Estimating enrichment of repetitive elements from high-throughput sequence data

被引：77
|
作者：
Day, Daniel S. [1 ,2 ]
Luquette, Lovelace J. [1 ]
Park, Peter J. [1 ,2 ,3 ]
Kharchenko, Peter V. [1 ,3 ]
机构：
[1] Harvard Univ, Sch Med, Ctr Biomed Informat, Boston, MA 02115 USA
[2] Harvard MIT Hlth Sci & Technol, Cambridge, MA 02139 USA
[3] Childrens Hosp, Informat Program, Boston, MA 02115 USA
来源：
GENOME BIOLOGY | 2010年 / 11卷 / 06期
关键词：
H3; LYSINE-9; METHYLATION; EMBRYONIC STEM-CELLS; CHIP-SEQ EXPERIMENTS; ENDOGENOUS RETROVIRUSES; TRANSPOSABLE ELEMENTS; HUMAN GENOME; HISTONE H3; TRANSCRIPTIONAL NETWORK; SATELLITE REPEATS; DNA METHYLATION;
D O I：
10.1186/gb-2010-11-6-r69
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 describe computational methods for analysis of repetitive elements from short-read sequencing data, and apply them to study histone modifications associated with the repetitive elements in human and mouse cells. Our results demonstrate that while accurate enrichment estimates can be obtained for individual repeat types and small sets of repeat instances, there are distinct combinatorial patterns of chromatin marks associated with major annotated repeat families, including H3K27me3/H3K9me3 differences among the endogenous retroviral element classes.
引用
收藏
页数：12
相关论文
共 50 条
  • [1] Estimating enrichment of repetitive elements from high-throughput sequence data
    Daniel S Day
    Lovelace J Luquette
    Peter J Park
    Peter V Kharchenko
    Genome Biology, 11
  • [2] High-throughput DNA sequence data compression
    Zhu, Zexuan
    Zhang, Yongpeng
    Ji, Zhen
    He, Shan
    Yang, Xiao
    BRIEFINGS IN BIOINFORMATICS, 2015, 16 (01) : 1 - 15
  • [3] High-throughput identification of structural variations from sequence trace data
    Koboldt, Daniel Christopher
    CELLULAR ONCOLOGY, 2007, 29 (02) : 120 - 120
  • [4] On the optimal trimming of high-throughput mRNA sequence data
    MacManes, Matthew D.
    FRONTIERS IN GENETICS, 2014, 5
  • [5] SeqyClean: A Pipeline for High-throughput Sequence Data Preprocessing
    Zhbannikov, Ilya Y.
    Hunter, Samuel S.
    Foster, James A.
    Settles, Matthew L.
    ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS, 2017, : 407 - 416
  • [6] Accurate and exact CNV identification from targeted high-throughput sequence data
    Nord, Alex S.
    Lee, Ming
    King, Mary-Claire
    Walsh, Tom
    BMC GENOMICS, 2011, 12
  • [7] Accurate and exact CNV identification from targeted high-throughput sequence data
    Alex S Nord
    Ming Lee
    Mary-Claire King
    Tom Walsh
    BMC Genomics, 12
  • [8] Detecting genomic deletions from high-throughput sequence data with unsupervised learning
    Li X.
    Wu Y.
    BMC Bioinformatics, 2022, 23 (Suppl 8)
  • [9] Identification and correction of systematic error in high-throughput sequence data
    Meacham, Frazer
    Boffelli, Dario
    Dhahbi, Joseph
    Martin, David I. K.
    Singer, Meromit
    Pachter, Lior
    BMC BIOINFORMATICS, 2011, 12
  • [10] Identification and correction of systematic error in high-throughput sequence data
    Frazer Meacham
    Dario Boffelli
    Joseph Dhahbi
    David IK Martin
    Meromit Singer
    Lior Pachter
    BMC Bioinformatics, 12
12345