.fastq\n\nThis command creates two files:\n\nunidentified.fastq containing all the sequence records that were not assigned to a sample/marker combination\nwolf.ali.assigned.fastq containing all the sequence records that were properly assigned to a sample/marker combination\n\nNote that each sequence record of the wolf.ali.assigned.fastq file contains only the barcode sequence as the sequences of primers and tags are removed by the obimultiplex program. Information concerning the experiment, sample, primers and tags is added as attributes in the sequence header.\nFor instance, the first sequence record of wolf.ali.assigned.fastq is:\n@HELIUM_000100422_612GNAAXX:7:108:5640:3823#0/1_sub[28..127] {\"ali_dir\":\"left\",\"ali_length\":62,\"direction\":\"direct\",\"experiment\":\"wolf_diet\",\"forward_match\":\"ttagataccccactatgc\",\"forward_mismatches\":0,\"forward_primer\":\"ttagataccccactatgc\",\"forward_tag\":\"gcctcct\",\"mode\":\"alignment\",\"pairing_mismatches\":{\"(T:26)->(G:13)\":35,\"(T:34)->(G:18)\":21},\"reverse_match\":\"tagaacaggctcctctag\",\"reverse_mismatches\":0,\"reverse_primer\":\"tagaacaggctcctctag\",\"reverse_tag\":\"gcctcct\",\"sample\":\"29a_F260619\",\"score\":484,\"score_norm\":0.968,\"seq_a_single\":46,\"seq_ab_match\":60,\"seq_b_single\":46}\nttagccctaaacacaagtaattaatataacaaaattgttcgccagagtactaccggcaatagcttaaaactcaaaggacttggcggtgctttataccctt\n+\nCCCBCCCCCBCCCCCCC<CcCccbe[`F`accXV<TA\\RYU\\\\ee_e[XZ[XEEEEEEEEEE?EEEEEEEEEEDEEEEEEECCCCCCCCCCCCCCCCCCC\n\n\n3.2.4 Dereplicate reads into uniq sequences\nThe same DNA Line truncated
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"text": "3.2 Step by step analysis\n\n3.2.1 Recover full sequence reads from forward and reverse partial reads\nWhen using the result of a paired-end sequencing assay with supposedly overlapping forward and reverse reads, the first step is to recover the assembled sequence.\nThe forward and reverse reads of the same fragment are at the same line position in the two fastq files obtained after sequencing. Based on these two files, the assembly of the forward and reverse reads is done with the obipairing utility that aligns the two reads and returns the reconstructed sequence.\nIn our case, the command is:\n\nobipairing --min-identity=0.8 \\\n --min-overlap=10 \\\n -F wolf_data/wolf_F.fastq \\\n -R wolf_data/wolf_R.fastq \\\n > results/wolf.fastq \n\nThe --min-identity and --min-overlap options allow discarding sequences with low alignment quality. If after the aligment, the overlaping parts of the reads is shorter than 10 base pairs or the similarity over this aligned region is below 80% of identity, in the output file, the forward and reverse reads are not aligned but concatenated, and the value of the mode attribute in the sequence header is set to joined instead of alignment.\n\n\n3.2.2 Remove unaligned sequence records\nUnaligned sequences (:pymode=joined) cannot be used. The following command allows removing them from the dataset:\n\nobigrep -p 'annotations.mode != \"join\"' \\\n results/wolf.fastq > results/wolf.ali.fastq\n\nThe -p requires a go like expression. annotations.mode != \"join\" means that if the value of the mode annotation of a sequence is different from join, the corresponding sequence record will be kept.\nThe first sequence record of wolf.ali.fastq can be obtained using the following command line:\n\nhead -n 4 results/wolf.ali.fastq\n\nThe folling piece of code appears on thew window of tour terminal.\n@HELIUM_000100422_612GNAAXX:7:108:5640:3823#0/1 {\"ali_dir\":\"left\",\"ali_length\":62,\"mode\":\"alignment\",\"pairing_mismatches\":{\"(T:26)->(G:13)\":62,\"(T:34)->(G:18)\":48},\"score\":484,\"score_norm\":0.968,\"seq_a_single\":46,\"seq_ab_match\":60,\"seq_b_single\":46}\nccgcctcctttagataccccactatgcttagccctaaacacaagtaattaatataacaaaattgttcgccagagtactaccggcaatagcttaaaactcaaaggacttggcggtgctttatacccttctagaggagcctgttctaaggaggcgg\n+\nCCCCCCCBCCCCCCCCCCCCCCCCCCCCCCBCCCCCBCCCCCCC<CcCccbe[`F`accXV<TA\\RYU\\\\ee_e[XZ[XEEEEEEEEEE?EEEEEEEEEEDEEEEEEECCCCCCCCCCCCCCCCCCCCCCCACCCCCACCCCCCCCCCCCCCCC\n\n\n3.2.3 Assign each sequence record to the corresponding sample/marker combination\nEach sequence record