ArticleAlgorithms for molecular biology : AMB2014
Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data.
Article in Algorithms for molecular biology : AMB, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A refined Saccharomyces cerevisiae reference transcriptome from direct RNA sequencing, with a reusable pipeline for UTR annotation updates.FEMS yeast research · 2026Article
- Cytoplasmic cleavage of IMPA1 3' UTR is necessary for maintaining axon integrity.Cell reports · 2021Article
- Targeted capture enrichment followed by NGS: development and validation of a single comprehensive NIPT for chromosomal aneuploidies, microdeletion syndromes and monogenic diseases.Molecular cytogenetics · 2019Article
- iSeg: an efficient algorithm for segmentation of genomic and epigenomic data.BMC bioinformatics · 2018Article
- Two interacting PPR proteins are major Arabidopsis editing factors in plastid and mitochondria.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- SegCorr a statistical procedure for the detection of genomic regions of correlated expression.BMC bioinformatics · 2017Article
- An Adaptive Ridge Procedure for L0 Regularization.PloS one · 2016Article
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Authors and funding
5 authors.
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Abstract
backgroundChange point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution.
resultsBecause of its complexity which is almost linear in the sequence length when the maximal number of segments is constant, and as its performance had been acknowledged for microarrays, we propose to use the Pruned Dynamic Programming algorithm for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binomial distribution with which we model the data. We show that if the dispersion in the signal is known, the PDP algorithm can be used, and we provide an estimator for this dispersion. We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation. We propose to estimate the number of segments via a penalized likelihood criterion. We illustrate the performance of the proposed methodology on RNA-Seq data.
conclusionsWe illustrate the results of our approach on a real dataset and show its good performance. Our algorithm is available as an R package on the CRAN repository.
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