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Julia Zeitlinger

dblp:136/2601 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · unresolved

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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene regulation
regulatory genomics
0.212013
Identification of transcription factor binding sites from ChIP-seq data at high resolution · Bioinform. 2013
Bioinformatics and computational biology › gene regulation
transcription factor binding site prediction
0.212013
Identification of transcription factor binding sites from ChIP-seq data at high resolution · Bioinform. 2013
Bioinformatics and computational biology › gene regulation
enhancer analysis
0.012013
Identification of transcription factor binding sites from ChIP-seq data at high resolution · Bioinform. 2013
Bioinformatics and computational biology
gene regulation
0.012013
Identification of transcription factor binding sites from ChIP-seq data at high resolution · Bioinform. 2013

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

ChIP-exo · 0.2
YearPublicationVenuePosition
2013 Identification of transcription factor binding sites from ChIP-seq data at high resolution
abstract
MOTIVATION: Chromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) is widely used to study the in vivo binding sites of transcription factors (TFs) and their regulatory targets. Recent improvements to ChIP-seq, such as increased resolution, promise deeper insights into transcriptional regulation, yet require novel computational tools to fully leverage their advantages. RESULTS: To this aim, we have developed peakzilla, which can identify closely spaced TF binding sites at high resolution (i.e. resolves individual binding sites even if spaced closely), as we demonstrate using semisynthetic datasets, performing ChIP-seq for the TF Twist in Drosophila embryos with different experimental fragment sizes, and analyzing ChIP-exo datasets. We show that the increased resolution reached by peakzilla is highly relevant, as closely spaced Twist binding sites are strongly enriched in transcriptional enhancers, suggesting a signature to discriminate functional from abundant non-functional or neutral TF binding. Peakzilla is easy to use, as it estimates all the necessary parameters from the data and is freely available. AVAILABILITY AND IMPLEMENTATION: The peakzilla program is available from https://github.com/steinmann/peakzilla or http://www.starklab.org/data/peakzilla/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Anaïs F. Bardet, Jonas Steinmann, Sangeeta Bafna, Juergen A. Knoblich, Julia Zeitlinger, Alexander Stark 0001
Bioinform.5