A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

被引:107
|
作者
Cheng, Chao [1 ]
Yan, Koon-Kiu [1 ]
Yip, Kevin Y. [1 ,2 ]
Rozowsky, Joel [1 ]
Alexander, Roger [1 ]
Shou, Chong [1 ]
Gerstein, Mark [1 ,3 ,4 ]
机构
[1] Yale Univ, Dept Mol Biophys & Biochem, New Haven, CT 06520 USA
[2] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
[3] Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA
[4] Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA
来源
GENOME BIOLOGY | 2011年 / 12卷 / 02期
关键词
GENOME-WIDE MAPS; HISTONE MODIFICATIONS; TRANSCRIPTION ELONGATION; CAENORHABDITIS-ELEGANS; STERILE-20; KINASE; X-CHROMOSOME; CODE; DNA; ACETYLATION; PROMOTERS;
D O I
10.1186/gb-2011-12-2-r15
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] CORRELATING CELLULAR FEATURES WITH GENE EXPRESSION USING CCA
    Subramanian, Vaishnavi
    Chidester, Benjamin
    Ma, Jian
    Do, Minh N.
    2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018), 2018, : 805 - 808
  • [42] Supervised feature selection on gene expression microarray datasets using manifold learning
    Zare, Masoumeh
    Azizizadeh, Najmeh
    Kazemipour, Ali
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 237
  • [43] Identification of Breast Cancer Subtypes Using Multiple Gene Expression Microarray Datasets
    Mendes, Alexandre
    AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2011, 7106 : 92 - 101
  • [44] Identification of IPF endotypes using publicly available gene-expression datasets
    Moss, B. J.
    Gandhi, T.
    Robertson, M. J.
    Poli, F.
    De Frias, S. Poli
    Celada, L. J.
    Tsoyi, K.
    Vasquez, F. Romero
    Ryter, S. W.
    Rosas, I. O.
    Coarfa, C.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2021, 203 (09)
  • [45] Visualizing High Dimensional Datasets Using Parallel Coordinates: Application to Gene Prioritization
    Boogaerts, Thomas
    Tranchevent, Leon-Charles
    Pavlopoulos, Georgios A.
    Aerts, Jan
    Vandewalle, Joos
    IEEE 12TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS & BIOENGINEERING, 2012, : 52 - 57
  • [46] Statistical Modeling Using a New Distribution with Application in Health Data
    Abdulrahman, Alanazi Talal
    Alshawarbeh, Etaf
    Abd El-Raouf, Mahmoud M.
    MATHEMATICS, 2023, 11 (14)
  • [47] Modeling for influenza vaccines and adjuvants profile for safety prediction system using gene expression profiling and statistical tools
    Sasaki, Eita
    Momose, Haruka
    Hiradate, Yuki
    Furuhata, Keiko
    Takai, Mamiko
    Asanuma, Hideki
    Ishii, Ken J.
    Mizukami, Takuo
    Hamaguchi, Isao
    PLOS ONE, 2018, 13 (02):
  • [48] A STATISTICAL MODEL TO ASSESS (ALLELE-SPECIFIC) ASSOCIATIONS BETWEEN GENE EXPRESSION AND EPIGENETIC FEATURES USING SEQUENCING DATA
    Rashid, Naim U.
    Sun, Wei
    Ibrahim, Joseph G.
    ANNALS OF APPLIED STATISTICS, 2016, 10 (04): : 2254 - 2273
  • [49] Gene Expression Profiling of Cervical Cancer Using Statistical Method
    Kapse, Deepak
    Mukherjee, Koel
    Banerjee, Debadyuti
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, 2017, 509 : 159 - 165
  • [50] An Expandable Hierarchical Statistical Framework for Count Data Modeling and its Application to Object Classification
    Bakhtiari, Ali Shojaee
    Bouguila, Nizar
    2011 23RD IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2011), 2011, : 817 - 824