Difference between revisions of "Evaluating a Read Mapper on Simulated Data"
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Revision as of 15:52, 11 February 2010
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.
Available Test Datasets
- Location: wonderland:~zhanxw/BigSimulation
- Scenarios:
no polymorphism ; 1, 2, 3 SNP ; Deletion 5, 30, 200; Insertion 5, 30
- Quality String
Picked the 75 percentile of Sanger Iluumina 108 mer test data set BCCCCBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBAAAAAAAAAA@@@@@@@@@@@@@@@???????????>>>>>>>>>>>>=========<<<<<<<<<<;;";
- Format
- Both base space and color space
- Both single end and paired end, and paired end reads are given insert size 1500.
- Forward strand and reverse strand are randomly assign with probability 1/2
- Tag
@2:12345:F:SE:Exact @2:12345:F:SE:SNP:2,12345,A,G;2,12346,T,C @2:12345:F:PE+offset:SNP:2,12345,A,G (ref is A, read is G) @2:12345:F:PE+offset:Indel:25M30D5M
- File Naming
BS_SE_EXACT_1M_50 BS_SE_SNP1_1M_50 CS_SE_INDEL1_1M CS_SE_INDEL30_1M CS_SE_INDEL200_1M CS_SE_DEL1_1M
For PE, appending "_1" and "_2", e.g.: PE_EXACT_1M_1 PE_EXACT_1M_2
- Program (generator)
Usage:
generator [bs|cs] [se|pe] [exact|snpXX|indelXX|delXX] -n numbers -l readLength -i insertSize exact: Accurate sample from reference genome snpXX: Bring total XXX SNP for a single read or a pair of reads indelXX: Insert a random XX-length piece for a single read, or at the same position for a paired reads delXX: Delete a random XX-length piece for a single read, or at the same position for a paired reads e.g. ./generator bs se exact -n 100 -l 35
- Output
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.
- Example
For illumina (from Sanger, 108mer hap1 test file): Example:
_1 file: @20:14812275:F:217;None;None/1 AGTTGTTTACTTTCCTTTCCTACCTGGCTGCATCTGTCACATGCATATAGTGTCCCCTGACATGAAGCTCTGATATTGATCTGGAGCCCTATTGGTCTGCAAGTGACT + %27::2:::<70<<::95<<6/8<.)3;::9-,3:6/67731/.+)66;;53'31;9<815.%%%+%4-%%%90-)./26<831))(.%%%%%%%)%0%2%%%%%+%% @15:59364621:R:-118;None;None/1 TGTTCAACCCACTATTAAGCCAGTATTAAATTGTTAATATCAGTTATTATACTTTTATTTCTAAAATTTCTATTTGATCCCTTTTTTTATAAACTCCAATGCATTCTC + %%2=;28>>>>=><>>>>>=>>=>>>;>=>9<1%+,//0+)<<91<4=;;<.%)2::8;;/9<;;;;8647<<;8;;066:<:4628;;;;5:9<<0/25752:3482 _2 file: @20:14812275:F:217;None;None/2 CACTGGAGGGAATCCAATCCCAAATTAATATAACAAAACCAGAAGCTTGCTTAAAAAATATTTTATCAGATTCCAAAGTTGAGCTTGTGTTAGGGTGTACTGGAACTC + %%0;+250::-863486::599<9679/2%%))%+80%--7<;9/1%33,-%%)28/),3,67-8;56<1%)0/%%8;<;59/%%,())%%1%%+%).%099'4;+%- @15:59364621:R:-118;None;None/2 AGAAATAAGACCACATGACAATGTTAAAAATAAAACAGGCAATAGCAATAGTCCCAGAGGTGGTTACAATATGATTTCATGCTCCAGAAAGTATAGGAGAAGACAAAG + %3===;==;7<<;7<5;==<<4<;9=8==<====:<<<<<;<==:=<58;===;:8'8:<===:.9:38908:=;;7;57)%.+%)967%%-%%'6:-%)7);<;0+%
Conclusion: If the first read is forward, then itself is the same as reference sequence and the second read is reverse complement to the reference sequence. If the first read is backward, then itself is reverse complement to the reference genome and the second read is the same as the reference sequence. The first strand always position can always obtain from tag, first two fields (seperated by colon). The second strand position is first strand position plus the offset.