is assigned to its corresponding sample and marker using the data provided in a text file (here wolf_diet_ngsfilter.txt). This text file contains one line per sample, with the name of the experiment (several experiments can be included in the same file), the name of the tags (for example: aattaac if the same tag has been used on each extremity of the PCR products, or aattaac:gaagtag if the tags were different), the sequence of the forward primer, the sequence of the reverse primer, the letter T or F for sample identification using the forward primer and tag only or using both primers and both tags, respectively (see obimultiplex for details).\n\nobimultiplex -t wolf_data/wolf_diet_ngsfilter.txt \\\n -u results/unidentified.fastq \\\n results/wolf.ali.fastq \\\n > results/wolf.ali.assigned.fastq\n\nThis command creates two files:\n\nunidentified.fastq containing all the sequence records that were not assigned to a sample/marker combination\nwolf.ali.assigned.fastq containing all the sequence records that were properly assigned to a sample/marker combination\n\nNote that each sequence record of the wolf.ali.assigned.fastq file contains only the barcode sequence as the sequences of primers and tags are removed by the obimultiplex program. Information concerning the experiment, sample, primers and tags is added as attributes in the sequence header.\nFor instance, the first sequence record of wolf.ali.assigned.fastq is:\n@HELIUM_000100422_612GNAAXX:7:108:5640:3823#0/1_sub[28..127] {\"ali_dir\":\"left\",\"ali_length\":62,\"direction\":\"direct\",\"experiment\":\"wolf_diet\",\"forward_match\":\"ttagataccccactatgc\",\"forward_mismatches\":0,\"forward_primer\":\"ttagataccccactatgc\",\"forward_tag\":\"gcctcct\",\"mode\":\"alignment\",\"pairing_mismatches\":{\"(T:26)->(G:13)\":35,\"(T:34)->(G:18)\":21},\"reverse_match\":\"tagaacaggctcctctag\",\"reverse_mismatches\":0,\"reverse_primer\":\"tagaacaggctcctctag\",\"reverse_tag\":\"gcctcct\",\"sample\":\"29a_F260619\",\"score\":484,\"score_norm\":0.968,\"seq_a_single\":46,\"seq_ab_match\":60,\"seq_b_single\":46}\nttagccctaaacacaagtaattaatataacaaaattgttcgccagagtactaccggcaatagcttaaaactcaaaggacttggcggtgctttataccctt\n+\nCCCBCCCCCBCCCCCCC<CcCccbe[`F`accXV<TA\\RYU\\\\ee_e[XZ[XEEEEEEEEEE?EEEEEEEEEEDEEEEEEECCCCCCCCCCCCCCCCCCC\n\n\n3.2.4 Dereplicate reads into uniq sequences\nThe same DNA Line truncated
|
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},
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"objectID": "inupt.html#specifying-input-format",
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"href": "inupt.html#specifying-input-format",
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"title": "4 Specifying the data input to OBITools commands",
|
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"section": "4.1 Specifying input format",
|
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"text": "4.1 Specifying input format\nFive sequence formats are accepted for input files. Fasta (Section 3.2) and Fastq (Section 3.3) are the main ones, EMBL and Genbank allow the use of flat files produced by these two international databases. The last one, ecoPCR, is maintained for compatibility with previous OBITools and allows to read ecoPCR outputs as sequence files.\n\n--ecopcr : Read data following the ecoPCR output format.\n--embl Read data following the EMBL flatfile format.\n--genbank Read data following the Genbank flatfile format.\n\nSeveral encoding schemes have been proposed for quality scores in Fastq format. Currently, OBITools considers Sanger encoding as the standard. For reasons of compatibility with older datasets produced with Solexa sequencers, it is possible, by using the following option, to force the use of the corresponding quality encoding scheme when reading these older files.\n\n--solexa Decodes quality string according to the Solexa specification. (default: false)"
|
