Demo Data from EDBT 2026 / reviewers in the wild / expert
Markus A. Grohme
dblp:127/9453
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0002-4250-5600ORCID · reported
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis › sequencing read preprocessing
duplicate read removal |
0.2 | 1 | 2013 | Filtering duplicate reads from 454 pyrosequencing data · Bioinform. 2013 |
Bioinformatics and computational biology › sequence analysis
sequencing data processing |
0.2 | 1 | 2013 | Filtering duplicate reads from 454 pyrosequencing data · Bioinform. 2013 |
Methods — techniques the papers use, named apart from their topics
read clustering · 0.2bayesian distance measures · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Filtering duplicate reads from 454 pyrosequencing dataabstractMOTIVATION: Throughout the recent years, 454 pyrosequencing has emerged as an efficient alternative to traditional Sanger sequencing and is widely used in both de novo whole-genome sequencing and metagenomics. Especially the latter application is extremely sensitive to sequencing errors and artificially duplicated reads. Both are common in 454 pyrosequencing and can create a strong bias in the estimation of diversity and composition of a sample. To date, there are several tools that aim to remove both sequencing noise and duplicates. Nevertheless, duplicate removal is often based on nucleotide sequences rather than on the underlying flow values, which contain additional information. RESULTS: With the novel tool JATAC, we present an approach towards a more accurate duplicate removal by analysing flow values directly. Making use of previous findings on 454 flow data characteristics, we combine read clustering with Bayesian distance measures. Finally, we provide a benchmark with an existing algorithm. AVAILABILITY: JATAC is freely available under the General Public License from http://malde.org/ketil/jatac/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Susanne Balzer, Ketil Malde, Markus A. Grohme, Inge Jonassen |
Bioinform. | 3 |