Demo Data from VLDB 2026 / reviewers in the wild / expert
Yafeng Zhu
dblp:193/9601
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
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 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › proteomics
proteogenomics |
0.6 | 1 | 2022 | Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides · Bioinform. 2022 |
Bioinformatics and computational biology
proteomics |
0.6 | 1 | 2022 | Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
three-frame translation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptidesabstractSUMMARY: We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling including optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we have reanalyzed six public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to >5% of the total number of peptides identified. AVAILABILITY AND IMPLEMENTATION: The software is freely available. pypgatk: https://github.com/bigbio/py-pgatk/ and pgdb: https://nf-co.re/pgdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Husen M. Umer, Enrique Audain, Yafeng Zhu, Julianus Pfeuffer, Timo Sachsenberg, Janne Lehtiö, Rui Mamede Branca, Yasset Pérez-Riverol |
Bioinform. | 3 |