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"text": "4.1 Specifying input format\nFive sequence formats are accepted for input files. Fasta (Section 2.1.2) and Fastq (Section 2.1.3) are the main ones, EMBL and Genbank allow the use of flat files produced by these two international databases. The last one, ecoPCR, is maintained for compatibility with previous OBITools and allows to read ecoPCR outputs as sequence files.\n\n--ecopcr : Read data following the ecoPCR output format.\n--embl Read data following the EMBL flatfile format.\n--genbank Read data following the Genbank flatfile format.\n\nSeveral encoding schemes have been proposed for quality scores in Fastq format. Currently, OBITools considers Sanger encoding as the standard. For reasons of compatibility with older datasets produced with Solexa sequencers, it is possible, by using the following option, to force the use of the corresponding quality encoding scheme when reading these older files.\n\n--solexa Decodes quality string according to the Solexa specification. (default: false)"
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},
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{
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"objectID": "output.html#specifying-output-format",
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@@ -130,7 +123,7 @@
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"href": "expressions.html#function-defined-in-the-language",
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"title": "7 OBITools expression language",
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"section": "7.2 Function defined in the language",
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"text": "7.2 Function defined in the language\n\nInstrospection functions\n\nlen(x)\n\nIt is a generic function allowing to retreive the size of a object. It returns the length of a sequences, the number of element in a map like annotations, the number of elements in an array. The reurned value is an int.\n\ncontains(map,key)\n\nTests if the map contains a value assciated to key\n\n\n\n\nCast functions\n\nint(x)\n\nConverts if possible the x value to an integer value. The function returns an int.\n\nnumeric(x)\n\nConverts if possible the x value to a float value. The function returns a float.\n\nbool(x)\n\nConverts if possible the x value to a boolean value. The function returns a bool.\n\n\n\n\nString related functions\n\nprintf(format,...)\n\nAllows to combine several values to build a string. format follows the classical C printf syntax. The function returns a string.\n\nsubspc(x)\n\nsubstitutes every space in the x string by the underscore (_) character. The function returns a string.\n\n\n\n\nCondition function\n\nifelse(condition,val1,val2)\n\nThe condition value has to be a bool value. If it is true the function returns val1, otherwise, it is returning val2.\n\n\n\n\n7.2.1 Sequence analysis related function\n\ncomposition(sequence)\n\nThe nucleotide composition of the sequence is returned as as map indexed by a, c, g, or t and each value is the number of occurrences of that nucleotide. A fifth key others accounts for all others symboles.\n\ngcskew(sequence)\n\nComputes the excess of g compare to c of the sequence, known as the GC skew.\n\\[\nSkew_{GC}=\\frac{G-C}{G+C}\n\\]"
|
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"text": "7.2 Function defined in the language\n\nInstrospection functions\n\nlen(x)\n\nIt is a generic function allowing to retreive the size of a object. It returns the length of a sequences, the number of element in a map like annotations, the number of elements in an array. The reurned value is an int.\n\ncontains(map,key)\n\nTests if the map contains a value assciated to key\n\n\n\n\nCast functions\n\nint(x)\n\nConverts if possible the x value to an integer value. The function returns an int.\n\nnumeric(x)\n\nConverts if possible the x value to a float value. The function returns a float.\n\nbool(x)\n\nConverts if possible the x value to a boolean value. The function returns a bool.\n\n\n\n\nString related functions\n\nprintf(format,...)\n\nAllows to combine several values to build a string. format follows the classical C printf syntax. The function returns a string.\n\nsubspc(x)\n\nsubstitutes every space in the x string by the underscore (_) character. The function returns a string.\n\n\n\n\nCondition function\n\nifelse(condition,val1,val2)\n\nThe condition value has to be a bool value. If it is true the function returns val1, otherwise, it is returning val2.\n\n\n\n\n7.2.1 Sequence analysis related function\n\ncomposition(sequence)\n\nThe nucleotide composition of the sequence is returned as as map indexed by a, c, g, or t and each value is the number of occurrences of that nucleotide. A fifth key others accounts for all others symboles.\n\ngcskew(sequence)\n\nComputes the excess of g compare to c of the sequence, known as the GC skew.\nSkewGC=G−CG+C\nSkew_{GC}=\\frac{G-C}{G+C}"
