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Revision as of 23:48, 20 April 2010
, 23:48, 20 April 2010→Read Mapping
: Calculate the input number of reads and number of bases for each sequenced sample
: Calculate the input number of reads and number of bases for each sequenced sample
== Read Mapping ==
; Map Reads with Appropriate Read Mapper
; Map Reads with Appropriate Read Mapper
: We should also tally the proportion of reads that map
: We should also tally the proportion of reads that map
:* Inside the target regions
:* Inside the target regions
:* Near the target regions
:* Near the target regions (defined as within 200bp of each target)
:* Elsewhere in the genome
:* Elsewhere in the genome (defined as regions that are >200bp from each target)
; Recalibrate Base Quality Scores
; Recalibrate Base Quality Scores
: Generate new curves with base quality scores per position.
: Generate new curves with base quality scores per position.
: Calculate the number of mapped bases that reach at least Q20. Potentially, calculate Q20 ''equivalent'' bases by summing the quality scores for bases with base quality >Q20 and dividing the total by 20.
: Calculate the number of mapped bases that reach at least Q20. Potentially, calculate Q20 ''equivalent'' bases by summing the quality scores for bases with base quality >Q20 and dividing the total by 20.
== Verify Sample Identities ==
; Verify that Each Sequenced Sample Matches Prior Information
: We should verify that each sequenced sample matches previous genotypes for that sample. Ideally, this should be done using a likelihood based approach that can also identify potentially contaminated samples.
; Adjudicate Mislabeled Samples
: If any samples that don't match prior genotype data are encountered, the read data for these samples should be compared to all available samples to identify potential sample mixups.
; Identify Potentially Related Samples
: Perhaps this step should be done *after* variant calling?
== Generate Variant Calls ==
; Generate Initial Set of Variant Calls
: Variant calls should be generated taking into account all available samples simultaneously. We should consider variants calls that fall in target regions but also those that fall near the target. An open question is whether off target variant calls will be trustworthy.
; Generate Linkage Disequilibrium Aware Set of Variant Calls
: A typical exome call set might include many sites where no variant is called due to low coverage. If we can integrate samples with previous [[GWAS]] data, we should be able to generate an update and much improved set of variant calls for each individual.
; Annotate Functional Impact of Each Variant
: Called variants should be annotated according to their potential function. At a minimum, we should distinguish synonymous, non-synonymous, conserved splice site, 5'UTR, 3'UTR and other variants. Ideally, we should also assess [[SIFT]] and [[PolyPhen]] scores for conserved variants.
; Calculate Overall Frequency Spectrum
: Calculate observed frequency spectrum and compare to neutral expectations (which are that the number of variants should be roughly proportional to ''1/n'', where ''n'' is the number of minor alleles).
; Annotate Overall Variant Characteristics
: Calculate overall ratio of transitions to transversions, separately for coding and non-coding variants. Within coding variants, analyse synonymous and non-synonymous variants separately.
; Tabulate for Each Sample
:* The number of synonymous and non-synonymous variants
:* The number of unique and shared variants
:* The number of transitions and transversions
== Special Considerations for Admixed Samples ==
; Estimate Local Ancestry Using GWAS Data
: For studies that include admixed samples, we should estimate local ancestry using GWAS data. If GWAS are not available, it is strongly recommended that these data should be generated. In principle, local ancestry estimates can be generated even before exome sequencing is complete.
== Initial Association Analyses ==
We anticipate that, at least early on, the initial association analysis of whole genome datasets in the context of complex trait association studies will focus on identifying and resolving quality control issues that might result in unexpected artifacts.
