DFA7, a new method to distinguish between intron-containing and intronless genes

Long Non-Coding RNA Review and Implications in Lung Diseases

Non-coding genes occupy the majority of the human genome and have recently garnered increased attention for their implications in a range of diseases. This review illustrates the current scientific landscape concerning long non-coding RNA biogenesis, regulation, and degradation, as well as their functional roles in lung pathogenesis. LncRNAs share many similar biogenesis and regulatory processes with mRNA, such as capping, polyadenylation, post-transcriptional modifications, and exonuclease degradation. Evidence suggests that these lncRNAs become dysregulated in lung diseases such as Acute Lung Injury, Idiopathic Pulmonary Fibrosis, COPD, Lung Cancer, and Pulmonary Arterial Fiypertension. Some lncRNAs have known functions, but the overwhelming majority requires further research to completely understand.

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites

Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs.

Genes from the TAS1R and TAS2R Families of Taste Receptors: Looking for Signatures of Their Adaptive Role in Human Evolution

Taste perception is crucial in monitoring food intake and, hence, is thought to play a significant role in human evolution. To gain insights into possible adaptive signatures in genes encoding bitter, sweet, and umami taste receptors, we surveyed the available sequence variation data from the 1000 Genomes Project Phase 3 for TAS1R (TAS1R1-3) and TAS2R (TAS2R16 and TAS2R38) families. Our study demonstrated that genes from these two families have experienced contrasting evolutionary histories: While TAS1R1 and TAS1R3 showed worldwide evidence of positive selection, probably correlated with improved umami and sweet perception, the patterns of variation displayed by TAS2R16 and TAS2R38 were more consistent with scenarios of balancing selection that possibly conferred a heterozygous advantage associated with better capacity to perceive a wide range of bitter compounds. In TAS2R16, such adaptive events appear to have occurred restrictively in mainland Africa, whereas the strongest evidence in TAS2R38 was detected in Europe. Despite plausible associations between taste perception and the TAS1R and TAS2R selective signatures, we cannot discount other biological mechanisms as driving the evolutionary trajectories of those TAS1R and TAS2R members, especially given recent findings of taste receptors behaving as the products of pleiotropic genes involved in many functions outside the gustatory system.

Investigation of copy number variation in subjects with major depression based on whole-genome sequencing data

BACKGROUND

Despite recent intensive research using genome-wide association studies, the underlying biological basis of major depressive disorder (MDD) still remains unknown. In contrast to genotyping platforms which identify specific variations, whole-genome sequencing (WGS) allows us to detect all private genetic variations within an individual. So far there have been no studies investigating copy number variations (CNVs) in subjects with MDD using WGS data.

METHODS

We obtained complete WGS paired-end reads data of 15 MDD patients and 10 ethnically matched healthy controls. We performed alignments for the sequencing reads and used GASV package to call CNVs including deletion, inversion, translocation and divergence for those subjects.

RESULTS

Our results show that, in the Mexican-American sample, deletion CNVs were significantly richer in MDD cases than healthy controls on each of 23 chromosomes. However, other types of CNVs failed to reach any significance. In the Australian sample, there was no statistically significant difference of CNVs between MDD cases and controls. Furthermore, we found that the Australian group had significantly more deletion CNVs than the Mexican-American group.

LIMITATIONS

High quality WGS costs limited obtaining larger datasets. The GASV package does not currently support duplication or insertion CNVs.

CONCLUSIONS

To our knowledge this is the first time that CNVs detected by WGS data are used to study major depression. The conclusion that deletion CNVs are significantly richer in MDD cases than healthy controls is consistent with the previous finding about recurrent depressive disorder by genome-wide association analysis of CNVs on a large genotyping microarray data.

Single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data

Major depressive disorder (MDD) is a common psychiatric illness with significant medical and socioeconomic impact. Genetic factors are likely to play important roles in the development of this condition. DNA sequencing technology has the ability to identify all private genetic mutations and provides new channels for studying the biology of MDD. In this proof-of-concept study we proposed a novel concept, single-nucleotide variant proportion (SNVP), to investigate MDD based on whole-genome sequencing (WGS) data. Our SNVP-based approach can be used to test newly found candidate genes as a complement to genome-wide genotyping analysis. Furthermore, we performed cluster analysis for MDD patients and ethnically matched healthy controls, and found that clusters based on SNVP may predict MDD diagnosis. Our results suggest that SNVP may be used as a potential biomarker associated with major depression. Our methodology could be a valuable predictive/diagnostic tool as one can test whether a new subject falls within or close to an existing MDD cluster. Advances in this study design have the potential to personalized treatments and could include the ability to diagnose patients based on their full or part DNA sequencing data.