|
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{
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"objectID": "expressions.html#accessing-to-the-sequence-annotations",
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@@ -179,7 +172,7 @@
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"href": "comm_computation.html#obipairing",
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"title": "11 Computations on sequences",
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"section": "11.1 obipairing",
|
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"text": "11.1 obipairing\n\nReplace the illuminapairedends original OBITools\n\n\nAlignment procedure\nobipairing is introducing a new alignment algorithm compared to the illuminapairedend command of the OBITools V2. Nethertheless this new algorithm has been design to produce the same results than the previous, except in very few cases.\nThe new algorithm is a two-step procedure. First, a FASTN-type algorithm (Lipman and Pearson 1985) identifies the best offset between the two matched readings. This identifies the region of overlap.\nIn the second step, the matching regions of the two reads are extracted along with a flanking sequence of \\(\\Delta\\) base pairs. The two subsequences are then aligned using a “one side free end-gap” dynamic programming algorithm. This latter step is only called if at least one mismatch is detected by the FASTP step.\nUnless the similarity between the two reads at their overlap region is very low, the addition of the flanking regions in the second step of the alignment ensures the same alignment as if the dynamic programming alignment was performed on the full reads.\n\n\nThe scoring system\nIn the dynamic programming step, the match and mismatch scores take into account the quality scores of the two aligned nucleotides. By taking these into account, the probability of a true match can be calculated for each aligned base pair.\nIf we consider a nucleotide read with a quality score \\(Q\\), the probability of misreading this base (\\(P_E\\)) is : \\[\nP_E = 10^{-\\frac{Q}{10}}\n\\]\nThus, when a given nucleotide \\(X\\) is observed with the quality score \\(Q\\). The probability that \\(X\\) is really an \\(X\\) is :\n\\[\nP(X=X) = 1 - P_E\n\\]\nOtherwise, \\(X\\) is actually one of the three other possible nucleotides (\\(X_{E1}\\), \\(X_{E2}\\) or \\(X_{E3}\\)). If we suppose that the three reading error have the same probability :\n\\[\nP(X=X_{E1}) = P(X=X_{E3}) = P(X=X_{E3}) = \\frac{P_E}{3}\n\\]\nAt each position in an alignment where the two nucleotides \\(X_1\\) and \\(X_2\\) face each other (not a gapped position), the probability of a true match varies depending on whether \\(X_1=X_2\\), an observed match, or \\(X_1 \\neq X_2\\), an observed mismatch.\nProbability of a true match when \\(X_1=X_2\\)\nThat probability can be divided in two parts. First \\(X_1\\) and \\(X_2\\) have been correctly read. The corresponding probability is :\n\\[\n\\begin{aligned}\nP_{TM} &= (1- PE_1)(1-PE_2)\\\\\n &=(1 - 10^{-\\frac{Q_1}{10} } )(1 - 10^{-\\frac{Q_2}{10}} )\n\\end{aligned}\n\\]\nSecondly, a match can occure if the true nucleotides read as \\(X_1\\) and \\(X_2\\) are not \\(X_1\\) and \\(X_2\\) but identical.\n\\[\n\\begin{aligned}\nP(X_1==X_{E1}) \\cap P(X_2==X_{E1}) &= \\frac{P_{E1} P_{E2}}{9} \\\\\nP(X_1==X_{Ex}) \\cap P(X_2==X_{Ex}) & = \\frac{P_{E1} P_{E2}}{3}\n\\end{aligned}\n\\]\nThe probability of a true match between \\(X_1\\) and \\(X_2\\) when \\(X_1 = X_2\\) an observed match :\n\\[\n\\begin{aligned}\nP(MATCH | X_1 = X_2) = (1- PE_1)(1-PE_2) + \\frac{P_{E1} P_{E2}}{3}\n\\end{aligned}\n\\]\nProbability of a true match when \\(X_1 \\neq X_2\\)\nThat probability can be divided in three parts.