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.

Conrad Leonard

dblp:370/5263 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-4131-2065ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequencing read preprocessing
duplicate marking
0.712023
streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023
Bioinformatics and computational biology
sequence analysis
0.712023
streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023
Bioinformatics and computational biology › sequence analysis › sequencing data processing
read preprocessing
0.212023
streammd: fast low-memory duplicate marking using a Bloom filter · Bioinform. 2023

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

bloom filter · 0.7
YearPublicationVenuePosition
2023 streammd: fast low-memory duplicate marking using a Bloom filter
abstract
SUMMARY: Identification of duplicate templates is a common preprocessing step in bulk sequence analysis; for large libraries, this can be resource intensive. Here, we present streammd: a fast, memory-efficient, single-pass duplicate marker operating on the principle of a Bloom filter. streammd closely reproduces outputs from Picard MarkDuplicates while being substantially faster, and requires much less memory than SAMBLASTER. AVAILABILITY AND IMPLEMENTATION: streammd is a C++ program available from GitHub https://github.com/delocalizer/streammd under the MIT license.
Conrad Leonard
Bioinform.1