Differential expression (DE) analysis is to find the genes whose expression values are significantly different among the groups of samples compared. Gene expression values could be measured by bulk RNA sequencing (RNA-seq) or single-cell RNA sequencing (scRNA-seq), which is emerging recently and could get the expression values of individual cell, while DE analysis methods designed for bulk RNA-seq are still commonly used on scRNA-seq data. We found that, since the characteristics of the two kinds of data are quite different, different DE analysis methods should be carefully chosen with regard to different situations of data when applied to scRNA-seq.

 

The presented communication is based on the assumption that complexity indexing of behavioral patterns provides objective and quantitative measurements of disorders as well as response to drugs and other treatments. The concept itself stems from and relies on mathematical frameworks yielding macroscopic characterizations of these patterns based on their microscopic dynamical properties. The results show that a longer-time use of novel non-disruptive multichannel body attached sensors can provide time-series that can be used to characterize humans and their behavioural patterns.

 

A resistant method for studying individual shape asymmetry in two dimensions is introduced, which uses the geometry of data to estimate the underlying symmetric shape. Unlike the classical least-squares approach, it is shown show that asymmetry is more accurately measured when a resistant method is used instead; this helps symmetry departures to be more easily understood. The percentage of asymmetry accounted for by each landmark can also be computed in the process, providing an objective basis for a comprehensive characterization of asymmetry. Overall, the resistant method turns out to be a useful exploratory tool whenever a non homogeneous deformation of bilateral symmetric structures is possible.

Background: Functional genomics employs dozens of OMICs technologies to explore the functions of DNA, RNA and protein regulators in gene regulation processes. Despite each of these technologies being powerful tools on their own, like the parable of blind men and an elephant, any one single technology has a limited ability to depict the complex regulatory system. Integrative OMICS approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to study gene regulations.

Results: This article reviews current popular OMICs technologies, OMICs data integration strategies, and bioinformatics tools used for multi-dimensional data integration. We highlight the advantages of these methods, particularly in elucidating molecular basis of biological regulatory mechanisms.

Conclusions: To better understand the complexity of biological processes, we need powerful bioinformatics tools to integrate these OMICs data. Integrating multi-dimensional OMICs data will generate novel insights into system-level gene regulations and serves as a foundation for further hypothesis-driven research.

Dozens of OMICs technologies have been developed to dissect the functions of DNA, RNA and protein in gene regulation. Each of these technologies is powerful on their own. However, like the parable of blind men and an elephant, any single technology has a limited ability to depict the whole complex regulatory system. Integrative approaches have emerged and become an important area in biology and medicine. It provides a precise and effective way to elucidate the regulatory mechanisms. This article reviews the popular OMICs technologies, OMICs data integration strategies, and the bioinformatics tools used for multi-dimensional data integration.

 

Genome is always working in the 3D space of the nucleus, and its 3D structure is critical for gene regulation. We review the computational methods that rebuild genome 3D structures from high throughput technologies, such as Hi-C. We also discuss the pros and cons in current methods and possible further directions in the field.

Background: Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way.

Results: In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available.

Conclusions: The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of biomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.

The biomarkers identified based on molecular networks can be of help for accurate diagnosis and prognosis in a more systematic way. In this survey, three types of network based biomarkers are introduced, including node biomarkers, edge biomarkers and network biomarkers. The computational approaches for detecting the network based biomarkers are also introduced.

 

It is important in bioinformatics to correctly understand and interpret the performance, as it is the key to rigorously compare performances of different predictors and to choose the right predictor. We present a comprehensive review to the performance measures in evaluating bioinformatics predictors, especially, the classification predictors.