LARP7-Mediated U6 snRNA Modification Ensures Splicing Fidelity and Spermatogenesis in Mice.

U6 snRNA, as an essential component of the catalytic core of the pre-mRNA processing spliceosome, is heavily modified post-transcriptionally, with 2'-O-methylation being most common. The role of these modifications in pre-mRNA splicing as well as their physiological function in mammals have remained largely unclear. Here we report that the La-related protein LARP7 functions as a critical cofactor for 2'-O-methylation of U6 in mouse male germ cells. Mechanistically, LARP7 promotes U6 loading onto box C/D snoRNP, facilitating U6 2'-O-methylation by box C/D snoRNP. Importantly, ablation of LARP7 in the male germline causes defective U6 2'-O-methylation, massive alterations in pre-mRNA splicing, and spermatogenic failure in mice, which can be rescued by ectopic expression of wild-type LARP7 but not an U6-loading-deficient mutant LARP7. Our data uncover a novel role of LARP7 in regulating U6 2'-O-methylation and demonstrate the functional requirement of such modification for splicing fidelity and spermatogenesis in mice.

tModBase: deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data.

tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.

tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data.

tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.

Rps6ka2 enhances iMSC chondrogenic differentiation to attenuate knee osteoarthritis through articular cartilage regeneration in mice.

Articular cartilage (AC) is most susceptible to degeneration in knee osteoarthritis (OA); however, the existing treatments for OA do not target the core link of the pathogenesis-"decreased tissue cell function activity and extracellular matrix (ECM) metabolic disorders" for effective intervention. iMSC hold lower heterogeneity and great promise in biological research and clinical applications. Rps6ka2 may play an important role in the iMSC to treat OA. In this study, the CRISPR/Cas9 gene editing Rps6ka2-/- iMSC were obtained. Effect of Rps6ka2 on iMSC proliferation and chondrogenic differentiation was evaluated in vitro. An OA model was constructed in mice by surgical destabilization of medial meniscus (DMM). The Rps6ka2-/- iMSC and iMSC were injected into the articular cavity twice-weekly for 8 weeks. In vitro experiments showed that Rps6ka2 could promote iMSC proliferation and chondrogenic differentiation. In vivo results further confirmed that Rps6ka2 could improve iMSC viability to promote ECM production to attenuate OA in mice.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells.

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

[Mechanism of Cistanches Herba in treatment of cancer-related fatigue based on network pharmacology and experimental verification].

This study aimed to explore the mechanism of Cistanches Herba in the treatment of cancer-induced fatigue(CRF) by network pharmacology combined with in vivo and in vitro experiments to provide a theoretical basis for the clinical medication. The chemical constituents and targets of Cistanches Herba were searched from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The targets of CRF were screened out by GeneCards and NCBI. The common targets of traditional Chinese medicine and disease were selected to construct a protein-protein interaction(PPI) network, followed by Gene Ontology(GO) functional and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses. A visual signal pathway rela-ted to Chinese medicine and disease targets was constructed. The CRF model was induced by paclitaxel(PTX) in mice. Mice were divided into a control group, a PTX model group, and low-and high-dose Cistanches Herba extract groups(250 and 500 mg·kg~(-1)). The anti-CRF effect in mice was evaluated by open field test, tail suspension test, and exhaustive swimming time, and the pathological morphology of skeletal muscle was evaluated by hematoxylin-eosin(HE) staining. The cancer cachexia model in C2C12 muscle cells was induced by C26 co-culture, and the cells were divided into a control group, a conditioned medium model group, and low-, medium-, and high-dose Cistanches Herba extract groups(62.5, 125, and 250 µg·mL~(-1)). The reactive oxygen species(ROS) content in each group was detected by flow cytometry, and the intracellular mitochondrial status was evaluated by transmission electron microscopy. The protein expression levels of hypoxia-inducible factor-1α(HIF-1α), BNIP3L, and Beclin-1 were detected by Western blot. Six effective constituents were screened out from Cistanches Herba. The core genes of Cistanches Herba in treating CRF were AKT1, IL-6, VEGFA, CASP3, JUN, EGFR, MYC, EGF, MAPK1, PTGS2, MMP9, IL-1B, FOS, and IL10, and the pathways related to CRF were AGE-RAGE and HIF-1α. Through GO enrichment analysis, it was found that the main biological functions involved were lipid peroxidation, nutrient deficiency, chemical stress, oxidative stress, oxygen content, and other biological processes. The results of the in vivo experiment showed that Cistanches Herba extract could significantly improve skeletal muscle atrophy in mice to relieve CRF. The in vitro experiment showed that Cistanches Herba extract could significantly reduce the content of intracellular ROS, the percentage of mitochondrial fragmentation, and the protein expression of Beclin-1 and increase the number of autophagosomes and the protein expression of HIF-1α and BNIP3L. Cistanches Herba showed a good anti-CRF effect, and its mechanism may be related to the key target proteins in the HIF-1α signaling pathway.

