Evaluating a Read Mapper on Simulated Data

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Revision as of 17:33, 8 February 2010 by Zhanxw (talk | contribs) (Available Test Datasets)
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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.

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

  • Format

both base space and color space both single end and paired end, and paired end reads are given insert size 1500.

  • 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.