| Title |
A short note on dynamic programming in a band
|
|---|---|
| Published in |
BMC Bioinformatics, June 2018
|
| DOI | 10.1186/s12859-018-2228-9 |
| Pubmed ID | |
| Authors |
Jean-François Gibrat |
| Abstract |
Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm. In this note, for the sake of efficiency, we consider dynamic programming in a band. We show how to choose the band width in function of the long reads' error rates, thus obtaining an [Formula: see text] algorithm in space and time. We also propose a procedure to decide whether this algorithm, when applied to semi-global alignments, provides the optimal score. We suggest that dynamic programming in a band is well suited to the problem of aligning long reads between themselves and can be used as a core component of methods for obtaining a consensus sequence from the long reads alone. The function implementing the dynamic programming algorithm in a band is available, as a standalone program, at: https://forgemia.inra.fr/jean-francois.gibrat/BAND_DYN_PROG.git. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 25% |
| Japan | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 46% |
| Other | 2 | 15% |
| Student > Bachelor | 1 | 8% |
| Student > Master | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 1 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 38% |
| Agricultural and Biological Sciences | 3 | 23% |
| Computer Science | 2 | 15% |
| Engineering | 2 | 15% |
| Unknown | 1 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 June 2018.
All research outputs
#14,287,221
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#4,575
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Outputs of similar age
#180,159
of 329,468 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 103 outputs
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