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Peiyang Fang

dblp:370/4818 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › proteomics › proteome comparison
differential protein expression analysis
0.712023
DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data · Bioinform. 2023
Bioinformatics and computational biology
proteomics
0.712023
DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data · Bioinform. 2023
Bioinformatics and computational biology › proteomics
quantitative proteomics
0.712023
DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data · Bioinform. 2023

Methods — techniques the papers use, named apart from their topics

statistical hypothesis testing · 0.7functional enrichment analysis · 0.7
YearPublicationVenuePosition
2023 DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data
abstract
SUMMARY: Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis. AVAILABILITY AND IMPLEMENTATION: DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.
Zhenhuan Feng, Peiyang Fang
Bioinform.2