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comparison microsatellite.xml @ 5:b27006b0a953

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author devteam@galaxyproject.org
date Wed, 22 Apr 2015 12:19:28 -0400
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4:ecfc9041bcc5 5:b27006b0a953
1 <tool id="microsatellite" name="Microsatellite detection" version="1.0.0"> 1 <tool id="microsatellite" name="STR detection" version="1.0.0">
2 <description>for short read, reference, and mapped data</description> 2 <description>for short read, reference, and mapped data</description>
3 <command interpreter="python2.7"> microsatellite.py 3 <command interpreter="python2.7"> microsatellite.py
4 "${filePath}" 4 "${filePath}"
5 #if $inputFileSource.inputFileType == "fasta" 5 #if $inputFileSource.inputFileType == "fasta"
6 --fasta 6 --fasta
106 106
107 .. class:: infomark 107 .. class:: infomark
108 108
109 **What it does** 109 **What it does**
110 110
111 We use different algorithms to detect microsatellites depend on hamming distance parameter. 111 This tool identifies simple as well interrupted STRs. Choosing a hamming distance of zero will return simple STRs.
112 If hamming distance is set to zero, the program will only concern about uninterrupted microsatellites. The process works as follows. 112 Choosing a hamming distance of greater than zero will return both simple and interrupted STRs.
113 113 The algorithms used to identify simple and interrupted STRs are described oin the manuscript cited below (see TABLE XXXX).
114 1) Scanning reads using sliding windows. For a given repeat period ‘k’ (e.g. k=2 for dinucleotide TRs), we compared consecutive k-mer window size sequences, with a step size of k. If a base at a given position matches one k positions earlier it was marked with a plus, if corresponding sites had different bases it was marked with a minus. The first k position is blank.
115
116 2) Since we do not allow mutations in reported TR, consecutive “+” signal sequence means that a k-mer TR is present in this sample.
117
118 3) Report k-mer TRs if the length is larger than a threshold provided by the user.
119
120 If hamming distance is set to integer more than zero, the program will concern both uninterrupted and interrupted microsatellites. The process works as follows:
121
122 (1) Identify intervals that are highly correlated with the interval shifted by ‘k’ (the repeat period). These intervals are called "runs" or "candidates". The allowed level of correlation is 6/7. Depending on whether we want to look for more than one microsat, we either find the longest such run (simple algorithm) or many runs (more complicated algorithm). The following steps are then performed on each run.
123
124 (2) Find the most likely repeat motif in the run. This is done by counting all kmers (of length P) and choosing the most frequent. If that kmer is itself covered by a sub-repeat we discard this run. The idea is that we can ignore a 6-mer like ACGACG because we will find it when we are looking for 3-mers.
125
126 (3) Once we identify the most likely repeat motif, we then modify the interval, adjusting start and end to find the interval that has the fewest mismatches vs. a sequence of the motif repeated (hamming distance).
127
128 (4) At this point we have a valid microsat interval (in the eyes of the program). It is subjected to some filtering stages (hamming distance or too close to an end), and if it satisfies those conditions, it's reported to the user
129
130 For more option, the script to run this program can be downloaded and run with python independently from Galaxy. There are more option for the script mode. Help page is build-in inside the script.
131 114
132 **Citation** 115 **Citation**
133 116
134 When you use this tool, please cite **Arkarachai Fungtammasan and Guruprasad Ananda (2014).** 117 When you use this tool, please cite **Fungtammasan A, Ananda G, Hile SE, Su MS, Sun C, Harris R, Medvedev P, Eckert K, Makova KD. 2015. Accurate Typing of Short Tandem Repeats from Genome-wide Sequencing Data and its Applications, Genome Research**
135 This tool is developed by Chen Sun (cxs1031@cse.psu.edu) and Bob Harris (rsharris@bx.psu.edu) 118 This tool is developed by Chen Sun (cxs1031@cse.psu.edu) and Bob Harris (rsharris@bx.psu.edu)
136 119
137 **Input** 120 **Input**
138 121
139 - The input files can be fastq, fasta, fastq without quality score, and SAM format. 122 - The input files can be fastq, fasta, fastq without quality score, and SAM format.
140 123
141 **Output** 124 **Output**
142 125
143 For fastq, the output will contain the following columns: 126 For fastq, the output will contain the following columns:
144 127
145 - Column 1 = length of microsatellites (bp) 128 - Column 1 = length of STR (bp)
146 - Column 2 = length of left flanking regions (bp) 129 - Column 2 = length of left flanking region (bp)
147 - Column 3 = length of right flanking regions (bp) 130 - Column 3 = length of right flanking region (bp)
148 - Column 4 = repeat motif (bp) 131 - Column 4 = repeat motif (bp)
149 - Column 5 = hamming distance 132 - Column 5 = hamming distance
150 - Column 6 = read name 133 - Column 6 = read name
151 - Column 7 = read sequence with soft masking of microsatellites 134 - Column 7 = read sequence with soft masking of STR
152 - Column 8 = read quality (the same Phred score scale as input) 135 - Column 8 = read quality (the same Phred score scale as input)
153 136
154 For fasta, fastq without quality score and sam format, column 8 will be replaced with dot(.). 137 For fasta, fastq without quality score and sam format, column 8 will be replaced with dot(.).
155 138
156 If the users have mapped file (SAM) and would like to profile microsatellites from premapped data instead of using flank-based mapping approach, they can select SAM format input and specify that they want correspond microsatellites in reference for comparison. The output will be as follow: 139 If the users have mapped file (SAM) and would like to profile STRs from premapped data instead of using flank-based mapping approach, they can select SAM format input and specify that they want correspond STRs in reference for comparison. The output will be as follow:
157 140
158 - Column 1 = length of microsatellites (bp) 141 - Column 1 = length of STR (bp)
159 - Column 2 = length of left flanking regions (bp) 142 - Column 2 = length of left flanking region (bp)
160 - Column 3 = length of right flanking regions (bp) 143 - Column 3 = length of right flanking region (bp)
161 - Column 4 = repeat motif (bp) 144 - Column 4 = repeat motif (bp)
162 - Column 5 = hamming distance 145 - Column 5 = hamming distance
163 - Column 6 = read name 146 - Column 6 = read name
164 - Column 7 = read sequence with soft masking of microsatellites 147 - Column 7 = read sequence with soft masking of STR
165 - Column 8 = read quality (the same Phred score scale as input) 148 - Column 8 = read quality (the same Phred score scale as input)
166 - Column 9 = read name (The same as column 6) 149 - Column 9 = read name (The same as column 6)
167 - Column 10 = chromosome 150 - Column 10 = chromosome
168 - Column 11 = left flanking region start 151 - Column 11 = left flanking region start
169 - Column 12 = left flanking region stop 152 - Column 12 = left flanking region stop
170 - Column 13 = microsatellite start as infer from pair-end 153 - Column 13 = STR start as infer from pair-end
171 - Column 14 = microsatellite stop as infer from pair-end 154 - Column 14 = STR stop as infer from pair-end
172 - Column 15 = right flanking region start 155 - Column 15 = right flanking region start
173 - Column 16 = right flanking region stop 156 - Column 16 = right flanking region stop
174 - Column 17 = microsatellite length in reference 157 - Column 17 = STR length in reference
175 - Column 18 = microsatellite sequence in reference 158 - Column 18 = STR sequence in reference
176 159
177 </help> 160 </help>
178 </tool> 161 </tool>