Thank you for sharing the interesting pipeline for detecting NUMTs!
However, I have some questions about the results of searchBreakpoint_fromblatoutputs.py. After applying the filtering criterion (readsCount >= 3) as described in the paper, I noticed that not all NUMT regions identified by searchNumtCluster_fromDiscordantReads.py exhibit high-confidence left and right breakpoints on both nuclear and mitochondrial genomes (Example 1), and some of the breakpoints given did not seem to constitute a reasonable interval (Example 2, nuclear bp). I wonder whether these regions should be considered genuine NUMTs, or if there exists an alternative approach to detect the breakpoints of these regions? Thank you very much!
Thank you for sharing the interesting pipeline for detecting NUMTs!
However, I have some questions about the results of searchBreakpoint_fromblatoutputs.py. After applying the filtering criterion (readsCount >= 3) as described in the paper, I noticed that not all NUMT regions identified by searchNumtCluster_fromDiscordantReads.py exhibit high-confidence left and right breakpoints on both nuclear and mitochondrial genomes (Example 1), and some of the breakpoints given did not seem to constitute a reasonable interval (Example 2, nuclear bp). I wonder whether these regions should be considered genuine NUMTs, or if there exists an alternative approach to detect the breakpoints of these regions? Thank you very much!
Example 1 pointGroup Group chr Tend strand readsCount Tstart nu_Tstart_Bright nu_Tstart_Bright chr21 -1 + 14 20074374 mt_Tstart mtRight chrM -1 - 14 5135
Example 2 pointGroup Group chr Tend strand readsCount Tstart nu_Tend_Bleft nu_Tend_Bleft chr11 50039357 + 8 -1 nu_Tstart_Bright nu_Tstart_Bright chr11 -1 + 10 50039359 mt_Tend mtLeft chrM 16053 - 8 -1 mt_Tstart mtRight chrM -1 - 10 15511