Enhancing Epitranscriptome Module Detection from m6A-Seq Data Using Threshold-Based Measurement Weighting Strategy

Joint Authors

Meng, Jia
Wei, Zhen
Chen, Kunqi
Liu, Hui
Magalhães, João Pedro de
Rong, Rong
Lu, Zhiliang

Source

BioMed Research International

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-06-14

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Medicine

Abstract EN

To date, with well over 100 different types of RNA modifications associated with various molecular functions identified on diverse types of RNA molecules, the epitranscriptome has emerged to be an important layer for gene expression regulation.

It is of crucial importance and increasing interest to understand how the epitranscriptome is regulated to facilitate different biological functions from a global perspective, which may be carried forward by finding biologically meaningful epitranscriptome modules that respond to upstream epitranscriptome regulators and lead to downstream biological functions; however, due to the intrinsic properties of RNA molecules, RNA modifications, and relevant sequencing technique, the epitranscriptome profiled from high-throughput sequencing approaches often suffers from various artifacts, jeopardizing the effectiveness of epitranscriptome modules identification when using conventional approaches.

To solve this problem, we developed a convenient measurement weighting strategy, which can largely tolerate the artifacts of high-throughput sequencing data.

We demonstrated on real data that the proposed measurement weighting strategy indeed brings improved performance in epitranscriptome module discovery in terms of both module accuracy and biological significance.

Although the new approach is integrated with Euclidean distance measurement in a hierarchical clustering scenario, it has great potential to be extended to other distance measurements and algorithms as well for addressing various tasks in epitranscriptome analysis.

Additionally, we show for the first time with rigorous statistical analysis that the epitranscriptome modules are biologically meaningful with different GO functions enriched, which established the functional basis of epitranscriptome modules, fulfilled a key prerequisite for functional characterization, and deciphered the epitranscriptome and its regulation.

American Psychological Association (APA)

Chen, Kunqi& Wei, Zhen& Liu, Hui& Magalhães, João Pedro de& Rong, Rong& Lu, Zhiliang…[et al.]. 2018. Enhancing Epitranscriptome Module Detection from m6A-Seq Data Using Threshold-Based Measurement Weighting Strategy. BioMed Research International،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1124876

Modern Language Association (MLA)

Chen, Kunqi…[et al.]. Enhancing Epitranscriptome Module Detection from m6A-Seq Data Using Threshold-Based Measurement Weighting Strategy. BioMed Research International No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1124876

American Medical Association (AMA)

Chen, Kunqi& Wei, Zhen& Liu, Hui& Magalhães, João Pedro de& Rong, Rong& Lu, Zhiliang…[et al.]. Enhancing Epitranscriptome Module Detection from m6A-Seq Data Using Threshold-Based Measurement Weighting Strategy. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1124876

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1124876