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The sequences were analyzed using an automated mutation detection pipeline developed by the MSKCC Bioinformatics Core.
Thus, it is first imperative to fully define and understand the mutation detection pipeline and quality control steps being used, especially if working with an external partner.
The updated multi-copy gene mutation detection pipeline aligned all Illumina reads to reference sequences with Bowtie2, which can perform local alignments and exclude ends with low quality or similarity [ 54], with the parameters set as: perform local alignments allowing 1 seed alignment mismatch and report all alignments.
After validating the single copy genes, our mutation detection pipeline was improved by setting the mutations at the ratio of non-reference nucleotide number counts of rows to columns at each position (variant multipliers), which ranged from 8to1414 at the minimum non-reference nucleotide percentage of 0.05% and Phred quality scores ranging from 16 to 23.
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A structural variation detection pipeline that takes advantage of three detection strategies was used to detect SVs (see below).
The appropriate level of coverage and the optimal mutation detection algorithm for the project pipeline were determined.
CDH1/E-cadherin mutation detection rates vary between 12% and 83%.
A rapid and reliable mutation detection assay has been developed.
The detection of JAK2 exon 12 mutations is technically much more complicated than V617F mutation detection.
Small scale variation including: SNPs and insertions and deletions, were identified by using the SNP detection pipeline of MUMmer [27].
Mutation detection was carried out as described previously [45].
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