For SOLiD (from Sanger, 50 mer hap1 test file) e.g.
_1 file: >2:67043752:F:1445;2,67043761,A,G;None T12221203021201200302123102221322000012301300211213 22212031230012003021211022213220000123013022112123 (ref) >4:125830377:R:-1541;None;None T30002222300330113020203010322111010300030003230320 _2 file: >2:67043752:F:1445;2,67043761,A,G;None G13031223023023012201210020003310110111111203310211 30312230230230122012100200033121201111113033112112 (ref) >4:125830377:R:-1541;None;None G13311131230200010201210032223330120312000301230032
Conclusion: The first strand and second strand have the same direction (both either same as the reference genome, or reverse complement to reference genome), where their positions are the same as Illumina reads.
Bulk statistics result
Running time (all submitted to the MOSIX client nodes)
Calculated by "./parseRunbatch.py batch2.log |cutrange 0,-1|charrange :-1".
Log file is from runbatch.pl and negative time means unfinished (at the moment of editing).
TODO: Add file size comparison; add link to memory page summarized by Dharknes.
BWA(second) Karma(second) Scenarios 2594 7182 BS_SE_DEL200_1000000_50.fastq 2641 -1 BS_SE_DEL30_1000000_50.fastq 2355 -1 BS_SE_DEL5_1000000_50.fastq 441 7941 BS_SE_EXACT_1000000_50.fastq 809 282 BS_SE_INDEL30_1000000_50.fastq 2217 -1 BS_SE_INDEL5_1000000_50.fastq 645 7206 BS_SE_SNP1_1000000_50.fastq 1102 -1 BS_SE_SNP2_1000000_50.fastq 1142 -1 BS_SE_SNP3_1000000_50.fastq 6536 8874 BS_PE_DEL200_1000000_50_?.fastq 6699 9017 BS_PE_DEL30_1000000_50_?.fastq 6468 9033 BS_PE_DEL5_1000000_50_?.fastq 1743 10112 BS_PE_EXACT_1000000_50_?.fastq 2305 231 BS_PE_INDEL30_1000000_50_?.fastq 5703 2989 BS_PE_INDEL5_1000000_50_?.fastq 1974 3718 BS_PE_SNP1_1000000_50_?.fastq 2396 3339 BS_PE_SNP2_1000000_50_?.fastq 2817 3131 BS_PE_SNP3_1000000_50_?.fastq 4362 16074 CS_PE_DEL200_1000000_50_?.fastq 4385 -1 CS_PE_DEL30_1000000_50_?.fastq 4373 9287 CS_PE_DEL5_1000000_50_?.fastq 773 -1 CS_PE_EXACT_1000000_50_?.fastq 1735 3142 CS_PE_INDEL30_1000000_50_?.fastq 4023 8591 CS_PE_INDEL5_1000000_50_?.fastq 1034 10528 CS_PE_SNP1_1000000_50_?.fastq 2236 -1 CS_PE_SNP2_1000000_50_?.fastq 3810 6617 CS_PE_SNP3_1000000_50_?.fastq 7129 1493 CS_SE_DEL200_1000000_50.fastq 7115 1513 CS_SE_DEL30_1000000_50.fastq 7065 1542 CS_SE_DEL5_1000000_50.fastq 1544 1666 CS_SE_EXACT_1000000_50.fastq 2954 289 CS_SE_INDEL30_1000000_50.fastq 6547 1390 CS_SE_INDEL5_1000000_50.fastq 1690 1661 CS_SE_SNP1_1000000_50.fastq 2853 1449 CS_SE_SNP2_1000000_50.fastq 4039 1237 CS_SE_SNP3_1000000_50.fastq