Background: A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge.

Methods: In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations.

Results: Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels.

Conclusions: With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity.

Availability and Implementation: Source codes of TACO are freely available at the following URL: https://github.com/baharvand/OTU-Taxonomy-Identification implemented in C++, supported on Linux and MS Windows.

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.

Single molecule techniques emerge as powerful and quantitative approaches for scientific investigations in last decades. Among them, single molecule fluorescence spectroscopy (SMFS) is able to non-invasively characterize and track samples at the molecular level. Here, applications of SMFS to fundamental biological questions have been briefly summarized in catalogues of single-molecule counting, distance measurements, force sensors, molecular tracking, and ultrafast dynamics. In these SMFS applications, statistics and physical laws are utilized to quantitatively analyze the behaviors of biomolecules in cellular signaling pathways and the mechanisms of biological functions. This not only deepens our understanding of bio-systems, but also provides a fresh angle to those fundamental questions, leading to a more quantitative thinking in life science.

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.

Human symbiotic microbes are our important “tiny friends”. They form microbiota communities, participate in multiple types of our biological processes and thus deeply affect our health status. The importance and intricacy of human microbiota studies have attracted extensive attentions. Metagenomic sequencing is one of the most widely used strategies in human microbiota study. Here, we gather the scattered achievements in metagenomic area and comb through their valuable ideas and resources from our perspective. We hope this review can serve as an entrance to this exciting and rapidly developing field for researchers interested in human microbiomes.

There is increasing interest in studying the molecular mechanisms of recent adaptations caused by positive selection in the genomics era. Such endeavors to detect recent positive selection, however, have been severely handicapped by false positives due to the confounding impact of demography and the population structure. To reduce false positives, it is critical to conduct a functional analysis to identify the true candidate genes/mutations from those that are filtered through neutrality tests. However, the extremely high cost of such functional analysis may restrict studies within a small number of model species. In particular, when the false positive rate of neutrality tests is high, the efficiency of the functional analysis will also be very low. Therefore, although the recent improvements have been made in the (joint) inference of demography and selection, our ultimate goal, which is to understand the mechanism of adaptation generally in a wide variety of natural populations, may not be achieved using the currently available approaches. More attention should thus be spent on the development of more reliable tests that could not only free themselves from the confounding impact of demography and the population structure but also have reasonable power to detect selection.

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.

Genome-wide chromatin interaction analysis has become important for understanding 3D topological structure of a genome as well as for linking distal cis-regulatory elements to their target genes. Compared to the Hi-C method, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is unique, in that one can interrogate thousands of chromatin interactions (in a genome) mediated by a specific protein of interest at high resolution and reasonable cost. However, because of the noisy nature of the data, efficient analytical tools have become necessary. Here, we review some new computational methods recently developed by us and compare them with other existing methods. Our intention is to help readers to better understand ChIA-PET results and to guide the users on selection of the most appropriate tools for their own projects.

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.

The next generation sequencing enables generation of high resolution and high throughput data for structure sequence of any genome at a fast declining cost. This opens opportunity for population based genetic and genomic analyses. In many applications, whole genome sequencing or re-sequencing is unnecessary or prohibited by budget limits. The Reduced Representation Genome Sequencing (RRGS), which sequences only a small proportion of the genome of interest, has been proposed to deal with the situations. Several forms of RRGS are proposed and implemented in the literature. When applied to plant or crop species, the current RRGS protocols shared a key drawback that a significantly high proportion (up to 60%) of sequence reads to be generated may be of non-genomic origin but attributed to chloroplast DNA or rRNA genes, leaving an exceptional low efficiency of the sequencing experiment. We recommended and discussed here the design of optimized simplified genomic DNA and bisulfite sequencing strategies, which may greatly improves efficiency of the sequencing experiments by bringing down the presentation of the undesirable sequencing reads to less than 10% in the whole sequence reads. The optimized RAD-seq and RRBS-seq methods are potentially useful for sequence variant screening and genotyping in large plant/crop populations.

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.