Current Genomics - Volume 15, Issue 2, 2014

In theoretical physics, there exist two basic mathematical approaches, algebraic and geometrical methods, which, in most cases, are complementary. In the area of genome sequence analysis, however, algebraic approaches have been widely used, while geometrical approaches have been less explored for a long time. The Z-curve theory is a geometrical approach to genome analysis. The Z-curve is a three-dimensional curve that represents a given DNA sequence in the sense that each can be uniquely reconstructed given the other. The Z-curve, therefore, contains all the information that the corresponding DNA sequence carries. The analysis of a DNA sequence can then be performed through studying the corresponding Z-curve. The Z-curve method has found applications in a wide range of areas in the past two decades, including the identifications of protein-coding genes, replication origins, horizontally-transferred genomic islands, promoters, translational start sides and isochores, as well as studies on phylogenetics, genome visualization and comparative genomics. Here, we review the progress of Z-curve studies from aspects of both theory and applications in genome analysis.

Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Zcurve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation.

Precise DNA replication is critical for the maintenance of genetic integrity in all organisms. In all three domains of life, DNA replication starts at a specialized locus, termed as the replication origin, oriC or ORI, and its identification is vital to understanding the complex replication process. In bacteria and eukaryotes, replication initiates from single and multiple origins, respectively, while archaea can adopt either of the two modes. The Z-curve method has been successfully used to identify replication origins in genomes of various species, including multiple oriCs in some archaea. Based on the Z-curve method and comparative genomics analysis, we have developed a web-based system, Ori-Finder, for finding oriCs in bacterial genomes with high accuracy. Predicted oriC regions in bacterial genomes are organized into an online database, DoriC. Recently, archaeal oriC regions identified by both in vivo and in silico methods have also been included in the database. Here, we summarize the recent advances of in silico prediction of oriCs in bacterial and archaeal genomes using the Z-curve based method.

The nucleotide composition of genomes undergoes dramatic variations among all three kingdoms of life. GC content, an important characteristic for a genome, is related to many important functions, and therefore GC content and its distribution are routinely reported for sequenced genomes. Traditionally, GC content distribution is assessed by computing GC contents in windows that slide along the genome. Disadvantages of this routinely used window-based method include low resolution and low sensitivity. Additionally, different window sizes result in different GC content distribution patterns within the same genome. We proposed a windowless method, the GC profile, for displaying GC content variations across the genome. Compared to the window-based method, the GC profile has the following advantages: 1) higher sensitivity, because of variation-amplifying procedures; 2) higher resolution, because boundaries between domains can be determined at one single base pair; 3) uniqueness, because the GC profile is unique for a given genome and 4) the capacity to show both global and regional GC content distributions. These characteristics are useful in identifying horizontallytransferred genomic islands and homogenous GC-content domains. Here, we review the applications of the GC profile in identifying genomic islands and genome segmentation points, and in serving as a platform to integrate with other algorithms for genome analysis. A web server generating GC profiles and implementing relevant genome segmentation algorithms is available at: www.zcurve.net.

Protein folding and clearance networks sense and respond to misfolded and aggregation-prone proteins by activating cytoprotective cell stress responses that safeguard the proteome against damage, maintain the health of the cell, and enhance lifespan. Surprisingly, cellular proteostasis undergoes a sudden and widespread failure early in Caenorhabditis elegans adulthood, with marked consequences on proteostasis functions later in life. These changes in the regulation of quality control systems, such as chaperones, the ubiquitin proteasome system and cellular stress responses, are controlled cell-nonautonomously by the proliferation of germline stem cells. Here, we review recent studies examining changes in proteostasis upon transition to adulthood and how proteostasis is modulated by reproduction onset, focusing on C. elegans. Based on these and our own findings, we propose that the regulation of quality control systems is actively remodeled at the point of transition between development and adulthood to influence the subsequent course of aging.

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.