Jie Ren
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Jie Ren


Summary

My research interests focus on modeling and comparing genomic and metagenomics sequences, including both fundamental theoretical work on statistical inferences and real world applications to biological problems. The specific topics I have examined include
• Estimation of Markov chain properties based on NGS short reads without reads assembly
• Comparing genomic and metagenomic samples using alignment-free sequence comparison methods
• Prediction of virus-host interactions and classification of virus and host sequences

Bioinformatics Computational Biology Ecology Environmental Sciences Microbiology

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Work details

Ph.D.

University of Southern California
August 2013
Computational Biology and Bioinformatics
RESEARCH INTERESTS My research interests focus on modeling and comparing genomic and metagenomics sequences, including both fundamental theoretical work on statistical inferences and real world applications to biological problems. The specific topics I have examined include • Estimation of Markov chain properties based on NGS short reads without reads assembly • Comparing genomic and metagenomic samples using alignment-free sequence comparison methods • Prediction of virus-host interactions and classification of virus and host sequences PUBLICATIONS 1 Ahlgren, N.∗, Ren, J.∗, Lu, Y., Fuhrman, J., Sun, F. (2016). Alignment-free d∗2 oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Research: gkw1002. ∗ co-first authors. 2 Liao, W.∗, Ren, J.∗, Wang, K., Wang, S., Zeng, F., Wang, Y., Sun, F. (2016). Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains. Scientific Reports: 6. ∗ co-first authors. 3 Zhang, M., Yang, L., Ren, J., Ahlgren, N., Fuhrman, J., and Sun, F. (2016). Prediction of virus-host infectious association by supervised learning methods. Accepted by APBC2017. To appear in BMC Bioinformatics. 4 Bai, X., Tang, K., Ren, J., Waterman, M., Sun, F. (2016). Optimal Choice of Word Length When Comparing Two Markov Sequences Using a χ2-statistic. Accepted by ICIMB2016. To appear in BMC Genomics. 5 Ren, J., Song, K., Deng, M., Reinert, G., Cannon, C. H., and Sun, F. (2015). Inference of Markovian Properties of Molecular Sequences from NGS Data and Applications to Comparative Genomics. Bioinformatics, 32(7): 993-1000. 6 Ren, J., Song, K., Sun, F., Deng, M., and Reinert, G. (2013). Multiple alignment-free sequence comparison. Bioinformatics, 29(21), 2690–2698. 7 Song, K., Ren, J., Reinert, G., Deng, M., Waterman, M. S., and Sun, F. (2013). New developments of alignment- free sequence comparison: measures, statistics and next-generation sequencing. Briefings in Bioinformatics, 15(3), 343–353. 8 Song, K., Ren, J., Zhai, Z., Liu, X., Deng, M., and Sun, F. (2013). Alignment-free sequence comparison based on next-generation sequencing reads Journal of Computational Biology, 20(2), 64–79. 9 Jiang, B.∗, Song, K.∗, Ren, J.∗, Deng, M., Sun, F., and Zhang, X. (2012). Comparison of metagenomic samples using sequence signatures. BMC Genomics, 13(1), 730. 10 Ren, J.∗, Ahlgren, N.∗, Lu, Y., Fuhrman, J., Sun, F. (2017). VirFinder: identifying viral sequences from metagenomic data using sequence signatures. Submitted. * co-first authors. 11 Lu, Y., Tang, K., Ren, J., Fuhrman, J., Waterman, M.S., Sun, F. (2017). CAFE: aCcelerated Alignment-FrEe sequence analysis. Submitted.

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