The cardiac translational landscape reveals that micropeptides are new players involved in cardiomyocyte hypertrophy.

Hypertrophic growth of cardiomyocytes is one of the major compensatory responses in the heart after physiological or pathological stimulation. Protein synthesis enhancement, which is mediated by the translation of messenger RNAs, is one of the main features of cardiomyocyte hypertrophy. Although the transcriptome shift caused by cardiac hypertrophy induced by different stimuli has been extensively investigated, translatome dynamics in this cellular process has been less studied. Here, we generated a nucleotide-resolution translatome as well as transcriptome data from isolated primary cardiomyocytes undergoing hypertrophy. More than 10,000 open reading frames (ORFs) were detected from the deep sequencing of ribosome-protected fragments (Ribo-seq), which orchestrated the shift of the translatome in hypertrophied cardiomyocytes. Our data suggest that rather than increase the translational rate of ribosomes, the increased efficiency of protein synthesis in cardiomyocyte hypertrophy was attributable to an increased quantity of ribosomes. In addition, more than 100 uncharacterized short ORFs (sORFs) were detected in long noncoding RNA genes from Ribo-seq with potential of micropeptide coding. In a random test of 15 candidates, the coding potential of 11 sORFs was experimentally supported. Three micropeptides were identified to regulate cardiomyocyte hypertrophy by modulating the activities of oxidative phosphorylation, the calcium signaling pathway, and the mitogen-activated protein kinase (MAPK) pathway. Our study provides a genome-wide overview of the translational controls behind cardiomyocyte hypertrophy and demonstrates an unrecognized role of micropeptides in cardiomyocyte biology.

RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.

More than 100 distinct chemical modifications to RNA have been characterized so far. However, the prevalence, mechanisms and functions of various RNA modifications remain largely unknown. To provide transcriptome-wide landscapes of RNA modifications, we developed the RMBase v2.0 (http://rna.sysu.edu.cn/rmbase/), which is a comprehensive database that integrates epitranscriptome sequencing data for the exploration of post-transcriptional modifications of RNAs and their relationships with miRNA binding events, disease-related single-nucleotide polymorphisms (SNPs) and RNA-binding proteins (RBPs). RMBase v2.0 was expanded with ∼600 datasets and ∼1 397 000 modification sites from 47 studies among 13 species, which represents an approximately 10-fold expansion when compared with the previous release. It contains ∼1 373 000 N6-methyladenosines (m6A), ∼5400 N1-methyladenosines (m1A), ∼9600 pseudouridine (Ψ) modifications, ∼1000 5-methylcytosine (m5C) modifications, ∼5100 2'-O-methylations (2'-O-Me), and ∼2800 modifications of other modification types. Moreover, we built a new module called 'Motif' that provides the visualized logos and position weight matrices (PWMs) of the modification motifs. We also constructed a novel module termed 'modRBP' to study the relationships between RNA modifications and RBPs. Additionally, we developed a novel web-based tool named 'modMetagene' to plot the metagenes of RNA modification along a transcript model. This database will help researchers investigate the potential functions and mechanisms of RNA modifications.

dreamBase: DNA modification, RNA regulation and protein binding of expressed pseudogenes in human health and disease.

Although thousands of pseudogenes have been annotated in the human genome, their transcriptional regulation, expression profiles and functional mechanisms are largely unknown. In this study, we developed dreamBase (http://rna.sysu.edu.cn/dreamBase) to facilitate the investigation of DNA modification, RNA regulation and protein binding of potential expressed pseudogenes from multidimensional high-throughput sequencing data. Based on ∼5500 ChIP-seq and DNase-seq datasets, we identified genome-wide binding profiles of various transcription-associated factors around pseudogene loci. By integrating ∼18 000 RNA-seq data, we analysed the expression profiles of pseudogenes and explored their co-expression patterns with their parent genes in 32 cancers and 31 normal tissues. By combining microRNA binding sites, we demonstrated complex post-transcriptional regulation networks involving 275 microRNAs and 1201 pseudogenes. We generated ceRNA networks to illustrate the crosstalk between pseudogenes and their parent genes through competitive binding of microRNAs. In addition, we studied transcriptome-wide interactions between RNA binding proteins (RBPs) and pseudogenes based on 458 CLIP-seq datasets. In conjunction with epitranscriptome sequencing data, we also mapped 1039 RNA modification sites onto 635 pseudogenes. This database will provide insights into the transcriptional regulation, expression, functions and mechanisms of pseudogenes as well as their roles in biological processes and diseases.

