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=====Read mapping===== *Mapping reads to the captured targets instead of the full genome will increase the speed by ~100x. However there are several considerations and practical issues in using this strategy: **Captured targets are partially overlapping, so if we use the target sequences as the mapping templates, some reads will have more than one identical hits. **Some flanking intronic sequences were also captured because the way the probes were designed. These sequences needs to be included as mapping templates. **The target sequences are a large number of relatively short sequences. Existing read mapping and variant calling software (Maq, SOAP etc) were not designed for such templates. Ideally, we should merge all the mapping templates into a single file. *Here are the procedures I used to prepare a mapping template file: **Combine CCDS_exons_UCSD_HARVARD.BED file with miRna.txt file, giving rise to a complete list of CCDS exons plus all microRNA genes. **Sort the list based on chromosome positions, add 100bp upstream and downstream sequences to each exon, and merge overlapping exons (145,532 in total). **Retrieve the DNA sequence of these regions from the hg18 unmasked sequences using GrabExons. **Merge all the DNA sequences in a single fasta file. Adjacent sequences are separated by Ns such that each sequence start at 1, 501, 1001... etc. The step size is 500. A table containing the sequence IDs and the starting positions was generated, so that we can remap the variant back to the genome coordinate after variant calling.
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