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Published on Apr 16, 2014
Most NGS analysis is founded on a very simple and powerful principle: look only at the differences of your data to a reference genome of your species. Alignment algorithms are the workhorse of this approach and accounts for the vast majority of the compute time necessary in a secondary analysis workflow. In this webcast, Gabe Rudy covers the history of alignment algorithms of short read, high-throughput sequencing data and the set of tools that represent the state of the art.
What you can expect to learn:
- How all alignment algorithms are a trade-off of speed versus accuracy, and what those trade-offs can mean with your data. - How the human reference sequence causes alignment artifacts, and how you can spot them. - How BWA, BWA-MEM and BWA-SW differ. - How local re-alignment works to improve variant calling, and when you will see it and won't see it in action in your data. - How to read a CIGAR string and other per-alignment data to investigate alignments at a particular locus.
We will use the newly launched GenomeBrowse 2.0 visualization engine to review examples of different alignment artifacts, false-positive variant calls, and other alignment and variant meta-data.