Limits...
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.


The clustering trees based on different models for the 90 simulation samples in Experiment 1.(a) The best clustering tree on VLMC. (b) The best clustering tree when using FOMC and lp-norm measures. *Samples are divided into three groups A–C. Each group has 30 samples numbered from 0 to 29.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5120338&req=5

f1: The clustering trees based on different models for the 90 simulation samples in Experiment 1.(a) The best clustering tree on VLMC. (b) The best clustering tree when using FOMC and lp-norm measures. *Samples are divided into three groups A–C. Each group has 30 samples numbered from 0 to 29.

Mentions: The best hierarchical clustering trees with VLMC and FOMC are shown in Fig. 1, and the corresponding triples distances are shown in Table 1. Clear groups of three simulated datasets among samples can be observed for both VLMC and FOMC. The best clustering trees with the smallest triples distance for VLMC and FOMC are both obtained in k = 9 and using dissimilarity measure. From the clustering tree in Fig. 1, it is clear that the tree built based on VLMC is more similar to the true tree than that build based on FOMC. Quantitatively, the smallest triples distance for VLMC and FOMC are 42,973 and 43,043, respectively, where VLMC outperforms FOMC with less misclassification.


Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains
The clustering trees based on different models for the 90 simulation samples in Experiment 1.(a) The best clustering tree on VLMC. (b) The best clustering tree when using FOMC and lp-norm measures. *Samples are divided into three groups A–C. Each group has 30 samples numbered from 0 to 29.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: The clustering trees based on different models for the 90 simulation samples in Experiment 1.(a) The best clustering tree on VLMC. (b) The best clustering tree when using FOMC and lp-norm measures. *Samples are divided into three groups A–C. Each group has 30 samples numbered from 0 to 29.
Mentions: The best hierarchical clustering trees with VLMC and FOMC are shown in Fig. 1, and the corresponding triples distances are shown in Table 1. Clear groups of three simulated datasets among samples can be observed for both VLMC and FOMC. The best clustering trees with the smallest triples distance for VLMC and FOMC are both obtained in k = 9 and using dissimilarity measure. From the clustering tree in Fig. 1, it is clear that the tree built based on VLMC is more similar to the true tree than that build based on FOMC. Quantitatively, the smallest triples distance for VLMC and FOMC are 42,973 and 43,043, respectively, where VLMC outperforms FOMC with less misclassification.

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.