Spatial-temporal transcriptional dynamics of long non-coding RNAs in human brain.

The functional architecture of the human brain is greatly determined by the temporal and spatial regulation of the transcription process. However, the spatial and temporal transcriptional landscape of long non-coding RNAs (lncRNAs) during human brain development remains poorly understood. Here, we report the genome-wide lncRNA transcriptional analysis in an extensive series of 1340 post-mortem human brain specimens collected from 16 regions spanning the period from early embryo development to late adulthood. We discovered that lncRNA transcriptome dramatically changed during fetal development, while transited to a surprisingly relatively stable state after birth till the late adulthood. We also discovered that the transcription map of lncRNAs was spatially different, and that this spatial difference was developmentally regulated. Of the 16 brain regions explored (cerebellar cortex, thalamus, striatum, amygdala, hippocampus and 11 neocortex areas), cerebellar cortex showed the most distinct lncRNA expression features from all remaining brain regions throughout the whole developmental period, reflecting its unique developmental and functional features. Furthermore, by characterizing the functional modules and cellular processes of the spatial-temporal dynamic lncRNAs, we found that they were significantly associated with the RNA processing, neuron differentiation and synaptic signal transportation processes. Furthermore, we found that many lncRNAs associated with the neurodegenerative Alzheimer and Parkinson diseases were co-expressed in the fetal development of the human brain, and affected the convergent biological processes. In summary, our study provides a comprehensive map for lncRNA transcription dynamics in human brain development, which might shed light on the understanding of the molecular underpinnings of human brain function and disease.

ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs.

RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data.

Although more than 100 different types of RNA modifications have been characterized across all living organisms, surprisingly little is known about the modified positions and their functions. Recently, various high-throughput modification sequencing methods have been developed to identify diverse post-transcriptional modifications of RNA molecules. In this study, we developed a novel resource, RMBase (RNA Modification Base, http://mirlab.sysu.edu.cn/rmbase/), to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies. The current release of RMBase includes ∼ 9500 pseudouridine (Ψ) modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼ 1000 5-methylcytosines (m(5)C) predicted from Aza-IP data, ∼ 124 200 N6-Methyladenosine (m(6)A) modifications discovered from m(6)A-seq and ∼ 1210 2'-O-methylations (2'-O-Me) identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. It provides web interfaces to show thousands of relationships between RNA modification sites and microRNA target sites. It can also be used to illustrate the disease-related SNPs residing in the modification sites/regions. RMBase provides a genome browser and a web-based modTool to query, annotate and visualize various RNA modifications. This database will help expand our understanding of potential functions of RNA modifications.

deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions.

tRF2Cancer: A web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers.

tRNA-derived small RNA fragments (tRFs) are one class of small non-coding RNAs derived from transfer RNAs (tRNAs). tRFs play important roles in cellular processes and are involved in multiple cancers. High-throughput small RNA (sRNA) sequencing experiments can detect all the cellular expressed sRNAs, including tRFs. However, distinguishing genuine tRFs from RNA fragments generated by random degradation remains a major challenge. In this study, we developed an integrated web-based computing system, tRF2Cancer, to accurately identify tRFs from sRNA deep-sequencing data and evaluate their expression in multiple cancers. The binomial test was introduced to evaluate whether reads from a small RNA-seq data set represent tRFs or degraded fragments. A classification method was then used to annotate the types of tRFs based on their sites of origin in pre-tRNA or mature tRNA. We applied the pipeline to analyze 10 991 data sets from 32 types of cancers and identified thousands of expressed tRFs. A tool called 'tRFinCancer' was developed to facilitate the users to inspect the expression of tRFs across different types of cancers. Another tool called 'tRFBrowser' shows both the sites of origin and the distribution of chemical modification sites in tRFs on their source tRNA. The tRF2Cancer web server is available at http://rna.sysu.edu.cn/tRFfinder/.

StarScan: a web server for scanning small RNA targets from degradome sequencing data.

Endogenous small non-coding RNAs (sRNAs), including microRNAs, PIWI-interacting RNAs and small interfering RNAs, play important gene regulatory roles in animals and plants by pairing to the protein-coding and non-coding transcripts. However, computationally assigning these various sRNAs to their regulatory target genes remains technically challenging. Recently, a high-throughput degradome sequencing method was applied to identify biologically relevant sRNA cleavage sites. In this study, an integrated web-based tool, StarScan (sRNA target Scan), was developed for scanning sRNA targets using degradome sequencing data from 20 species. Given a sRNA sequence from plants or animals, our web server performs an ultrafast and exhaustive search for potential sRNA-target interactions in annotated and unannotated genomic regions. The interactions between small RNAs and target transcripts were further evaluated using a novel tool, alignScore. A novel tool, degradomeBinomTest, was developed to quantify the abundance of degradome fragments located at the 9-11th nucleotide from the sRNA 5' end. This is the first web server for discovering potential sRNA-mediated RNA cleavage events in plants and animals, which affords mechanistic insights into the regulatory roles of sRNAs. The StarScan web server is available at http://mirlab.sysu.edu.cn/starscan/.

