Amanote Research

Amanote Research

    RegisterSign In

Statistical Issues in Binding Site Identification Through CLIP-seq

Statistics and its Interface - United States
doi 10.4310/sii.2015.v8.n4.a2
Full Text
Open PDF
Abstract

Available in full text

Categories
Applied MathematicsStatisticsProbability
Date

January 1, 2015

Authors
Xiaowei ChenDongjun ChungGiovanni StefaniFrank J. SlackHongyu Zhao
Publisher

International Press of Boston


Related search

A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data

PLoS ONE
Multidisciplinary
2014English

omniCLIP: Probabilistic Identification of Protein-Rna Interactions From CLIP-seq Data

Genome Biology
2018English

Identification of Transcription Factor Binding Sites Using ATAC-seq

Genome Biology
2019English

Biomarker Identification From RNA-Seq Data Using a Robust Statistical Approach

Bioinformation
2018English

Transcriptome-Wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP

Cell
BiochemistryGeneticsMolecular Biology
2010English

Saturation Analysis of ChIP-seq Data for Reproducible Identification of Binding Peaks

Genome Research
Genetics
2015English

Identification of a Second Substrate-Binding Site in Solute-Sodium Symporters

Journal of Biological Chemistry
BiochemistryCell BiologyMolecular Biology
2014English

Identification of Novel Cyclin A2 Binding Site and Nanomolar Inhibitors

Biophysical Journal
Biophysics
2019English

Every Site Counts: Submitting Transcription Factor-Binding Site Information Through the CollecTF Portal

Journal of Bacteriology
MicrobiologyMolecular Biology
2015English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy