BACKGROUND: With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data. RESULTS: In this paper, we propose a new method TM-MCluster for binning metagenomic reads. First, we represent each metagenomic read as a set of "k-mers" with their frequencies occurring in the read. Then, we employ a probabilistic topic model -- the Latent Dirichlet Allocation (LDA) model to the reads, which generates a number of hidden "topics" such that each read can be represented by a distribution vector of the generated topics. Finally, as in the MCluster method, we apply SKWIC -- a variant of the classical K-means algorithm with automatic feature weighting mechanism to cluster these reads represented by topic distributions. CONCLUSIONS: Experiments show that the new method TM-MCluster outperforms major existing methods, including AbundanceBin, MetaCluster 3.0/5.0 and MCluster. This result indicates that the exploitation of topic modeling can effectively improve the binning performance of metagenomic reads.

Nucleosome positioning plays an essential role in cellular processes by modulating accessibility of DNA to proteins. Many computational models have been developed to predict genome-wide nucleosome positions from DNA sequences. Comparative analysis of predicted and experimental nucleosome positioning maps facilitates understanding the regulatory mechanisms of transcription and DNA replication. Therefore, a comprehensive evaluation of existing computational methods is important and useful for biologists to choose appropriate ones in their research. In this article, we carried out a performance comparison among eight widely used computational methods on four species including yeast, fruitfly, mouse and human. In particular, we compared these methods on different regions of each species such as gene sequences, promoters and 5'UTR exons. The experimental results show that the performances of the two latest versions of the thermodynamic model are relatively steadier than the other four methods. Moreover, these methods are workable on four species, but their performances decrease gradually from yeast to human, indicating that the fundamental mechanism of nucleosome positioning is conserved through the evolution process, but more and more factors participate in the determination of nucleosome positions, which leads to sophisticated regulation mechanisms.

The rapid development of high-throughput technologies enables researchers to sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these sequence reads into different species or taxonomical classes is a crucial step for metagenomic analysis, which is referred to as binning of metagenomic data. Most traditional binning methods rely on known reference genomes for accurate assignment of the sequence reads, therefore cannot classify reads from unknown species without the help of close references. To overcome this drawback, unsupervised learning based approaches have been proposed, which need not any known species' reference genome for help. In this paper, we introduce a novel unsupervised method called MCluster for binning metagenomic sequences. This method uses N-grams to extract sequence features and utilizes automatic feature weighting to improve the performance of the basic K-means clustering algorithm. We evaluate MCluster on a variety of simulated data sets and a real data set, and compare it with three latest binning methods: AbundanceBin, MetaCluster 3.0, and MetaCluster 5.0. Experimental results show that MCluster achieves obviously better overall performance (F-measure) than AbundanceBin and MetaCluster 3.0 on long metagenomic reads (≥800 bp); while compared with MetaCluster 5.0, MCluster obtains a larger sensitivity, and a comparable yet more stable F-measure on short metagenomic reads (<300 bp). This suggests that MCluster can serve as a promising tool for effectively binning metagenomic sequences.