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Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains

View Article: PubMed Central - PubMed

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.

No MeSH data available.


PCA ordinates of samples in Experiment 5.(a) Two-dimensional PCA plot based on FOMC. (b) Two-dimensional PCA plot based on VLMC.
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f6: PCA ordinates of samples in Experiment 5.(a) Two-dimensional PCA plot based on FOMC. (b) Two-dimensional PCA plot based on VLMC.

Mentions: The two-dimension Principal Component Analysis (PCA) plots based on the optimal results from FOMC and VLMC when k = 8 are shown in Fig. 6a,b, respectively. The PCA plot based on VLMC reflects the gradient information for collection depths and sites as the first and second principal component. We also plotted the PCA figures for k = 7 and 9, shown in Figure S3 in Supplementary Section 4. Although k = 7 and 9 are not the optimal value for VLMC, they still can separate the different depths and collecting sites. In comparison, the PCA ordinates based on FOMC did not show clear separations, and some points are shown as outliers.


Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains
PCA ordinates of samples in Experiment 5.(a) Two-dimensional PCA plot based on FOMC. (b) Two-dimensional PCA plot based on VLMC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120338&req=5

f6: PCA ordinates of samples in Experiment 5.(a) Two-dimensional PCA plot based on FOMC. (b) Two-dimensional PCA plot based on VLMC.
Mentions: The two-dimension Principal Component Analysis (PCA) plots based on the optimal results from FOMC and VLMC when k = 8 are shown in Fig. 6a,b, respectively. The PCA plot based on VLMC reflects the gradient information for collection depths and sites as the first and second principal component. We also plotted the PCA figures for k = 7 and 9, shown in Figure S3 in Supplementary Section 4. Although k = 7 and 9 are not the optimal value for VLMC, they still can separate the different depths and collecting sites. In comparison, the PCA ordinates based on FOMC did not show clear separations, and some points are shown as outliers.

View Article: PubMed Central - PubMed

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.

No MeSH data available.