\n\n\\(X_1\\) has been correctly read and \\(X_2\\) is a sequencing error and is actually equal to \\(X_1\\). \\[\nP_a = (1-P_{E1})\\frac{P_{E2}}{3}\n\\]\n\\(X_2\\) has been correctly read and \\(X_1\\) is a sequencing error and is actually equal to \\(X_2\\). \\[\nP_b = (1-P_{E2})\\frac{P_{E1}}{3}\n\\]\n\\(X_1\\) and \\(X_2\\) corresponds to sequencing error but are actually the same base \\(X_{Ex}\\) \\[\nP_c = 2\\frac{P_{E1} P_{E2}}{9}\n\\]\n\nConsequently : \\[\n\\begin{aligned}\nP(MATCH | X_1 \\neq X_2) = (1-P_{E1})\\frac{P_{E2}}{3} + (1-P_{E2})\\frac{P_{E1}}{3} + 2\\frac{P_{E1} P_{E2}}{9}\n\\end{aligned}\n\\]\nProbability of a match under the random model\nThe second considered model is a pure random model where every base is equiprobable, hence having a probability of occurrence of a nucleotide equals \\(0.25\\). Under that hypothesis\n\\[\nP(MATCH | \\text{Random model}) = 0.25\n\\]\nThe score is a log ration of likelyhood\nScore is define as the logarithm of the ratio between the likelyhood of the observations considering the sequencer error model over tha likelyhood u\n\n\n\n\n\nEvolution of the match and mismatch scores when the quality of base is 20 while the second range from 10 to 40."
|
||||
"text": "11.1 obipairing\n\nReplace the illuminapairedends original OBITools\n\n\nAlignment procedure\nobipairing is introducing a new alignment algorithm compared to the illuminapairedend command of the OBITools V2. Nethertheless this new algorithm has been design to produce the same results than the previous, except in very few cases.\nThe new algorithm is a two-step procedure. First, a FASTN-type algorithm (Lipman and Pearson 1985) identifies the best offset between the two matched readings. This identifies the region of overlap.\nIn the second step, the matching regions of the two reads are extracted along with a flanking sequence of Δ\\Delta base pairs. The two subsequences are then aligned using a “one side free end-gap” dynamic programming algorithm. This latter step is only called if at least one mismatch is detected by the FASTP step.\nUnless the similarity between the two reads at their overlap region is very low, the addition of the flanking regions in the second step of the alignment ensures the same alignment as if the dynamic programming alignment was performed on the full reads.\n\n\nThe scoring system\nIn the dynamic programming step, the match and mismatch scores take into account the quality scores of the two aligned nucleotides. By taking these into account, the probability of a true match can be calculated for each aligned base pair.\nIf we consider a nucleotide read with a quality score QQ, the probability of misreading this base (PEP_E) is : PE=10−Q10\nP_E = 10^{-\\frac{Q}{10}}\n\nThus, when a given nucleotide XX is observed with the quality score QQ. The probability that XX is really an XX is :\nP(X=X)=1−PE\nP(X=X) = 1 - P_E\n\nOtherwise, XX is actually one of the three other possible nucleotides (XE1X_{E1}, XE2X_{E2} or XE3X_{E3}). If we suppose that the three reading error have the same probability :\nP(X=XE1)=P(X=XE3)=P(X=XE3)=PE3\nP(X=X_{E1}) = P(X=X_{E3}) = P(X=X_{E3}) = \\frac{P_E}{3}\n\nAt each position in an alignment where the two nucleotides X1X_1 and X2X_2 face each other (not a gapped position), the probability of a true match varies depending on whether X1=X2X_1=X_2, an observed match, or X1≠X2X_1 \\neq X_2, an observed mismatch.\nProbability of a true match when X1=X2X_1=X_2\nThat probability can be divided in two parts. First X1X_1 and X2X_2 have been correctly read. The corresponding probability is :\nPTM=(1−PE1)(1−PE2)=(1−10−Q110)(1−10−Q210)\n\\begin{aligned}\nP_{TM} &= (1- PE_1)(1-PE_2)\\\\ \n &=(1 - 10^{-\\frac{Q_1}{10} } )(1 - 10^{-\\frac{Q_2}{10}} )\n\\end{aligned}\n\nSecondly, a match can occure if the true nucleotides read as X1X_1 and X2X_2 are not X1X_1 and X2X_2 but identical.