EDBT 2026 Demo / reviewers in the wild / expert
Eric Sobie
dblp:251/2589
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Processor architecture and microarchitecture · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis
read mapping |
0.4 | 1 | 2019 | Holistic optimization of an RNA-seq workflow for multi-threaded environments · Bioinform. 2019 |
Bioinformatics and computational biology › transcriptomics › RNA-seq analysis
RNA-seq data processing |
0.4 | 1 | 2019 | Holistic optimization of an RNA-seq workflow for multi-threaded environments · Bioinform. 2019 |
Processor architecture and microarchitecture › multithreading
multithreaded execution |
0.1 | 1 | 2019 | Holistic optimization of an RNA-seq workflow for multi-threaded environments · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
workflow optimization · 0.8burrows-wheeler alignment · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Holistic optimization of an RNA-seq workflow for multi-threaded environmentsabstractSUMMARY: For many next generation-sequencing pipelines, the most computationally intensive step is the alignment of reads to a reference sequence. As a result, alignment software such as the Burrows-Wheeler Aligner is optimized for speed and is often executed in parallel on the cloud. However, there are other less demanding steps that can also be optimized to significantly increase the speed especially when using many threads. We demonstrate this using a unique molecular identifier RNA-sequencing pipeline consisting of 3 steps: split, align, and merge. Optimization of all three steps yields a 40% increase in speed when executed using a single thread. However, when executed using 16 threads, we observe a 4-fold improvement over the original parallel implementation and more than an 8-fold improvement over the original single-threaded implementation. In contrast, optimizing only the alignment step results in just a 13% improvement over the original parallel workflow using 16 threads. AVAILABILITY AND IMPLEMENTATION: Code (M.I.T. license), supporting scripts and Dockerfiles are available at https://github.com/BioDepot/LINCS_RNAseq_cpp and Docker images at https://hub.docker.com/r/biodepot/rnaseq-umi-cpp/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Ling-Hong Hung, Wes Lloyd, Radhika Agumbe Sridhar, Saranya Devi Athmalingam Ravishankar, Yuguang Xiong, Eric Sobie, Ka Yee Yeung |
Bioinform. | 6 |