Main logo
?
tutorial arrow
×
Create your own tool library
Bookmark tools and put favorites into folders to find them easily.

LR450K

Online

A computational strategy to predict DNA methylation by integrating cell-type specific 450K array data and common DNA sequence features. We developed a computational model that is trained on 14 tissues with both whole genome bisulfite sequencing and 450K array data. This model integrates information derived from the similarity of local methylation pattern between tissues, the methylation information of flanking CpG sites and the methylation tendency of flanking DNA sequences. When applying to a new sample, our model only requires input of 450K array data and avoids the need of histone modification or MeDIP-seq/MRE-seq data that are not always available, which significantly expands its applicability.

User report

tutorial arrow
×
Vote up tools and offer feedback
Give value to tools and make your expertise visible
Give your feedback on this tool
Sign up for free to join and share with the community

0 user reviews

0 user reviews

No review has been posted.

LR450K forum

tutorial arrow
×
Communicate with other users
Participate in the forum to get support for using tools. Ask questions about technical specifications.
Take part in the discussion
Sign up for free to ask question and share your advices

No open topic.

LR450K classification

LR450K specifications

Software type:
Package/Module
Restrictions to use:
None
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:
Advanced
Maintained:
Yes
Interface:
Command line interface
Biological technology:
Illumina
Programming languages:
R
Stability:
Stable

LR450K distribution

versioning

tutorial arrow
×
Upload and version your source code
Get a DOI for each update to improve tool traceability. Archive your releases so the community can easily visualize progress on your work.
Facilitate your tool traceability
Sign up for free to upload your code and get a DOI

No versioning.

LR450K support

Documentation

Maintainer

  • Wei Wang <>

Credits

tutorial arrow
×
Promote your skills
Define all the tasks you managed and assign your profile the appropriate badges. Become an active member.
Promote your work
Sign up for free to badge your contributorship

Publications

Institution(s)

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, USA; Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA

Funding source(s)

This project was supported by the National Natural Science Foundation of China under No. 61503061 (SCF) and grants from the Rheumatology Research Foundation, the Arthritis Foundation, and the National Institutes of Arthritis, Musculoskeletal and Skin Diseases (RO1065066).

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.