\nP(X1==XE1)∩P(X2==XE1)=PE1PE29P(X1==XEx)∩P(X2==XEx)=PE1PE23\n\\begin{aligned}\nP(X_1==X_{E1}) \\cap P(X_2==X_{E1}) &= \\frac{P_{E1} P_{E2}}{9} \\\\\nP(X_1==X_{Ex}) \\cap P(X_2==X_{Ex}) & = \\frac{P_{E1} P_{E2}}{3}\n\\end{aligned}\n\nThe probability of a true match between X1X_1 and X2X_2 when X1=X2X_1 = X_2 an observed match :\nP(MATCH|X1=X2)=(1−PE1)(1−PE2)+PE1PE23\n\\begin{aligned}\nP(MATCH | X_1 = X_2) = (1- PE_1)(1-PE_2) + \\frac{P_{E1} P_{E2}}{3}\n\\end{aligned}\n\nProbability of a true match when X1≠X2X_1 \\neq X_2\nThat probability can be divided in three parts.\n\nX1X_1 has been correctly read and X2X_2 is a sequencing error and is actually equal to X1X_1. Pa=(1−PE1)PE23\nP_a = (1-P_{E1})\\frac{P_{E2}}{3}\n\nX2X_2 has been correctly read and X1X_1 is a sequencing error and is actually equal to X2X_2. Pb=(1−PE2)PE13\nP_b = (1-P_{E2})\\frac{P_{E1}}{3}\n\nX1X_1 and X2X_2 corresponds to sequencing error but are actually the same base XExX_{Ex} Pc=2PE1PE29\nP_c = 2\\frac{P_{E1} P_{E2}}{9}\n\n\nConsequently : P(MATCH|X1≠X2)=(1−PE1)PE23+(1−PE2)PE13+2PE1PE29\n\\begin{aligned}\nP(MATCH | X_1 \\neq X_2) = (1-P_{E1})\\frac{P_{E2}}{3} + (1-P_{E2})\\frac{P_{E1}}{3} + 2\\frac{P_{E1} P_{E2}}{9}\n\\end{aligned}\n\nProbability of a match under the random model\nThe second considered model is a pure random model where every base is equiprobable, hence having a probability of occurrence of a nucleotide equals 0.250.25. Under that hypothesis\nP(MATCH|Random model)=0.25\nP(MATCH | \\text{Random model}) = 0.25\n\nThe score is a log ration of likelyhood\nScore is define as the logarithm of the ratio between the likelyhood of the observations considering the sequencer error model over tha likelyhood u\n\n\n\n\n\nEvolution of the match and mismatch scores when the quality of base is 20 while the second range from 10 to 40."
|
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|
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"objectID": "comm_computation.html#obimultiplex",
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@@ -214,7 +207,7 @@
|
||||
"href": "comm_sampling.html#obigrep-filters-sequence-files-according-to-numerous-conditions",
|
||||
"title": "12 Sequence sampling and filtering",
|
||||
"section": "12.1 obigrep – filters sequence files according to numerous conditions",
|
||||
"text": "12.1 obigrep – filters sequence files according to numerous conditions\nThe obigrep command is somewhat analogous to the standard Unix grep command. It selects a subset of sequence records from a sequence file. A sequence record is a complex object consisting of an identifier, a set of attributes (a key, defined by its name, associated with a value), a definition, and the sequence itself. Instead of working text line by text line like the standard Unix tool, obigrep selection is done sequence record by sequence record. A large number of options allow you to refine the selection on any element of the sequence. obigrep allows you to specify multiple conditions simultaneously (which take on the value TRUE or FALSE) and only those sequence records which meet all conditions (all conditions are TRUE) are selected. obigrep is able to work on two paired read files. The selection criteria apply to one or the other of the readings in each pair depending on the mode chosen (--paired-mode option). In all cases the selection is applied in the same way to both files, thus maintaining their consistency.\n\n12.1.1 The options usable with obigrep\n\n12.1.1.1 Selecting sequences based on their caracteristics\nSequences can be selected on several of their caracteristics, their length, their id, their sequence. Options allow for specifying the condition if selection.\nSelection based on the sequence\nSequence records can be selected according if they match or not with a pattern. The simplest pattern is as short sequence (e.g AACCTT). But the usage of regular patterns allows for looking for more complex pattern. As example, A[TG]C+G matches a A, followed by a T or a G, then one or several C and endly a G.\n\n--sequence|-s PATTERN\n\nRegular expression pattern to be tested against the sequence itself. The pattern is case insensitive. A complete description of the regular pattern grammar is available here.\n\nExamples:\n\nSelects only the sequence records that contain an EcoRI restriction site.