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Juliette Hayer

dblp:124/1112 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-4899-9637ORCID · reported

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%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
metagenomics
0.412019
Simulating Illumina metagenomic data with InSilicoSeq · Bioinform. 2019
Bioinformatics and computational biology
sequencing simulation
0.412019
Simulating Illumina metagenomic data with InSilicoSeq · Bioinform. 2019

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

in silico read simulation · 0.4
YearPublicationVenuePosition
2019 Simulating Illumina metagenomic data with InSilicoSeq
abstract
Motivation: The accurate in silico simulation of metagenomic datasets is of great importance for benchmarking bioinformatics tools as well as for experimental design. Users are dependant on large-scale simulation to not only design experiments and new projects but also for accurate estimation of computational needs within a project. Unfortunately, most current read simulators are either not suited for metagenomics, out of date or relatively poorly documented. In this article, we describe InSilicoSeq, a software package to simulate metagenomic Illumina sequencing data. InsilicoSeq has a simple command-line interface and extensive documentation. Results: InSilicoSeq is implemented in Python and capable of simulating realistic Illumina (meta) genomic data in a parallel fashion with sensible default parameters. Availability and implementation: Source code and documentation are available under the MIT license at https://github.com/HadrienG/InSilicoSeq and https://insilicoseq.readthedocs.io/. Supplementary information: Supplementary data are available at Bioinformatics online.
Hadrien Gourlé, Oskar Karlsson-Lindsjö, Juliette Hayer, Erik Bongcam-Rudloff
Bioinform.3