Open Access Highly Accessed Research article

Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study

Lei Sun123, Zhihua Zhang2, Timothy L Bailey3, Andrew C Perkins4, Michael R Tallack4, Zhao Xu1 and Hui Liu1*

Author Affiliations

1 School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221008, JiangSu, PR China

2 Center for Computational Biology, and Laboratory of Disease Genomics and Personalized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Chaoyang District, Beijing, 100029, PR China

3 Institute for Molecular Bioscience, The University of Queensland, Brisbane, 4072, Queensland, Australia

4 Mater Medical Research Institute, Mater Hospital, Brisbane, 4101, Queensland, Australia

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BMC Bioinformatics 2012, 13:331  doi:10.1186/1471-2105-13-331

Published: 13 December 2012

Additional files

Additional file 1:

Supplementary materials. It contains supplementary materials supporting the main text.

Format: ZIP Size: 332KB Download file

Open Data

Additional file 2:

GTF of high-quality assembled transcripts. It is a GTF file recording the 28963 high-quality assemblies, which were used in the differential expression tests. And their structures can be visualized by UCSC genome browser.

Format: ZIP Size: 4MB Download file

Open Data

Additional file 3:

GTF of novel lncRNAs. It is a GTF file recording the 308 novel lncRNAts detected by lncRScan. The transcript structures can be visualized by UCSC genome browser.

Format: ZIP Size: 19KB Download file

Open Data

Additional file 4:

GTF of differentially expressed lncRNAs. It is a GTF file recording the 13 differentially expressed lncRNAs between the WT and Klf1 KO samples. The transcript structures can be visualized by UCSC genome browser.

Format: ZIP Size: 1KB Download file

Open Data