F-Seq: a feature density estimator for high-throughput sequence tags

By Alan P. Boyle, Justin Guinney, Gregory E. Crawford, Terrence S. Furey. Bioinform., 2008

361 citations recorded by the source corpus.

Summary: Tag sequencing using high-throughput sequencing technologies are now regularly employed to identify specific sequence features, such as transcription factor binding sites (ChIP-seq) or regions of open chromatin (DNase-seq). To intuitively summarize and display individual sequence data as an accurate and interpretable signal, we developed F-Seq, a software package that generates a continuous tag sequence density estimation allowing identification of biologically meaningful sites whose output can be displayed directly in the UCSC Genome Browser. Availability: The software is written in the Java language and is available on all major computing platforms for download at http://www.genome.duke.edu/labs/furey/software/fseq. Contact: terry.furey@duke.edu

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0a9a7a07ed45cbebe103affca52df5b7c5a0b4df

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Citations in the corpus

Measuring reproducibility of high-throughput experiments (2011, background): “Peaks were identiﬁed in biology labs, using nine commonly used and publicly available peak callers, namely, Peakseq [Rozowsky et al. (2009)], MACS [Zhang et al. (2008)], SPP [Kharchenko, Tolstorukov and Park (2008)], Fseq [Boyle et al. (2008)], Hotspot [Thurman et al. (2011)], Erange [Mortazavi et a”
Dual role of FoxA1 in androgen receptor binding to chromatin, androgen signalling and prostate cancer (2011, background): “…(DHS) sites) are good indicators to distinguish between accessible and non-accessible chromatin sites that are known to be highly cell-specific and often dictated by the available compilation of chromatin remodellers and other associated factors (John et al, 2008; Boyle et al, 2008).”
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection (2010, methodology): “employ a Gaussian kernel density estimator (G-KDE) that generates a continuous coverage estimate [14,33,34].”
From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis (2020, methodology): “F-seq [69] and PeakDEck [70] use kernel density estimation to profile fragment distribution.”
A clustering approach for identification of enriched domains from histone modification ChIP-Seq data (2009, positive): “We used four methods in comparison: QuEST (Valouev et al., 2008) version 2.1, F-Seq (Boyle et al., 2008) version 1.8.3, MACS (Zhang et al., 2008a) version 1.3.5 and FindPeaks (Fejes et al., 2008) version 3.2.2.3.”