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Sen Wang 0012

dblp:69/6403-12 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0008-7180-7473ORCID · verified

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 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › single-cell analysis › single-cell data preprocessing
demultiplexing
0.812024
mgikit: demultiplexing toolkit for MGI fastq files · Bioinform. 2024
Bioinformatics and computational biology
genomics
0.812024
mgikit: demultiplexing toolkit for MGI fastq files · Bioinform. 2024
Bioinformatics and computational biology › sequence analysis
sequencing data processing
0.812024
mgikit: demultiplexing toolkit for MGI fastq files · Bioinform. 2024
Bioinformatics and computational biology › sequence analysis › sequencing data processing
sequencing quality control
0.212024
mgikit: demultiplexing toolkit for MGI fastq files · Bioinform. 2024

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

quality report generation · 0.8demultiplexing · 0.8
YearPublicationVenuePosition
2024 mgikit: demultiplexing toolkit for MGI fastq files
abstract
SUMMARY: MGI sequencing is reported to be an inexpensive solution to obtain genomics information. There is a growing need for software and tools to analyse MGI's outputs efficiently. mgikit is a tool collection to demultiplex MGI fastq data, reformat it effectively and produce visual quality reports. mgikit overcomes several limitations of the standard MGI demultiplexer. It is highly customizable to suit different kinds of datasets and is designed to achieve high performance and optimal memory utilization. AVAILABILITY AND IMPLEMENTATION: The tool and its documentation are available at: https://sagc-bioinformatics.github.io/mgikit/.
Ziad Al Bkhetan, Sen Wang 0012
Bioinform.2