Decide what to do with coverage reporting in presence of large deletions.
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- Dominant language
- Python
- Stars
- 21
- Forks
- 11
- Avg merge
- 12h 5m
- Merged PRs (30d)
- 24
Description
Decide what to do with coverage reporting in presence of large deletions.
Currently, we can have following two cases:
- query aligned as
100M600D100Msomewhere in the reference. Then coverage values for the big deletion in the middle are missing. (reference region is not covered by query) - query aligned as
100M599D100Msomewhere in the reference. Then coverage values for the big deletion in the middle are present (reference region is covered by query).
The threshold of 600 deletions is sort of arbitrary.
We would like to develop a better decision procedure on what to report as "coverage".
Possibly, one that looks into the individual reads (from fastq files) in order to see whether it was the reads that spanned the big deletion, or whether the query is two separate consensus sequences "stitched" together.
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Research direction
No source file or test is named. Start by tracing the coverage-reporting path for alignments containing large deletions, then compare the 100M600D100M and 100M599D100M cases described here. Done requires an agreed decision procedure and coverage tests that distinguish these cases, including whether read-level FASTQ evidence is needed.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- bioinformatics
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100