Current Genomics - Volume 18, Issue 4, 2017

Comparative genomics, evolutionary biology, and cancer researches require tools to elucidate the evolutionary trajectories and reconstruct the ancestral genomes. Various methods have been developed to infer the genome content and gene ordering of ancestral genomes by using such genomic structural variants. There are mainly two kinds of computational approaches in the ancestral genome reconstruction study. Distance/event-based approaches employ genome evolutionary models and reconstruct the ancestral genomes that minimize the total distance or events over the edges of the given phylogeny. The homology/adjacency-based approaches search for the conserved gene adjacencies and genome structures, and assemble these regions into ancestral genomes along the internal node of the given phylogeny. We review the principles and algorithms of these approaches that can reconstruct the ancestral genomes on the whole genome level. We talk about their advantages and limitations of these approaches in dealing with various genome datasets, evolutionary events, and reconstruction problems. We also talk about the improvements and developments of these approaches in the subsequent researches. We select four most famous and powerful approaches from both distance/eventbased and homology/adjacency-based categories to analyze and compare their performances in dealing with different kinds of datasets and evolutionary events. Based on our experiment, GASTS has the best performance in solving the problems with equal genome contents that only have genome rearrangement events. PMAG++ achieves the best performance in solving the problems with unequal genome contents that have all possible complicated evolutionary events.

Predicting protein submitochondrial location has been studied for about ten years. A number of methods have been developed. The prediction performances have been improved to an almost perfect level. In this review, we introduce the background of this research topic. We also compare the methods, the performances and the datasets that have been used by these studies. Towards the end, we provide hints for the future directions of this research topic.

Backgrounds: With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings. Results: We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence. Conclusion: Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

The goal of this review article is to provide a conceptual based summary of how heritability estimates for complex traits such as obesity are determined and to explore the future directions of research in the heritability field. The target audience are researchers who use heritability data rather than those conducting heritability studies. The article provides an introduction to key concepts critical to understanding heritability studies including: i) definitions of heritability: broad sense versus narrow sense heritability; ii) how data for heritability studies are collected: twin, adoption, family and population- based studies; and iii) analytical techniques: path analysis, structural equations and mixed or regressive models of complex segregation analysis. For each section, a discussion of how the different definitions and methodologies influence heritability estimates is provided. The general limitations of heritability studies are discussed including the issue of “missing heritability” in which heritability estimates are significantly higher than the variance explained by known genetic variants. Potential causes of missing heritability include restriction of many genetic association studies to single nucleotide polymorphisms, gene by gene interactions, epigenetics, and gene by environment interactions. Innovative strategies of accounting for missing heritability including modeling techniques and improved software are discussed.

The genetic background is unknown for the 50-60% of the HNPCC families, who fulfill the Amsterdam criteria, but do not have a mutation in an MMR gene, and is referred to as FCCTX. This study reviews the clinical, morphological and molecular characteristics of FCCTX, and discusses the molecular genetic methods used to localize new FCCTX genes, along with an overview of the genes and chromosomal areas that possibly relate to FCCTX. FCCTX is a heterogeneous group, mainly comprising cases caused by single high-penetrance genes, or by multiple low-penetrance genes acting together, and sporadic CRC cases. FCCTX differs in clinical, morphological and molecular genetic characteristics compared to LS, including a later age of onset, distal location of tumours in the colon, lower risk of developing extracolonic tumours and a higher adenoma/carcinoma ratio, which indicates a slower progression to CRC. Certain characteristics are shared with sporadic CRC, e.g. similarities in gene expression and a high degree of CIN+, with significanly increased 20q gain in FCCTX. Other molecular characteristics of FCCTX include longer telomere length and hypomethylation of LINE-1, both being a possible explanation for CIN+. Some genes in FCCTX families (RPS20, BMPR1A, SEMA4A) have been identified by using a combination of linkage analysis and sequencing. Sequencing strategies and subsequent bioinformatics are improving fast. Exome sequencing and whole genome sequencing are currently the most promising tools. Finally, the involvement of CNV’s and regulatory sequences are widely unexplored and would be interesting for further investigation in FCCTX.

Background: Recently, identification and functional studies of circular RNAs, a type of non-coding RNAs arising from a ligation of 3’ and 5’ ends of a linear RNA molecule, were conducted in mammalian cells with the development of RNA-seq technology. Method: Since compared with animals, studies on circular RNAs in plants are less thorough, a genome-wide identification of circular RNA candidates in Arabidopsis was conducted with our own developed bioinformatics tool to several existing RNA-seq datasets specifically for non-coding RNAs. Results: A total of 164 circular RNA candidates were identified from RNA-seq data, and 4 circular RNA transcripts, including both exonic and intronic circular RNAs, were experimentally validated. Interestingly, our results show that circular RNA transcripts are enriched in the photosynthesis system for the leaf tissue and correlated to the higher expression levels of their parent genes. Sixteen out of all 40 genes that have circular RNA candidates are related to the photosynthesis system, and out of the total 146 exonic circular RNA candidates, 63 are found in chloroplast.

Background: The Hoary Edge Skipper (Achalarus lyciades) is an eastern North America endemic butterfly from the Eudaminae subfamily of skippers named for an underside whitish patch near the hindwing edge. Its caterpillars feed on legumes, in contrast to Grass skippers (subfamily Hesperiinae) which feed exclusively on monocots. Results: To better understand the evolution and phenotypic diversification of Skippers (family Hesperiidae), we sequenced, assembled and annotated a complete genome draft and transcriptome of a wild-caught specimen of A. lyciades and compared it with the available genome of the Clouded Skipper (Lerema accius) from the Grass skipper subfamily. The genome of A. lyciades is nearly twice the size of L. accius (567 Mbp vs. 298 Mbp), however it encodes a smaller number of proteins (15881 vs. 17411). Gene expansions we identified previously in L. accius apparently did not occur in the genome of A. lyciades. For instance, a family of hypothetical cellulases that diverged from an endochitinase (possibly associated with feeding of L. accius caterpillars on nutrient-poor grasses) is absent in A. lyciades. While L. accius underwent gene expansion in pheromone binding proteins, A. lyciades has more opsins. This difference may be related to the mate recognition mechanisms of the two species: visual cues might be more important for the Eudaminae skippers (which have more variable wing patterns), whereas odor might be more important for Grass skippers (that are hardly distinguishable by their wings). Phylogenetically, A. lyciades is a sister species of L. accius, the only other Hesperiidae with a complete genome. Conclusions: A new reference genome of a dicot-feeding skippers, the first from the Eudaminae subfamily, reveals its larger size and suggests hypotheses about phenotypic traits and differences from monocot-feeding skippers.