Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Tao Li 0033

dblp:75/4601-33 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
0since 2021 · last 2020
0000-0002-7206-5597ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-author

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 · 50% Computational science and engineering · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genome engineering
0.412020
MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries · Bioinform. 2020
Computational science and engineering
parallel computing
0.412020
MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries · Bioinform. 2020
Parallel and multicore computing
parallel programming models
0.412020
MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries · Bioinform. 2020

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

multi-process parallelism · 0.9
YearPublicationVenuePosition
2020 MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries
abstract
SUMMARY: The recent advance in genome engineering technologies based on CRISPR/Cas9 system is enabling people to systematically understand genomic functions. A short RNA string (the CRISPR guide RNA) can guide the Cas9 endonuclease to specific locations in complex genomes to cut DNA double-strands. The CRISPR guide RNA is essential for gene editing systems. Recently, the GuideScan software is developed to design CRISPR guide RNA libraries, which can be used for genome editing of coding and non-coding genomic regions effectively. However, GuideScan is a serial program and computationally expensive for designing CRISPR guide RNA libraries from large genomes. Here, we present an efficient guide RNA library designing tool (MultiGuideScan) by implementing multiple processes of GuideScan. MultiGuideScan speeds up the guide RNA library designing about 9-12 times on a 32-process mode comparing to GuideScan. MultiGuideScan makes it possible to design guide RNA libraries from large genomes. AVAILABILITY AND IMPLEMENTATION: MULTIGUIDESCAN IS AVAILABLE AT GITHUB: https://github.com/bioinfomaticsCSU/MultiGuideScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Tao Li 0033, Shaokai Wang, Feng Luo 0001, Fang-Xiang Wu, Jianxin Wang 0001
Bioinform.1
2020 MultiMotifMaker: A Multi-Thread Tool for Identifying DNA Methylation Motifs from Pacbio Reads
abstract
The methylation of DNA is an important mechanism to control biological processes. Recently, the Pacbio SMRT technology provides a new way to identify base methylation in the genome. MotifMaker is a tool developed by Pacbio for discovering DNA methylation motifs from methylated DNA sequences. However, MotifMaker is single-threaded and computational expensive for identifying methylation motifs from large genomes. Here, we present an efficient motif finding algorithm (MultiMotifMaker) by implementing multi threads of the MotifMaker. The MultiMotifMaker speeds up the motif search about 8-9 times on a 32 core computer comparing to MotifMaker. MultiMotifMaker makes it possible to identify methylation motifs from Pacbio reads for large genomes.
Tao Li 0033, Xiankai Zhang, Feng Luo 0001, Fang-Xiang Wu, Jianxin Wang 0001
IEEE ACM Trans. Comput. Biol. Bioinform.1