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1 This document has the information on how to run CREST for structural
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2 variation detection.
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3
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4 =============
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5 Requirements:
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6 =============
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7 Before running CREST, you need to make sure that several pieces of software
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8 and/or modules are installed on the system:
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9 1. BLAT software suite, especially blat, gfClient, and gfServer. BLAT
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10 can be obtained from these links:
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11 BLAT for academic use: http://www.soe.ucsc.edu/~kent
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12 BLAT commercial license: http://www.kentinformatics.com/
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13 2. CAP3 assembly program, available here:
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14 CAP3 for academic use: http://seq.cs.iastate.edu/cap3.html
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15 CAP3 commercial license: Contact Robin Kolehmainen at Michigan Tech,
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16 rakolehm@mtu.edu or (906)487-2228.
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17 3: SAMtools library for accessing SAM/BAM files, available from SourceForge:
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18 SAMtools: http://sourceforge.net/projects/samtools/files/
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19 4. BioPerl and Bio::DB::Sam modules. They are usually available as
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20 packages on most Linux distributions, but are also available at this link:
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21 BioPerl: http://www.bioperl.org/
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22 Bio::DB::Sam: http://search.cpan.org/~lds/Bio-SamTools/lib/Bio/DB/Sam.pm
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23 Important: you must install SAMtools library before install Bio::DB::Sam.
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24 5. ptrfinder is needed if you want to remove short tandem repeat mediated
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25 SVs, the executable is included in the download package, put it on the path.
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26
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27 Note:
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28 1. You can use your own programs in place of BLAT and CAP3, but you need to
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29 implement the run method in SVExtTools.pm.
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30 2. The pipeline uses gfServer to mimic a standard blat server, so you need
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31 to setup your own gfServer. Details on setting up the server can be found in
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32 the BLAT package. Using a query server can significantly increase the speed of
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33 the pipeline.
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34
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35 Your BAM files must contain soft-clipping signatures at the breakpoints. If
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36 they do not, you will not get any results. For more information see the
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37 section "About Soft-Clipping" at the end of this document.
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38
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39 =====================
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40 Running the pipeline:
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41 =====================
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42
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43 Make sure that all the required perl modules are in @INC. One simple way
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44 is to put all .pm and .pl scripts in the same directory and run them from
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45 this same directory. Also, the input bam file, must be sorted and indexed
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46 before running the pipeline.
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47
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48 We are going to use two sample bam files (tumor.bam and germline.bam) to
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49 illustate how to run the pipeline. The examples assume you want to find SV
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50 in tumor.bam and you also have the matched germline sample bam file.
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51
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52 Important: indexing all bam files before running the pipeline is required.
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53
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54 1. Get soft-clipping positions.
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55
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56 The program extractSClip.pl will extract all soft-clipping positions first,
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57 and identify those positions with a cluster of soft-clipped reads. The
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58 program requires only the BAM file and the reference genome's FASTA file
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59 The following is an example to extract all positions:
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60
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61 extractSClip.pl -i tumor.bam --ref_genome hg18.fa
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62
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63 Two files named tumor.bam.cover and tumor.bam.sclip.txt will be generated
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64 for use in the next step.
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65
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66 Note: The program can use either paired or single-end sequencing data. For
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67 single-end data, use the --nopaired parameter.
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68
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69 For whole genome sequencing project, we highly suggest running the procedure
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70 in parallel by dividing the genome into pieces. One natural way is by
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71 chromosome. The following is an example to extract all positons on chr4.
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72
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73 extractSClip.pl -i tumor.bam --ref_genome hg18.fa -r 4
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74
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75 Important: The genome file used in this pipeline must be the same as the one
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76 used to map reads, so the chromosome names need to agree. In this example,
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77 the genome file and bam file all have the chromosome name as 4 instead of
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78 chr4 you may encounter.
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79
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80 Two files named tumor.bam.4.cover and tumor.bam.4.sclip.txt will be generated
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81 for use in the next step. So it's very easy to run this step in parallel and
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82 combine the results together to form a final result.
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83
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84 The output files for this step have names with suffixes of *.cover and
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85 *.sclip.txt. The .cover file is a tab-delimited text file, with columns:
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86 chr, position, strand, number of soft-clipped reads, and coverage at that
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87 position. The strand is just left-clipped or right-clipped to help identify
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88 the SV orientation. The .sclip.txt file has the detailed information for
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89 all soft-clipped reads including sequence and quality values. This file is
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90 also tab-delimited with the following columns: chr, posiiton, strand, read
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91 name, sequence, and quality.
