Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shengkang Li
dblp:146/9841
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-6864-5644ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 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
2 papers |
Bioinformatics and computational biology · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › metagenomics
metagenome profiling |
0.5 | 1 | 2021 | SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters · Bioinform. 2021 |
Bioinformatics and computational biology
metagenomics |
0.5 | 1 | 2021 | SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters · Bioinform. 2021 |
Bioinformatics and computational biology › transcriptomics › transcript assembly
de novo transcriptome assembly |
0.2 | 1 | 2014 | SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads · Bioinform. 2014 |
Bioinformatics and computational biology › sequence analysis › sequence assembly
transcriptome assembly |
0.2 | 1 | 2014 | SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads · Bioinform. 2014 |
High-performance computing
cluster computing |
0.1 | 1 | 2021 | SOAPMetaS: profiling large metagenome datasets efficiently on distributed clusters · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
marker gene-based profiling · 1.0apache spark · 1.0de bruijn graph assembly · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | SOAPMetaS: profiling large metagenome datasets efficiently on distributed clustersabstractSUMMARY: Rapid increase of the data size in metagenome researches has raised the demand for new tools to process large datasets efficiently. To accelerate the metagenome profiling process in the scenario of big data, we developed SOAPMetaS, a marker gene-based multiple-sample metagenome profiling tool built on Apache Spark. SOAPMetaS demonstrates high performance and scalability to process large datasets. It can process 80 samples of FASTQ data, summing up to 416 GiB, in around half an hour; and the accuracy of species profiling results of SOAPMetaS is similar to that of MetaPhlAn2. SOAPMetaS can deal with a large volume of metagenome data more efficiently than common-used single-machine tools. AVAILABILITY AND IMPLEMENTATION: Source code is implemented in Java and freely available at https://github.com/BGI-flexlab/SOAPMetaS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Shixu He, Zhibo Huang, Shengkang Li, Yong Zhang 0036, Gengyun Zhang |
Bioinform. | 5 |
| 2014 | SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq readsabstractMOTIVATION: Transcriptome sequencing has long been the favored method for quickly and inexpensively obtaining a large number of gene sequences from an organism with no reference genome. Owing to the rapid increase in throughputs and decrease in costs of next- generation sequencing, RNA-Seq in particular has become the method of choice. However, the very short reads (e.g. 2 × 90 bp paired ends) from next generation sequencing makes de novo assembly to recover complete or full-length transcript sequences an algorithmic challenge. RESULTS: Here, we present SOAPdenovo-Trans, a de novo transcriptome assembler designed specifically for RNA-Seq. We evaluated its performance on transcriptome datasets from rice and mouse. Using as our benchmarks the known transcripts from these well-annotated genomes (sequenced a decade ago), we assessed how SOAPdenovo-Trans and two other popular transcriptome assemblers handled such practical issues as alternative splicing and variable expression levels. Our conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution. AVAILABILITY AND IMPLEMENTATION: Source code and user manual are available at http://sourceforge.net/projects/soapdenovotrans/. Yinlong Xie, Gengxiong Wu, Jingbo Tang, Ruibang Luo, Jordan Patterson, Shanlin Liu, Weihua Huang, Guangzhu He, Shengchang Gu, Shengkang Li, Tak Wah Lam, Yingrui Li, Gane Ka-Shu Wong, Jun Wang 0004 |
Bioinform. | 10 |