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Q: Where can I find a copy of the blacklist used in MutSig runs?

A: The blacklist is used to filter out recurrent mutation sites that the MutSig development team found to cause issues with the determination of significance. Because these by nature include germline mutations that may not have been part of available databases at the original generation of the MAF, we are not permitted to release it to the public.

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Q: How do I analyze samples that aren't included in your Firehose runs (e.g. Blood Normals, Solid Tissue Normals, etc.)?

A: All analysis-ready patient samples are available in our stddata archives; this will include matched normals, where available (but note that the so-called TCGA control samples are not included). Normals can be identified by inspection of the barcode schema below, in conjunction with the TCGA code tables. You can obtain the stddata archives using our firehose_get utility or by traversing the FireBrowse user interface or stddata API. Each sample in the archive is identified by a TCGA Barcode that contains the sample type. As shown below, the Sample portion of the barcode can be looked up in the sample type code table available here (as can the tissue source site, aka TSS, et cetera). In addition, FireBrowse makes much of this information available programmatically in its metadata API.

TCGA Barcode Description: As described here, a batch is uniquely determined by the first shipment of a group of analytes (or plate) from the Biospecimen Core Resource. So, in most cases the plate number of a sample is effectively synonymous with the batch id of the sample; an exception to this is when additional analytes from a participant are subsequently shipped the batch id will remain fixed at the first plate number.

Q: How are the copy number data generated, and what do their file names mean?

A: This is discussed in the application note posted here: http://www.broadinstitute.org/cancer/cga/copynumber_pipeline. Note that the 'minus_germline, or 'nocnv' segment files, refer to whether the steps in section 2.3 are applied.  The steps in section 2.4 are applied regardless.

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Q: What is the difference between RPKM and RSEM mRNASeq data?

A: RPKM and RSEM are different methods for estimating expression levels from mRNASeq data. RPKM (Reads Per Kilobase per Million mapped reads) is described in a paper by Mortazavi, Williams, McCue, Schaeffer & Wold titled Mapping and quantifying mammalian transcriptomes by RNA-Seq. RSEM (RNA-Seq by Expectation-Maximization) is considered by many to be a better estimation method and, if available, RSEM data is preferentially used in our downstream analyses. It is described in a paper by Bo Li & Colin Dewey titled RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.

Q: What can you tell me about the RPPA data?

A: RPPA stands for "reverse phase protein array," which are data generated by the M.D. Anderson Cancer Center as described here. The MDACC also hosts the TCPA portal, which serves clean, batch-corrected RPPA data that may be preferred for your analysis over the uncorrected data deposited directly to the TCGA data coordination center.