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92
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93 Example of part of a *.cover file:
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94 4 125892327 + 1 28
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95 4 125892458 + 1 27
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96 4 125893225 + 1 28
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97 4 125893227 + 5 29
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98 4 125893365 - 1 26
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99 4 125893979 - 1 16
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100 10 66301086 - 1 33
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101 10 66301858 + 4 14
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102 10 66301865 - 8 21
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103 10 66301871 - 1 22
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104 10 66302136 + 1 51
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105
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106 Example of part of a *.sclip.txt file:
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107 4 125892327 + HWUSI-EAS1591_6113C:3:17:12332:19420#0 CC CC
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108 4 125892458 + HWUSI-EAS1591_6113C:4:91:6281:9961#0 GACTAACCACCACGGTACATGTTTTCCTATGTAAAAAACCTGCACATTCTACACATGTATCCCAGAACTTAAAGTAAAACAC B@C@?:CC>CCBCCCCACBCDCCCCCC;<:<9CCCCC@CCCCCBCCCCCCCCCCCCCCCACCCCCCCCCCCCCCCCCCCCAC
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109 4 125893225 + HWI-EAS90_614M9:5:18:17924:10181#0 CCCTCCTGGGTTCAAGTGATTCTCCTGCCTCTACCTCCCGAGTAGCTGGGATTACAGGTGCCCACCACCATGCCTGGCTAA #######@@7@:8@><16+6(B>AABCAA3AB@CC6CCCCCCCDCCCCCCBCCDCCCCCCCCCCCCDCCCCCCCCCCCCCC
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110
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111 If you run this step in parallel, you need to combine the outputs by
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112 concatenating the files. Tumor and germline files must be concatenated
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113 separately, for example:
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114 cat tumor.bam.*.cover > tumor.bam.cover
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115 cat germline.bam.*.cover > germline.bam.cover
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116
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117 2.Remove germline events (optional)
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118
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119 Running step 1 on both germline and tumor samples, you will get the
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120 soft-clipping posiitons in both samples. This step will remove any position
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121 in the tumor sample that also appears in germline sample, so germline events
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122 will be removed. This step does not use any sequence information and could
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123 remove true events. By our observations, true events are rarely removed.
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124 You can skip this step and the program will do germline clean up at later step
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125 (see the -g parameter for CREST.pl).
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126
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127 The script for this step is countDiff.pl and it only requires two parameters
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128 to specity the two output files from previous step.
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129
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130 countDiff.pl -d tumor.bam.cover -g germline.bam.cover > soft_clip.dist.txt
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131
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132 A file named tumor.bam.cover.somatic.cover will be generated for next step.
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133
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134 The program will generate a file with suffix *.somatic.cover, and it will
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135 be used for the next step. The file has the same format as *.cover generated
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136 in the previous step.
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137
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138 The standard output will show the coverage distribution. For every read count
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139 in the range 1-999, it will show the number of breakpoints supported by that
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140 many soft-clipped reads.
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141
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142 3. Running the SV detection script.
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143
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144 This is the core step in the detection process. The program is CREST.pl.
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145
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146 The program needs quite a few parameters, but you can think about what you
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147 will need. Here is a partial list of required and common parameters:
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148
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149 -f The input soft-clipped coverage file produced in step 1 or 2.
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150 -d The disease or tumor bam file
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151 -g The germline bam file. If you want to identify somatic SVs only, you
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152 should provide this parameter. If you also want to identify germline
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153 events, you can leave this parameter unspecified. When treat your
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154 germline file as disease without specify -g parameter, the program can
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155 be used to identify germline events, or SV polymorphism.
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156 --ref_genome The reference genome in fa format (used by bam file)
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157 -t The reference genome in 2bit format (used by gfClient), this file can
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158 be generated by using faToTwoBit program in BLAT program suit. This
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159 file must be the same as the one you used to setup gfServer.
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160 --blatserver The name or IP address of blat server, you need to use your
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161 own one instead of using the public one at UCSC.
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162 --blatport The port number for the blat server.
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163 --nopaired Tell the program the reads are not paired.
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164
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165 If all of the required programs are on the path then you won't need to
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166 specify them again, otherwise you need to specify the paths to the programs
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167 using the corresponding parameters. Please use CREST.pl --man to show the
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168 man page, which provides a detailed parameter list.
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169
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170 An example of running this step is:
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171 CREST.pl -f tumor.bam.cover -d tumor.bam -g germline.bam --ref_genome \
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172 hg18.fa -t hg18.2bit
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173
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174 There is also a -r parameter to specify the range to be searched and it's highly
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175 recommended to run using -r as below:
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176 CREST.pl -f tumor.bam.cover -d tumor.bam -g germline.bam --ref_genome \
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177 hg18.fa -t /genome/hg18.2bit -r chr1
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178 So it's very easy to run the program in parallel by spliting the genome into
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179 pieces.
