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Constance Ciaudo

dblp:45/8711 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-0857-4506ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3

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%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
sequence analysis
0.112012
ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data · Bioinform. 2012
Bioinformatics and computational biology › transcriptomics
small RNA-seq analysis
0.112012
ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data · Bioinform. 2012
Bioinformatics and computational biology › genomics
next-generation sequencing data analysis
0.112010
girafe - an R/Bioconductor package for functional exploration of aligned next-generation sequencing reads · Bioinform. 2010

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

read alignment · 0.1annotation pipeline · 0.1
YearPublicationVenuePosition
2019 Comparative analysis of differential gene expression tools for RNA sequencing time course data
abstract
RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.
Daniel Spies, Peter F. Renz, Tobias A. Beyer, Constance Ciaudo
Briefings Bioinform.4
2012 ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data
abstract
SUMMARY: Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs. It also has a module to identify regions significantly enriched with short reads, which cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or small interfering RNA (siRNA)-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that sRNA raw data from the three current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including mammals and plants. AVAILABILITY: Source code, annotation files, manual and online version are available at http://ncpro.curie.fr/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Chong-Jian Chen, Nicolas Servant, Joern Toedling, Alexis Sarazin, Antonin Marchais, Evelyne Duvernois-Berthet, Valérie Cognat, Vincent Colot, Olivier Voinnet, Edith Heard, Constance Ciaudo, Emmanuel Barillot
Bioinform.11
2010 girafe - an R/Bioconductor package for functional exploration of aligned next-generation sequencing reads
abstract
UNLABELLED: The R/Bioconductor package girafe facilitates the functional exploration of alignments of sequence reads from next-generation sequencing data to a genome. It allows users to investigate the genomic intervals together with the aligned reads and to work with, visualise and export these intervals. Moreover, the package operates within and extends the ever-growing Bioconductor framework and thus enables users to leverage a multitude of methods for their data in order to answer specific research questions. AVAILABILITY AND IMPLEMENTATION: The R package girafe is available from the Bioconductor web site: http://www.bioconductor.org/packages/release/bioc/html/girafe.html. An extensive vignette and the Bioconductor mailing lists provide additional documentation and help for using the package.
Joern Toedling, Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot
Bioinform.2