Comments (2)
Furthermore, theses approaches might be used to reduce the candidate set, ie. filter those reads that likely do not belong to this locus. Additional information such as the pairwise alignment of the candidates to each other or the actual sequence should be used.
from djunctor.
One point of critique of GMcloser
is that it has been trained on a set of rice data only – even though they do not use machine learning they need to estimate some parameters. Machine learning approaches require a training set and that introduces a number of problems. Some of them conflicts with our goal "keep it simple". Consequently, we discard this idea.
from djunctor.
Related Issues (20)
- Choose error tolerance for self-alignment such that all relevant repeats will be detected HOT 1
- Alignment coverage for detection of "bad" loci HOT 2
- Option to output the set of filtered reads HOT 1
- Select only proper reads as candidates
- Weakly anchored alignment chains should not be used for pile ups HOT 1
- `getComplementaryOrder` should not combine all local alignments into one
- Favor direct sequential output
- Move loop structure out of the main algorithm
- Make main algorithm one-pass-only HOT 1
- Initialize repeat mask with user input
- Is the self-alignment essential?
- Filtering for `isValid` discard good reads
- Require a minimum coverage in pile ups before consensus
- Take complementary alignment into account when assembling contigs HOT 1
- Add option to enable extensions
- Users must provide initial alignment
- User may select a block of the reference DB HOT 1
- Testing: insert real Illumina gaps into PacBio assembly HOT 1
- Concise logging rules
- Reduce number of consensus computations
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from djunctor.