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180
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181 The program will generate a *.predSV.txt file. The filename will be the input
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182 bam with .predSV.txt appended unless you specify the -p parameter. Also the
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183 STDERR output has the full list of SVs, including rejected ones. The output
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184 file *.predSV.txt has the following tab-delimited columns: left_chr, left_pos,
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185 left_strand, # of left soft-clipped reads, right_chr, right_pos, right_strand,
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186 # right soft-clipped reads, SV type, coverage at left_pos, coverage at
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187 right_pos, assembled length at left_pos, assembled length at right_pos,
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188 average percent identity at left_pos, percent of non-unique mapping reads at
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189 left_pos, average percent identity at right_pos, percent of non-unique mapping
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190 reads at right_pos, start position of consensus mapping to genome,
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191 starting chromosome of consensus mapping, position of the genomic mapping of
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192 consensus starting position, end position of consensus mapping to genome,
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193 ending chromsome of consnesus mapping, position of genomic mapping of
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194 consensus ending posiiton, and consensus sequences. For inversion(INV), the
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195 last 7 fields will be repeated to reflect the fact two different breakpoints
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196 are needed to identify an INV event.
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197
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198 Example of the tumor.predSV.txt file:
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199 4 125893227 + 5 10 66301858 - 4 CTX 29 14 83 71 0.895173453996983 0.230769230769231 0.735384615384615 0.5 1 4 125893135 176 10 66301773 TTATGAATTTTGAAATATATATCATATTTTGAAATATATATCATATTCTAAATTATGAAAAGAGAATATGATTCTCTTTTCAGTAGCTGTCACCTCCTGGGTTCAAGTGATTCTCCTGCCTCTACCTCCCGAGTAGCTGGGATTACAGGTGCCCACCACCATGCCTGGCTAATTTT
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200 5 7052198 - 0 10 66301865 + 8 CTX 0 22 0 81 0.761379310344828 0.482758620689655 0 0 1 5 7052278 164 10 66301947 AGCCATGGACCTTGTGGTGGGTTCTTAACAATGGTGAGTCCGGAGTTCTTAACGATGGTGAGTCCGTAGTTTGTTCCTTCAGGAGTGAGCCAAGATCATGCCACTGCACTCTAGCCTGGGCAACAGAGGAAGACTCCACCTCAAAAAAAAAAAGTGGGAAGAGG
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201 10 66301858 + 4 4 125893225 - 1 CTX 15 28 71 81 0.735384615384615 0.5 0.889507154213037 0.243243243243243 1 10 66301777 153 4 125893154 TTAGCCAGGCATGGTGGTGGGCACCTGTAATCCCAGCTACTCGGGAGGTAGAGGCAGGAGAATCACTTGAACCCAGGAGGTGACAGCTACTGAAAAGAGAATCATATTCTCTTTTCATAATTTAGAATATGATATATATTTCAAAATATGATA
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202
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203 If there are no or very few results, there may be a lack of soft-clipping. See
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204 the section "About Soft-Clipping" at the end of this document.
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205
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206 4. Visulization of the detailed alignment at breakpoint (optional)
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207
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208 The bam2html.pl script builds an html view of the multiple alignment for
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209 the breakpoint, so you can manually check the soft-clipping and other things.
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210
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211 bam2html.pl -d diag.bam -g germline.bam -i diag.bam.predSV.txt \
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212 --ref_genome /genome/hg18 -o diag.bam.predSV.html
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213
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214 The output file is specified by -o option.
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215
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216 ====================
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217 About Soft-Clipping:
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218 ====================
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219
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220 CREST uses soft-clipping signatures to identify breakpoints. Soft-clipping is
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221 indicated by "S" elements in the CIGAR for SAM/BAM records. Soft-clipping may
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222 not occur, depending on the mapping algorithm and parameters and sometimes even
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223 the library preparation.
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224
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225 With bwa sampe:
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226 ---------------
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227
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228 One mapping method that will soft-clip reads is bwa sampe (BWA for paired-end
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229 reads). When BWA successfully maps one read in a pair but is not able to map
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230 the other, it will attempt a more permissive Smith-Waterman alignment of the
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231 unmapped read in the neighborhood of the mapped mate. If it is only able to
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232 align part of the read, then it will soft-clip the portion on the end that it
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233 could not align. Often this occurs at the breakpoints of structural
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234 variations.
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235
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236 In some cases when the insert sizes approach the read length, BWA will not
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237 perform Smith-Waterman alignment. Reads from inserts smaller than the read
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238 length will contain primer and/or adapter and will often not map. When the
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239 insert size is close to the read length, this creates a skewed distribution
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240 of inferred insert sizes which may cause BWA to not attempt Smith-Waterman
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241 realignment. This is indicated by the error message "weird pairing". Often
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242 in these cases there are also unusually low mapping rates.
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243
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244 One way to fix this problem is to remap unmapped reads bwasw. To do this,
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245 extract the unmapped reads as FASTQ files (this may be done with a combination
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246 of samtools view -f 4 and Picard's SamToFastq). Realign using bwa bwasw and
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247 build a BAM file. Then, re-run CREST on this new BAM file, and you may pick
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248 up events that would have been missed otherwise.
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249
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250 With other aligners:
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251 --------------------
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252
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253 Consult the documentation or mailing list(s) for your mapper to determine its
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254 behavior with regard to soft-clipping.