TY - JOUR
T1 - Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data
AU - Qin, Zhaohui
AU - Li, Ben
AU - Conneely, Karen N.
AU - Wu, Hao
AU - Hu, Ming
AU - Ayyala, Deepak Nag
AU - Park, Yongseok
AU - Jin, Victor X.
AU - Zhang, Fangyuan
AU - Zhang, Han
AU - Li, Li
AU - Lin, Shili
N1 - Funding Information:
We thank all members of the Statistical and Applied Mathematical Sciences Institute (SAMSI) Epigenetics Working Group as part of the SAMSI Beyond Bioinformatics Program. We are also grateful for the support of Drs. Sujit Ghosh and Snehalata Huzurbazar at SAMSI. This material was based upon work partially supported by the National Science Foundation (NSF) under Grant DMS-1127914 to SAMSI. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. This research was also supported in part from NSF Grant DMS-1220772 and NIH Grant 1R01GM114142-01.
Publisher Copyright:
© 2016, International Chinese Statistical Association.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - With the rapid development of high-throughput technologies such as array and next generation sequencing, genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.
AB - With the rapid development of high-throughput technologies such as array and next generation sequencing, genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.
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U2 - 10.1007/s12561-016-9145-0
DO - 10.1007/s12561-016-9145-0
M3 - Article
AN - SCOPUS:84960105545
SN - 1867-1764
VL - 8
SP - 284
EP - 309
JO - Statistics in Biosciences
JF - Statistics in Biosciences
IS - 2
ER -