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data.

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

[Application of Bioinformatics in Long Non-coding RNAs].

Long non-coding RNAs (lncRNAs)are non-coding RNA molecules larger than 200 nt.They play a key regulatory role in crucial biological processes.Recently,lncRNA researches have been devel-oped rapidly and a set of bioinformatic tools and databases about lncRNA identification,quantification, structural analysis and function prediction have been emerged.This review introduces the resources for lncRNA studies.

Comparative transcriptome analysis of small noncoding RNAs in different stages of Trypanosoma brucei.

Trypanosoma brucei, a pathogen of human and domestic animals, is an early evolved parasitic protozoan with a complex life cycle. Most genes of this parasite are post-transcriptionally regulated. However, the mechanisms and the molecules involved remain largely unknown. We have deep-sequenced the small RNAs of two life stages of this parasite--the bloodstream form and the procyclic form. Our results show that the small RNAs of T. brucei could derive from multiple sources, including NATs (natural antisense transcripts), tRNAs, and rRNAs. Most of these small RNAs in the two stages were found to share uniform characteristics. However, our results demonstrate that their variety and expression show significant differences between different stages, indicating possible functional differentiation. Dicer-knockdown evidence further proved that some of the small interfering RNAs (siRNAs) could regulate the expression of genes. Based on the genome-wide analysis of the small RNAs in the two stages of T. brucei, our results not only provide evidence to study their differentiation but also shed light on questions regarding the origins and evolution of small RNA-based mechanisms in early eukaryotes.

Association between the TLR2 Arg753Gln polymorphism and the risk of sepsis: a meta-analysis.

INTRODUCTION: Recently, researchers in a number of studies have explored the association between the Toll-like receptor 2 (TLR2) Arg753Gln polymorphism and sepsis risk. However, the results were conflicting. In this meta-analysis, we aimed to confirm the effect of the TLR2 Arg753Gln polymorphism on sepsis risk. METHODS: Relevant records up to 1 June 2015 were retrieved from the PubMed, Embase, and Web of Knowledge databases. The odds ratios with their corresponding 95 % confidence intervals were used to assess the association between the TLR2 Arg753Gln polymorphism and sepsis risk. The selection of a fixed or random effects model was made according to a heterogeneity test in total and subgroup analyses. Sensitivity analysis and publication bias test were performed to ensure the reliability of our results. RESULTS: A total of 12 studies with aggregate totals of 898 cases and 1517 controls met our inclusion criteria for meta-analysis. There were significant associations between the TLR2 Arg753Gln polymorphism and sepsis risk in overall analyses under two genetic models (the allele comparison and the dominant model). In addition, subgroup analyses based on age group, ethnicity, sepsis type, and source of control also showed a significant effect of the TLR2 Arg753Gln polymorphism on sepsis risk. CONCLUSIONS: Our present meta-analysis supports a direct effect of the TLR2 Arg753Gln polymorphism on sepsis risk, especially in Europeans. The TLR2 Arg753Gln polymorphism might be used as a relevant risk estimate for the development of sepsis. Studies with larger sample sizes and homogeneous groups of patients with sepsis are required for further analysis.

ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) represent two classes of important non-coding RNAs in eukaryotes. Although these non-coding RNAs have been implicated in organismal development and in various human diseases, surprisingly little is known about their transcriptional regulation. Recent advances in chromatin immunoprecipitation with next-generation DNA sequencing (ChIP-Seq) have provided methods of detecting transcription factor binding sites (TFBSs) with unprecedented sensitivity. In this study, we describe ChIPBase (http://deepbase.sysu.edu.cn/chipbase/), a novel database that we have developed to facilitate the comprehensive annotation and discovery of transcription factor binding maps and transcriptional regulatory relationships of lncRNAs and miRNAs from ChIP-Seq data. The current release of ChIPBase includes high-throughput sequencing data that were generated by 543 ChIP-Seq experiments in diverse tissues and cell lines from six organisms. By analysing millions of TFBSs, we identified tens of thousands of TF-lncRNA and TF-miRNA regulatory relationships. Furthermore, two web-based servers were developed to annotate and discover transcriptional regulatory relationships of lncRNAs and miRNAs from ChIP-Seq data. In addition, we developed two genome browsers, deepView and genomeView, to provide integrated views of multidimensional data. Moreover, our web implementation supports diverse query types and the exploration of TFs, lncRNAs, miRNAs, gene ontologies and pathways.