This paper proposes a new approach to taxonomic classification of novel metagenomic sequences. Combining sequence similarity information with co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities, we develop a statistical model to predict taxonomic origins of unknown microorganisms at phylum and class levels. The results demonstrate that OTU co-abundant associations are strongly correlated with taxonomic associations.

It is essential and fundamental to understand life science at the molecular level. Single molecule techniques are able to uncover the mystical insights of the biological systems and quantitatively measure the important parameters, and thus become extensively applied in biology. The unique advantage of fluorescence detection makes single molecule fluorescence spectroscopy (SMFS) especially suitable in this field, and plenty of applications of SMFS to locate and track biomolecules have supplied us tremendous new quantitative knowledge. Here, we summarize the applications in five catalogues to show that SMFS brings the new angle of view and more quantitative thinking to life science.

Many microbes are important symbiotes of human. They form specific microbiota communities, participate in various kinds of biological processes of their host and thus deeply affect human health status. Metagenomic sequencing has been widely used in human microbiota study due to its capacity of studying all genetic materials in an environment as a whole without any extra need of isolation or cultivation of microorganisms. Many efforts have been made by researchers in this area trying to dig out interesting knowledge from various metagenome data. In this review, we go through some prominent studies in the metagenomic area. We summarize them into three categories, constructing taxonomy and gene reference, characterization of microbiome distribution patterns, and detection of microbiome alternations associated with specific human phenotypes or diseases. Some available data resources are also provided. This review can serve as an entrance to this exciting and rapidly developing field for researchers interested in human microbiomes.

Natural selection is the differential reproductive success of individuals due to variation in traits. Positive selection increases the frequency of beneficial alleles and negative selection decreases the frequency of harmful alleles. Natural selection is one of the most important mechanisms for us to understand evolution. A lot of methods have been developed to detect positive selection. However, relatively less attention has been paid to false positives of candidates, mainly due to the confounding effects of demography. By reviewing the advantages and disadvantages of different methods, we suggest that new methods robust with demography should be developed in the future.

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) technology was designed for detecting genome-wide chromatin loops mediated by a specific protein of interest, which has become one of the most important biological methods for understanding 3D genome organization. Here we review five bioinformatics tools which are related to ChIA-PET data analysis and data mining. Meanwhile, we also introduce one interesting computational method which is to predict chromatin loops with ChIP-Seq data. Our intention is to help reader to better understand ChIA-PET experiments and to select the most appropriate bioinformatics tools for their 3D genome research.

One of the primary objectives of functional genomics is to simultaneously identify genetic/epigenetic polymorphisms and genotype at genome-wide in large populations. We discuss here an optimized design for “Reduced-Representation” genomic DNA and bisulfite sequencing strategies for plant/crop species with an attempt to significantly improve the efficiency and reduce the cost of the sequencing experiments. The optimized RAD-seq and RRBS-seq methods confer flexibility in the genome regions to be targeted, effective control of undesirable reads and uniform coverage of target regions. In general, the proposed ideal would deliver a potentially efficient approach for cost-effectively genetic and epigenetic typing large plant/crop populations.