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Algorithms for the description of molecular sequences
the outcome of clinical diagnostic DNA tests. The standard
nomenclature of the Human Genome Variation Society (HGVS) describes
the observed variant sequence relative to a given reference sequence.
We propose an efficient algorithm for the extraction of
HGVS descriptions from two DNA sequences.
Our algorithm is able to compute the HGVS~descriptions of complete
chromosomes or other large DNA strings in a reasonable amount of
computation time and its resulting descriptions are relatively small.
Additional applications include updating of gene variant database
contents and reference sequence liftovers.
Next, we adapted our method for the extraction of descriptions for protein sequences in particular for describing frame shifted...Show more
Unambiguous sequence variant descriptions are important in reporting
the outcome of clinical diagnostic DNA tests. The standard
nomenclature of the Human Genome Variation Society (HGVS) describes
the observed variant sequence relative to a given reference sequence.
We propose an efficient algorithm for the extraction of
HGVS descriptions from two DNA sequences.
Our algorithm is able to compute the HGVS~descriptions of complete
chromosomes or other large DNA strings in a reasonable amount of
computation time and its resulting descriptions are relatively small.
Additional applications include updating of gene variant database
contents and reference sequence liftovers.
Next, we adapted our method for the extraction of descriptions for protein sequences in particular for describing frame shifted variants. We propose an addition to the HGVS nomenclature for accommodating the (complex)
frame shifted variants that can be described with our method.
Finally, we applied our method to generate descriptions for Short Tandem Repeats (STRs), a form of self-similarity. We propose an alternative repeat variant that can be added to the existing
HGVS nomenclature.
The final chapter takes an explorative approach to classification in large cohort studies. We provide a ``cross-sectional'' investigation on this data to see the relative power of the different groups.
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- All authors
- Vis, J.K.
- Supervisor
- Kok, J.N.; Slagboom, P.E.
- Co-supervisor
- Laros, J.F.J.
- Committee
- Arbab, F.; Herik, H.J. van den; Olabarriaga, S.D.; Plaat, A.; Taschner, P.E.M.
- Qualification
- Doctor (dr.)
- Awarding Institution
- Leiden Institute of Advanced Computer Science (LIACS) , Science , Leiden University
- Date
- 2016-12-21
- ISBN
- 9789090300948