Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from a single pair of miRNA-mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone. Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related interactions. For example, ProMISe-BRCA network involves miR-155/183/21, which exhibits higher ProMISe coupled with coherently higher miRNA expression and lower target expression; oncomirs miR-221/222 in the ProMISe-THCA network engage with many downregulated target genes. Together, our probabilistic approach of integrating expression and sequence scores establishes a functional link between the aberrant miRNA and mRNA expression, which was previously under-appreciated due to the methodological differences.

RIP-seq has recently been developed to discover genome-wide RNA transcripts that interact with a protein or protein complex. RIP-seq is similar to both RNA-seq and ChIP-seq, but presents unique properties and challenges. Currently, no statistical tool is dedicated to RIP-seq analysis. We developed RIPSeeker (http://www.bioconductor.org/packages/2.12/bioc/html/RIPSeeker.html), a free open-source Bioconductor/R package for de novo RIP peak predictions based on HMM. To demonstrate the utility of the software package, we applied RIPSeeker and six other published programs to three independent RIP-seq datasets and two PAR-CLIP datasets corresponding to six distinct RNA-binding proteins. Based on receiver operating curves, RIPSeeker demonstrates superior sensitivity and specificity in discriminating high-confidence peaks that are consistently agreed on among a majority of the comparison methods, and dominated 9 of the 12 evaluations, averaging 80% area under the curve. The peaks from RIPSeeker are further confirmed based on their significant enrichment for biologically meaningful genomic elements, published sequence motifs and association with canonical transcripts known to interact with the proteins examined. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within a self-contained software package comprehensively addressing issues ranging from post-alignments' processing to visualization and annotation.

With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.

HMCES and yedK were recently identified as sensors of abasic sites in ssDNA. In this study, we present multiple crystal structures captured in the apo-, nonspecific-substrate-binding, specific-substrate-binding, and product-binding states of yedK. In combination with biochemical data, we unveil the molecular basis of AP site sensing in ssDNA by yedK. Our results indicate that yedK has a strong preference for AP site-containing ssDNA over native ssDNA and that the conserved Glu105 residue is important for identifying AP sites in ssDNA. Moreover, our results reveal that a thiazolidine linkage is formed between yedK and AP sites in ssDNA, with the residues that stabilize the thiazolidine linkage important for the formation of DNA-protein crosslinks between yedK and the AP sites. We propose that our findings offer a unique platform to develop yedK and other SRAP domain-containing proteins as tools for detecting abasic sites in vitro and in vivo.

Genome wide association studies (GWAS) provide a powerful approach for uncovering disease-associated variants in human, but fine-mapping the causal variants remains a challenge. This is partly remedied by prioritization of disease-associated variants that overlap GWAS-enriched epigenomic annotations. Here, we introduce a new Bayesian model RiVIERA (Risk Variant Inference using Epigenomic Reference Annotations) for inference of driver variants from summary statistics across multiple traits using hundreds of epigenomic annotations. In simulation, RiVIERA promising power in detecting causal variants and causal annotations, the multi-trait joint inference further improved the detection power. We applied RiVIERA to model the existing GWAS summary statistics of 9 autoimmune diseases and Schizophrenia by jointly harnessing the potential causal enrichments among 848 tissue-specific epigenomics annotations from ENCODE/Roadmap consortium covering 127 cell/tissue types and 8 major epigenomic marks. RiVIERA identified meaningful tissue-specific enrichments for enhancer regions defined by H3K4me1 and H3K27ac for Blood T-Cell specifically in the nine autoimmune diseases and Brain-specific enhancer activities exclusively in Schizophrenia. Moreover, the variants from the 95% credible sets exhibited high conservation and enrichments for GTEx whole-blood eQTLs located within transcription-factor-binding-sites and DNA-hypersensitive-sites. Furthermore, joint modeling the nine immune traits by simultaneously inferring and exploiting the underlying epigenomic correlation between traits further improved the functional enrichments compared to single-trait models.

Differentially methylated regions (DMRs) are stable epigenetic features within or in proximity to imprinted genes. We used this feature to identify candidate human imprinted loci by quantitative DNA methylation analysis. We discovered a unique DMR at the 5'-end of FAM50B at 6p25.2. We determined that sense transcripts originating from the FAM50B locus are expressed from the paternal allele in all human tissues investigated except for ovary, in which expression is biallelic. Furthermore, an antisense transcript, FAM50B-AS, was identified to be monoallelically expressed from the paternal allele in a variety of tissues. Comparative phylogenetic analysis showed that FAM50B orthologs are absent in chicken and platypus, but are present and biallelically expressed in opossum and mouse. These findings indicate that FAM50B originated in Therians after divergence from Prototherians via retrotransposition of a gene on the X chromosome. Moreover, our data are consistent with acquisition of imprinting during Eutherian evolution after divergence of Glires from the Euarchonta mammals. FAM50B expression is deregulated in testicular germ cell tumors, and loss of imprinting occurs frequently in testicular seminomas, suggesting an important role for FAM50B in spermatogenesis and tumorigenesis. These results also underscore the importance of accounting for parental origin in understanding the mechanism of 6p25-related diseases.

XAB2 is a multi-functional protein participating processes including transcription, splicing, DNA repair and mRNA export. Here, we report POLR2A, the largest catalytic subunit of RNA polymerase II, as a major target gene down-regulated after XAB2 depletion. XAB2 depletion led to severe splicing defects of POLR2A with significant intron retention. Such defects resulted in substantial loss of POLR2A at RNA and protein levels, which further impaired global transcription. Treatment of splicing inhibitor madrasin induced similar reduction of POLR2A. Screen using TMT-based quantitative proteomics identified several proteins involved in mRNA surveillance including Dom34 with elevated expression. Inhibition of translation or depletion of Dom34 rescued the expression of POLR2A by stabilizing its mRNA. Immuno-precipitation further confirmed that XAB2 associated with spliceosome components important to POLR2A expression. Domain mapping revealed that TPR motifs 2-4 and 11 of XAB2 were critical for POLR2A expression by interacting with SNW1. Finally, we showed POLR2A mediated cell senescence caused by XAB2 deficiency. Depletion of XAB2 or POLR2A induced cell senescence by up-regulation of p53 and p21, re-expression of POLR2A after XAB2 depletion alleviated cellular senescence. These data together support that XAB2 serves as a guardian of POLR2A expression to ensure global gene expression and antagonize cell senescence.

The protein-DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein-DNA interactions across different DNA binding families.

Tumor-infiltrating immune cells as integral component of the tumor microenvironment are associated with tumor progress, prognosis and responses to immunotherapy. Genetic variants have been demonstrated to impact tumor-infiltrating, underscoring the heritable character of immune landscape. Therefore, identification of immunity quantitative trait loci (immunQTLs), which evaluate the effect of genetic variants on immune cells infiltration, might present a critical step toward fully understanding the contribution of genetic variants in tumor development. Although emerging studies have demonstrated the determinants of germline variants on immune infiltration, no database has yet been developed to systematically analyze immunQTLs across multiple cancer types. Using genotype data from TCGA database and immune cell fractions estimated by CIBERSORT, we developed a computational pipeline to identify immunQTLs in 33 cancer types. A total of 913 immunQTLs across different cancer types were identified. Among them, 5 immunQTLs are associated with patient overall survival. Furthermore, by integrating immunQTLs with GWAS data, we identified 527 immunQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerImmunityQTL (http://www.cancerimmunityqtl-hust.com/) for users to browse, search and download data of interest. This database provides an informative resource to understand the germline determinants of immune infiltration in human cancer and benefit from personalized cancer immunotherapy.