title: Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains.
abstract: The comparison between microbial sequencing data is critical to understand the dynamics of microbial communities. The alignment-based tools analyzing metagenomic datasets require reference sequences and read alignments. The available alignment-free dissimilarity approaches model the background sequences with Fixed Order Markov Chain (FOMC) yielding promising results for the comparison of microbial communities. However, in FOMC, the number of parameters grows exponentially with the increase of the order of Markov Chain (MC). Under a fixed high order of MC, the parameters might not be accurately estimated owing to the limitation of sequencing depth. In our study, we investigate an alternative to FOMC to model background sequences with the data-driven Variable Length Markov Chain (VLMC) in metatranscriptomic data. The VLMC originally designed for long sequences was extended to apply to high-throughput sequencing reads and the strategies to estimate the corresponding parameters were developed. The flexible number of parameters in VLMC avoids estimating the vast number of parameters of high-order MC under limited sequencing depth. Different from the manual selection in FOMC, VLMC determines the MC order adaptively. Several beta diversity measures based on VLMC were applied to compare the bacterial RNA-Seq and metatranscriptomic datasets. Experiments show that VLMC outperforms FOMC to model the background sequences in transcriptomic and metatranscriptomic samples. A software pipeline is available at https://d2vlmc.codeplex.com.
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.
authors.forename: Weinan
authors.initials: W
authors.lastname: Liao
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, CA 90089 USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Ren
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.
authors.forename: Kun
authors.initials: K
authors.lastname: Wang
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.
authors.forename: Shun
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.
authors.forename: Feng
authors.initials: F
authors.lastname: Zeng
authors.affiliation: Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Molecular and Computational Biology Program, University of Southern California, Los Angeles, California, CA 90089 USA.|Center for Computational Systems Biology, Fudan University, Shanghai 200433, China.
authors.forename: Fengzhu
authors.initials: F
authors.lastname: Sun
mesh_terms: D001483:Base Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D012984:Software; D059467:Transcriptome