We are interested in exploring the contribution of pseudogenes and reads from pseudogenes in the challenge of mapping reads and how different aligners perform. We’re testing the performance of three different aligners on this specific problem: TopHat/Bowtie2, STAR and Subjunc.
The challenge of pseudogenes and pseudogene reads is how each algorithm decides on a location, when faced with an ambiguous situation. Obviously how each algorithm performs will be a function of several parameters.
As each algorithm approaches ambiguous mapping situations differently, I was hoping to crowd-source some ideas as to which parameters in each algorithm might make the most differences.
For example, there is an option in STAR that allows you to more heavily weight the transcriptome in mapping decisions:
sjdbScore 2
int: extra alignment score for alignmets that cross database junctions
In addition to identifying parameters and how they might affect mapping in this respect, any general ideas surrounding this problem are welcomed.
Thanks in advance!
The challenge of pseudogenes and pseudogene reads is how each algorithm decides on a location, when faced with an ambiguous situation. Obviously how each algorithm performs will be a function of several parameters.
As each algorithm approaches ambiguous mapping situations differently, I was hoping to crowd-source some ideas as to which parameters in each algorithm might make the most differences.
For example, there is an option in STAR that allows you to more heavily weight the transcriptome in mapping decisions:
sjdbScore 2
int: extra alignment score for alignmets that cross database junctions
In addition to identifying parameters and how they might affect mapping in this respect, any general ideas surrounding this problem are welcomed.
Thanks in advance!
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