Demo Data from EDBT 2026 / reviewers in the wild / expert
Naruki Yoshikawa
dblp:228/5053
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0003-1546-8709ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
sequence analysis |
0.3 | 1 | 2018 | Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018 |
Bioinformatics and computational biology › sequence analysis › read mapping
split-read alignment |
0.3 | 1 | 2018 | Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018 |
Bioinformatics and computational biology › genomics
variant calling |
0.1 | 1 | 2018 | Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
probabilistic alignment · 0.3paired-end read information · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Combining probabilistic alignments with read pair information improves accuracy of split-alignmentsabstractMotivation: Split-alignments provide base-pair-resolution evidence of genomic rearrangements. In practice, they are found by first computing high-scoring local alignments, parts of which are then combined into a split-alignment. This approach is challenging when aligning a short read to a large and repetitive reference, as it tends to produce many spurious local alignments leading to ambiguities in identifying the correct split-alignment. This problem is further exacerbated by the fact that rearrangements tend to occur in repeat-rich regions. Results: We propose a split-alignment technique that combats the issue of ambiguous alignments by combining information from probabilistic alignment with positional information from paired-end reads. We demonstrate that our method finds accurate split-alignments, and that this translates into improved performance of variant-calling tools that rely on split-alignments. Availability and implementation: An open-source implementation is freely available at: https://bitbucket.org/splitpairedend/last-split-pe. Supplementary information: Supplementary data are available at Bioinformatics online. Anish Man Singh Shrestha, Naruki Yoshikawa, Kiyoshi Asai |
Bioinform. | 2 |