Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large‐scale data sets is important to present‐day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi‐cliques and quasi‐bipartites) of complicated protein–protein interaction networks. Our analyses suggest that these hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classification of known proteins within topological structures. Using this spectral analysis method, 48 quasi‐cliques and six quasi‐bipartites were isolated from a network involving 11 855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.

The behavior of transcriptomes and epigenomes in hybrids of heterotic parents is of fundamental interest. Here, we report highly integrated maps of the epigenome, mRNA, and small RNA transcriptomes of two rice (Oryza sativa) subspecies and their reciprocal hybrids. We found that gene activity was correlated with DNA methylation and both active and repressive histone modifications in transcribed regions. Differential epigenetic modifications correlated with changes in transcript levels among hybrids and parental lines. Distinct patterns in gene expression and epigenetic modifications in reciprocal hybrids were observed. Through analyses of single nucleotide polymorphisms from our sequence data, we observed a high correlation of allelic bias of epigenetic modifications or gene expression in reciprocal hybrids with their differences in the parental lines. The abundance of distinct small RNA size classes differed between the parents, and more small RNAs were downregulated than upregulated in the reciprocal hybrids. Together, our data reveal a comprehensive overview of transcriptional and epigenetic trends in heterotic rice crosses and provide a useful resource for the rice community.

Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we developed a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. Our results highlight that lncRNAs are not a homogenous class of molecules but rather a mixture of multiple functional classes with distinct biological mechanism and/or roles. Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we make available through the slncky Evolution Browser.

Background: Small noncoding RNAs (ncRNAs), including short interfering RNAs (siRNAs) and microRNAs (miRNAs), can silence genes at the transcriptional, post-transcriptional or translational level [1, 2]. Results: Here, we show that microRNA-10a (miR-10a) targets a homologous DNA region in the promoter region of the hoxd4 gene and represses its expression at the transcriptional level. Mutational analysis of the miR-10a sequence revealed that the 3' end of the miRNA sequence is the most critical element for the silencing effect. MicroRNA-10a-induced transcriptional gene inhibition requires the presence of Dicer and Argonautes 1 and 3, and it is related to promoter associated noncoding RNAs. Bisulfite sequencing analysis showed that the reduced hoxd4 expression was accompanied by de novo DNA methylation at the hoxd4 promoter. We further demonstrated that trimethylation of histone 3 lysine 27 (H3K27me3) is involved in the miR-10a-induced hoxd4 transcriptional gene silence. Conclusion: In conclusion, our results demonstrate that miR-10a can regulate human gene expression in a transcriptional manner, and indicate that endogenous small noncoding RNA-induced control of transcription may be a potential system for expressional regulation in human breast cancer cells.

A novel normalization method based on the GC content of probes is developed for two-color tiling arrays. The proposed method, together with robust estimates of the model parameters, is shown to perform superbly on published data sets. A robust algorithm for detecting peak regions is also formulated and shown to perform well compared to other approaches. The tools have been implemented as a stand-alone Java program called MA2C, which can display various plots of statistical analysis for quality control.