\n\n\nobigrep -s 'GAATTC' seq1.fasta > seq2.fasta\n: Selects only the sequence records that contain a stretch of at least 10 A.\nobigrep -s 'A{10,}' seq1.fasta > seq2.fasta\n: Selects only the sequence records that do not contain ambiguous nucleotides.\nobigrep -s '^[ACGT]+$' seq1.fasta > seq2.fasta\n\n--min-count | -c COUNT\n\nonly sequences reprensenting at least COUNT reads will be selected. That option rely on the count attribute. If the count attribute is not defined for a sequence record, it is assumed equal to \\(1\\).\n\n--max-count | -C COUNT\n\nonly sequences reprensenting no more than COUNT reads will be selected. That option rely on the count attribute. If the count attribute is not defined for a sequence record, it is assumed equal to \\(1\\).\n\nExamples\n\nSelecting sequence records representing at least five reads in the dataset.\n\n\nobigrep -c 5 data_SPER01.fasta > data_norare_SPER01.fasta"
|
||||
"text": "12.1 obigrep – filters sequence files according to numerous conditions\nThe obigrep command is somewhat analogous to the standard Unix grep command. It selects a subset of sequence records from a sequence file. A sequence record is a complex object consisting of an identifier, a set of attributes (a key, defined by its name, associated with a value), a definition, and the sequence itself. Instead of working text line by text line like the standard Unix tool, obigrep selection is done sequence record by sequence record. A large number of options allow you to refine the selection on any element of the sequence. obigrep allows you to specify multiple conditions simultaneously (which take on the value TRUE or FALSE) and only those sequence records which meet all conditions (all conditions are TRUE) are selected. obigrep is able to work on two paired read files. The selection criteria apply to one or the other of the readings in each pair depending on the mode chosen (--paired-mode option). In all cases the selection is applied in the same way to both files, thus maintaining their consistency.\n\n12.1.1 The options usable with obigrep\n\n12.1.1.1 Selecting sequences based on their caracteristics\nSequences can be selected on several of their caracteristics, their length, their id, their sequence. Options allow for specifying the condition if selection.\nSelection based on the sequence\nSequence records can be selected according if they match or not with a pattern. The simplest pattern is as short sequence (e.g AACCTT). But the usage of regular patterns allows for looking for more complex pattern. As example, A[TG]C+G matches a A, followed by a T or a G, then one or several C and endly a G.\n\n--sequence|-s PATTERN\n\nRegular expression pattern to be tested against the sequence itself. The pattern is case insensitive. A complete description of the regular pattern grammar is available here.\n\nExamples:\n\nSelects only the sequence records that contain an EcoRI restriction site.\n\n\nobigrep -s 'GAATTC' seq1.fasta > seq2.fasta\n: Selects only the sequence records that contain a stretch of at least 10 A.\nobigrep -s 'A{10,}' seq1.fasta > seq2.fasta\n: Selects only the sequence records that do not contain ambiguous nucleotides.\nobigrep -s '^[ACGT]+$' seq1.fasta > seq2.fasta\n\n--min-count | -c COUNT\n\nonly sequences reprensenting at least COUNT reads will be selected. That option rely on the count attribute. If the count attribute is not defined for a sequence record, it is assumed equal to 11.\n\n--max-count | -C COUNT\n\nonly sequences reprensenting no more than COUNT reads will be selected. That option rely on the count attribute. If the count attribute is not defined for a sequence record, it is assumed equal to 11.\n\nExamples\n\nSelecting sequence records representing at least five reads in the dataset.\n\n\nobigrep -c 5 data_SPER01.fasta > data_norare_SPER01.fasta"
|
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},
|
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{
|
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"objectID": "comm_utilities.html#obicount",
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Reference in new issue
Block a user