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| == Mapping Qualities == | | == Mapping Qualities == |
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− | We should evaluate mapping qualities by counting how many reads are assigned each mapping quality (or greater) and among those how many map correctly or incorrectly. This gives a Heng Li graph, where one plots number of correctly mapped reads vs. number of mismapped reads. | + | We should evaluate mapping qualities by counting how many reads are assigned each mapping quality (or greater) and among those how many map correctly or incorrectly. This gives a Heng Li graph, where one plots number of correctly mapped reads vs. number of mismapped reads. |
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− | == Available Test Datasets ==
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− | *Location: wonderland:~zhanxw/BigSimulation
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− | *Scenarios:
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− | no polymorphism ; 1, 2, 3 SNP ; Deletion 5, 30, 200; Insertion 5, 30
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− | *Quality String
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− | Picked the 75 percentile of Sanger Iluumina 108 mer test data set
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− | *Format
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− | both base space and color space both single end and paired end, and paired end reads are given insert size 1500.
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− | *Program (generator)
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− | Usage:
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− | generator [bs|cs] [se|pe] [exact|snpXX|indelXX|delXX] -n numbers -l readLength -i insertSize
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− | exact: Accurate sample from reference genome
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− | snpXX: Bring total XXX SNP for a single read or a pair of reads
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− | indelXX: Insert a random XX-length piece for a single read, or at the same position for a paired reads
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− | delXX: Delete a random XX-length piece for a single read, or at the same position for a paired reads
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− | e.g. ./generator bs se exact -n 100 -l 35
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− | *Output
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− | Simulation file are named like: BS_SE_EXACT_1000000_35, meaning base space, single end, exact (no polymorphism), 1M reads, 35 bp per read. For each read, the tag was named in a similar way to Sanger's.
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− | <br>
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− | = Bulk statistics result =
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− | Running time (all submitted to the MOSIX client nodes)
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− | <br>
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− | Calculated by "./parseRunbatch.py batch2.log |cutrange 0,-1|charrange :-1".
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− | Log file is from runbatch.pl and negative time means unfinished (at the moment of editing).
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− | <pre>
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− | BWA(second) Karma(second) Scenarios
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− | 2594 7182 BS_SE_DEL200_1000000_50.fastq
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− | 2641 -1 BS_SE_DEL30_1000000_50.fastq
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− | 2355 -1 BS_SE_DEL5_1000000_50.fastq
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− | 441 7941 BS_SE_EXACT_1000000_50.fastq
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− | 809 282 BS_SE_INDEL30_1000000_50.fastq
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− | 2217 -1 BS_SE_INDEL5_1000000_50.fastq
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− | 645 7206 BS_SE_SNP1_1000000_50.fastq
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− | 1102 -1 BS_SE_SNP2_1000000_50.fastq
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− | 1142 -1 BS_SE_SNP3_1000000_50.fastq
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− | 6536 8874 BS_PE_DEL200_1000000_50_?.fastq
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− | 6699 9017 BS_PE_DEL30_1000000_50_?.fastq
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− | 6468 9033 BS_PE_DEL5_1000000_50_?.fastq
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− | 1743 10112 BS_PE_EXACT_1000000_50_?.fastq
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− | 2305 231 BS_PE_INDEL30_1000000_50_?.fastq
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− | 5703 2989 BS_PE_INDEL5_1000000_50_?.fastq
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− | 1974 3718 BS_PE_SNP1_1000000_50_?.fastq
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− | 2396 3339 BS_PE_SNP2_1000000_50_?.fastq
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− | 2817 3131 BS_PE_SNP3_1000000_50_?.fastq
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− | 4362 16074 CS_PE_DEL200_1000000_50_?.fastq
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− | 4385 -1 CS_PE_DEL30_1000000_50_?.fastq
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− | 4373 9287 CS_PE_DEL5_1000000_50_?.fastq
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− | 773 -1 CS_PE_EXACT_1000000_50_?.fastq
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− | 1735 3142 CS_PE_INDEL30_1000000_50_?.fastq
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− | 4023 8591 CS_PE_INDEL5_1000000_50_?.fastq
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− | 1034 10528 CS_PE_SNP1_1000000_50_?.fastq
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− | 2236 -1 CS_PE_SNP2_1000000_50_?.fastq
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− | 3810 6617 CS_PE_SNP3_1000000_50_?.fastq
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− | 7129 1493 CS_SE_DEL200_1000000_50.fastq
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− | 7115 1513 CS_SE_DEL30_1000000_50.fastq
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− | 7065 1542 CS_SE_DEL5_1000000_50.fastq
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− | 1544 1666 CS_SE_EXACT_1000000_50.fastq
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− | 2954 289 CS_SE_INDEL30_1000000_50.fastq
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− | 6547 1390 CS_SE_INDEL5_1000000_50.fastq
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− | 1690 1661 CS_SE_SNP1_1000000_50.fastq
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− | 2853 1449 CS_SE_SNP2_1000000_50.fastq
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− | 4039 1237 CS_SE_SNP3_1000000_50.fastq
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− | </pre>
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Grouping
When evaluating read mappers, we should always focus on well defined sets of reads:
- Reads with no polymorphisms.
- Reads with 1, 2, 3 or more SNPs.
- Reads with specific types of short indels (<10bp).
- Reads with larger structural variants (>100bp).
SNPs and errors are different because SNPs can lead to mismatches in high-quality bases. In addition to integrating according to the metrics above, we could separate results by the number of errors in each read.
Should also be grouped according to whether reads are paired-end or single-end and according to read-length.
Bulk Statistics
- Speed (millions of reads per hour)
- Memory requirements
- Size of output files
- Raw count of mapped reads
Mapping Accuracy
The key quantities are:
- How many reads were not mapped at all?
- How many reads were mapped incorrectly? This is the least desirable outcome.
- How many reads were mapped correctly?
Correct mapping should be defined as:
- Most stringent: matches simulated location and CIGAR string.
- Less stringent: overlaps simulated location at base-pair level, CIGAR string and end positions may differ.
- Incorrect: Doesn't overlap simulated location.
Mapping Qualities
We should evaluate mapping qualities by counting how many reads are assigned each mapping quality (or greater) and among those how many map correctly or incorrectly. This gives a Heng Li graph, where one plots number of correctly mapped reads vs. number of mismapped reads.