ArticleBMC genomics2013
QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Molecular determinants of pathogenicity in a Streptococcus equi subsp. zooepidemicus strain from wild boar in Russia.Molecular genetics and genomics : MGG · 2026Article
- Predicting Protein-Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence.Biology · 2022Article
- Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.Genome biology · 2022Article
- Genomic patterns of transcription-replication interactions in mouse primary B cells.Nucleic acids research · 2022Article
- Article
- A physical basis for quantitative ChIP-sequencing.The Journal of biological chemistry · 2020Article
- Using Random Forest Model Combined With Gabor Feature to Predict Protein-Protein Interaction From Protein Sequence.Evolutionary bioinformatics online · 2020Article
- Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.Scientific reports · 2019Article
- Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.Scientific reports · 2018Article
- Transcription factor-associated combinatorial epigenetic pattern reveals higher transcriptional activity of TCF7L2-regulated intragenic enhancers.BMC genomics · 2017Article
- An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences.Oncotarget · 2017Article
- A comprehensive comparison of tools for differential ChIP-seq analysis.Briefings in bioinformatics · 2016Article
- Statistical challenges in analyzing methylation and long-range chromosomal interaction data.Statistics in biosciences · 2016Article
- CrossNorm: a novel normalization strategy for microarray data in cancers.Scientific reports · 2016Article
- Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.BioMed research international · 2016Article
- Protein Remote Homology Detection Based on an Ensemble Learning Approach.BioMed research international · 2016Article
- Calibrating ChIP-Seq with Nucleosomal Internal Standards to Measure Histone Modification Density Genome Wide.Molecular cell · 2015Article
- Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence.BioMed research international · 2015Article
- Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.BMC systems biology · 2015Article
- Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico.BioMed research international · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundMany computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.
resultsIn this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.
conclusionsSeveral advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.