Difference between revisions of "Evaluating a Read Mapper on Simulated Data"

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== Mapping Qualities  ==
 
== 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.  
+
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:
 
<pre>
 
_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+%
 
</pre>
 
 
 
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.
 
 
 
<pre>
 
_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
 
 
 
</pre>
 
 
 
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.
 
 
 
<br>
 
 
 
= Bulk statistics result  =
 
Running time (all submitted to the MOSIX client nodes)
 
<br>
 
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.
 
<pre>
 
BWA(second) Karma(second) Scenarios
 
7561 4638 BS_PE_DEL200_1000000_50_?.fastq
 
7548 4677 BS_PE_DEL30_1000000_50_?.fastq
 
7225 4730 BS_PE_DEL5_1000000_50_?.fastq
 
975 6531 BS_PE_EXACT_1000000_50_?.fastq
 
1726 793 BS_PE_INDEL30_1000000_50_?.fastq
 
6199 4140 BS_PE_INDEL5_1000000_50_?.fastq
 
1193 4949 BS_PE_SNP1_1000000_50_?.fastq
 
1646 4513 BS_PE_SNP2_1000000_50_?.fastq
 
2064 4089 BS_PE_SNP3_1000000_50_?.fastq
 
2594 3707 BS_SE_DEL200_1000000_50.fastq
 
2641 3942 BS_SE_DEL30_1000000_50.fastq
 
2355 4263 BS_SE_DEL5_1000000_50.fastq
 
441 4228 BS_SE_EXACT_1000000_50.fastq
 
809 764 BS_SE_INDEL30_1000000_50.fastq
 
2217 3932 BS_SE_INDEL5_1000000_50.fastq
 
645 3808 BS_SE_SNP1_1000000_50.fastq
 
1102 3473 BS_SE_SNP2_1000000_50.fastq
 
1142 3267 BS_SE_SNP3_1000000_50.fastq
 
6193 6909 CS_PE_DEL200_1000000_50_?.fastq
 
6173 6636 CS_PE_DEL30_1000000_50_?.fastq
 
6096 6702 CS_PE_DEL5_1000000_50_?.fastq
 
858 8496 CS_PE_EXACT_1000000_50_?.fastq
 
1743 948 CS_PE_INDEL30_1000000_50_?.fastq
 
5517 5412 CS_PE_INDEL5_1000000_50_?.fastq
 
1253 8454 CS_PE_SNP1_1000000_50_?.fastq
 
2113 7420 CS_PE_SNP2_1000000_50_?.fastq
 
2622 6076 CS_PE_SNP3_1000000_50_?.fastq
 
3878 1493 CS_SE_DEL200_1000000_50.fastq
 
3859 1513 CS_SE_DEL30_1000000_50.fastq
 
3775 1542 CS_SE_DEL5_1000000_50.fastq
 
621 1666 CS_SE_EXACT_1000000_50.fastq
 
1392 289 CS_SE_INDEL30_1000000_50.fastq
 
3525 1390 CS_SE_INDEL5_1000000_50.fastq
 
874 1661 CS_SE_SNP1_1000000_50.fastq
 
1965 1449 CS_SE_SNP2_1000000_50.fastq
 
3314 1237 CS_SE_SNP3_1000000_50.fastq
 
</pre>
 

Latest revision as of 22:19, 8 September 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.