N6-methyladenosine (m6A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m6A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m6A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m6A site named SRAMP is established. To depict the sequence context around m6A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/.

Comprehensive databases of microRNA–disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA–disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA–disease associations, HMDD database will keep on growing in the future.

Prunus mume (mei), which was domesticated in China more than 3,000 years ago as ornamental plant and fruit, is one of the first genomes among Prunus subfamilies of Rosaceae been sequenced. Here, we assemble a 280M genome by combining 101-fold next-generation sequencing and optical mapping data. We further anchor 83.9% of scaffolds to eight chromosomes with genetic map constructed by restriction-site-associated DNA sequencing. Combining P. mume genome with available data, we succeed in reconstructing nine ancestral chromosomes of Rosaceae family, as well as depicting chromosome fusion, fission and duplication history in three major subfamilies. We sequence the transcriptome of various tissues and perform genome-wide analysis to reveal the characteristics of P. mume, including its regulation of early blooming in endodormancy, immune response against bacterial infection and biosynthesis of flower scent. The P. mume genome sequence adds to our understanding of Rosaceae evolution and provides important data for improvement of fruit trees.

The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play vital roles in various biological processes. It has been reported that aberrant regulation of miRNAs was associated with the development and progression of various diseases, but the underlying mechanisms are not fully deciphered. Here, we described our updated TransmiR v2.0 database for more comprehensive information about transcription factor (TF)-miRNA regulations. 3730 TF–miRNA regulations among 19 species from 1349 reports were manually curated by surveying >8000 publications, and more than 1.7 million tissue-specific TF–miRNA regulations were further incorporated based on ChIP-seq data. Besides, we constructed a ‘Predict’ module to query the predicted TF–miRNA regulations in human based on binding motifs of TFs. To facilitate the community, we provided a ‘Network’ module to visualize TF–miRNA regulations for each TF and miRNA, or for a specific disease. An ‘Enrichment analysis’ module was also included to predict TFs that are likely to regulate a miRNA list of interest. In conclusion, with improved data coverage and webserver functionalities, TransmiR v2.0 would be a useful resource for investigating the regulation of miRNAs. TransmiR v2.0 is freely accessible at http://www.cuilab.cn/transmir.

Galectin-1 is implicated in making tumor cells immune privileged, in part by regulating the survival of infiltrating T cells. Galectin-1 is strongly expressed in activated pancreatic stellate cells (PSCs); however, whether this is linked to tumor cell immune escape in pancreatic cancer is unknown. Galectin-1 was knocked down in PSCs isolated from pancreatic tissues using small interfering RNA (siRNA), or overexpressed using recombinant lentiviruses, and the PSCs were cocultured with T cells. CD31 , CD4 1 and CD8 1 T cell apoptosis was detected by flow cytometry; T cell IL-2, IL-4, IL-5 and INF-c production levels were quantified using ELISA. Immunohistochemical analysis showed increased numbers of PSCs expressed Galectin-1 (p < 0.01) and pancreatic cancers had increased CD3 1 T cell densities (p < 0.01) compared to normal pancreas or chronic pancreatitis samples. In coculture experiments, PSCs that overexpressed Galectin-1 induced apoptosis of CD4 1 T cells (p < 0.01) and 05). Supernatants from T cells cocultured with PSCs that overexpressedGalectin-1 contained significantly increased levels of Th2 cytokines (IL-4 and IL-5, p < 0.01) and decreased Th1 cytokines (IL-2 and INF-c, p < 0.01). However, the knockdown of PSC Galectin-1 had the opposite effect on Th1 and Th2 cytokine secretion. Our study suggests that the overexpression of Galectin-1 in PSCs induced T cell apoptosis and Th2 cytokine secretion, which may regulate PSC-dependent immunoprivilege in the pancreatic cancer microenvironment. Galectin-1 may provide a novel candidate target for pancreatic cancer immunotherapy.Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, which is resistant to currently available systemic therapies. PDAC has one of the worst prognoses of all human cancers with incidence rates nearly equal to mortality rates. 1 There is evidence that excessive desmoplasia play a crucial role in the aggressive behavior of pancreatic cancer, 2 which impedes effective systemic treatments on a molecular level. Pancreatic stellate cells (PSCs) are stellate-shaped mesenchymal pancreatic cells, which have been identified as important regulators of desmoplasia in PDAC. 3In their quiescent state, PSCs can be identified by the presence of vitamin A-containing lipid droplets in the cytoplasm and the expression of desmin and glial-fibrillary-acidic protein (GFAP). 4 In response to pancreatic damage or stress, PSC are transformed into an activated myofibroblast-like phenotype. Activated PSCs express a-smooth muscle actin (a-SMA), and synthesize excessive amounts of ECM proteins, including Collagen I and III, fibronectin and matrix-degrading enzymes such as MMPs.5-7 Activated PSCs have a variety of cell functions and can promote the proliferation, migration, invasion and metastasis of pancreatic cancer cells; 1,8,9 however, the factors that PSCs secrete to advance pancreatic cancer progression remain largely unknown.Galectin-1, a member of the galectin family of b-galactosidebinding proteins, is a homodimer of 14-kDa subunits pos...

Lysine succinylation is an emerging protein post-translational modification, which plays an important role in regulating the cellular processes in both eukaryotic and prokaryotic cells. However, the succinylation modification site is particularly difficult to detect because the experimental technologies used are often time-consuming and costly. Thus, an accurate computational method for predicting succinylation sites may help researchers towards designing their experiments and to understand the molecular mechanism of succinylation. In this study, a novel computational tool termed SuccinSite has been developed to predict protein succinylation sites by incorporating three sequence encodings, i.e., k-spaced amino acid pairs, binary and amino acid index properties. Then, the random forest classifier was trained with these encodings to build the predictor. The SuccinSite predictor achieves an AUC score of 0.802 in the 5-fold cross-validation set and performs significantly better than existing predictors on a comprehensive independent test set. Furthermore, informative features and predominant rules (i.e. feature combinations) were extracted from the trained random forest model for an improved interpretation of the predictor. Finally, we also compiled a database covering 4411 experimentally verified succinylation proteins with 12 456 lysine succinylation sites. Taken together, these results suggest that SuccinSite would be a helpful computational resource for succinylation sites prediction. The web-server, datasets, source code and database are freely available at http://systbio.cau.edu.cn/SuccinSite/.

With the rapid accumulation of high-throughput microRNA (miRNA) expression profile, the up-to-date resource for analyzing the functional and disease associations of miRNAs is increasingly demanded. We here describe the updated server TAM 2.0 for miRNA set enrichment analysis. Through manual curation of over 9000 papers, a more than two-fold growth of reference miRNA sets has been achieved in comparison with previous TAM, which covers 9945 and 1584 newly collected miRNA-disease and miRNA-function associations, respectively. Moreover, TAM 2.0 allows users not only to test the functional and disease annotations of miRNAs by overrepresentation analysis, but also to compare the input de-regulated miRNAs with those de-regulated in other disease conditions via correlation analysis. Finally, the functions for miRNA set query and result visualization are also enabled in the TAM 2.0 server to facilitate the community. The TAM 2.0 web server is freely accessible at http://www.scse.hebut.edu.cn/tam/ or http://www.lirmed.com/tam2/.