Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas.

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine).

Antibiotic resistance genes (ARGs) have moved from the environmental resistome into human commensals and pathogens, driven by human selection with antimicrobial agents. These genes have increased in abundance in humans and domestic animals, to become common components of waste streams. Estuarine habitats lie between terrestrial/freshwater and marine ecosystems, acting as natural filtering points for pollutants. Here, we have profiled ARGs in sediments from 18 estuaries over 4,000 km of coastal China using high-throughput quantitative polymerase chain reaction, and investigated their relationship with bacterial communities, antibiotic residues and socio-economic factors. ARGs in estuarine sediments were diverse and abundant, with over 200 different resistance genes being detected, 18 of which were found in all 90 sediment samples. The strong correlations of identified resistance genes with known mobile elements, network analyses and partial redundancy analysis all led to the conclusion that human activity is responsible for the abundance and dissemination of these ARGs. Such widespread pollution with xenogenetic elements has environmental, agricultural and medical consequences.

NONCODE (http://www.bioinfo.org/noncode/) is a systematic database that is dedicated to presenting the most complete collection and annotation of non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs). Since NONCODE 2016 was released two years ago, the amount of novel identified ncRNAs has been enlarged by the reduced cost of next-generation sequencing, which has produced an explosion of newly identified data. The third-generation sequencing revolution has also offered longer and more accurate annotations. Moreover, accumulating evidence confirmed by biological experiments has provided more comprehensive knowledge of lncRNA functions. The ncRNA data set was expanded by collecting newly identified ncRNAs from literature published over the past two years and integration of the latest versions of RefSeq and Ensembl. Additionally, pig was included in the database for the first time, bringing the total number of species to 17. The number of lncRNAs in NONCODEv5 increased from 527 336 to 548 640. NONCODEv5 also introduced three important new features: (i) human lncRNA–disease relationships and single nucleotide polymorphism-lncRNA–disease relationships were constructed; (ii) human exosome lncRNA expression profiles were displayed; (iii) the RNA secondary structures of NONCODE human transcripts were predicted. NONCODEv5 is also accessible through http://www.noncode.org/.

Composting is widely used for recycling of urban sewage sludge to improve soil properties, which represents a potential pathway of spreading antibiotic resistant bacteria and genes to soils. However, the dynamics of antibiotic resistance genes (ARGs) and the underlying mechanisms during sewage sludge composting were not fully explored. Here, we used high-throughput quantitative PCR and 16S rRNA gene based illumina sequencing to investigate the dynamics of ARGs and bacterial communities during a lab-scale in-vessel composting of sewage sludge. A total of 156 unique ARGs and mobile genetic elements (MGEs) were detected encoding resistance to almost all major classes of antibiotics. ARGs were detected with significantly increased abundance and diversity, and distinct patterns, and were enriched during composting. Marked shifts in bacterial community structures and compositions were observed during composting, with Actinobacteria being the dominant phylum at the late phase of composting. The large proportion of Actinobacteria may partially explain the increase of ARGs during composting. ARGs patterns were significantly correlated with bacterial community structures, suggesting that the dynamic of ARGs was strongly affected by bacterial phylogenetic compositions during composting. These results imply that direct application of sewage sludge compost on field may lead to the spread of abundant ARGs in soils.

Long noncoding RNAs (lncRNAs) have been implicated in controlling various aspects of embryonic stem cell (ESC) biology, although the functions of specific lncRNAs, and the molecular mechanisms through which they act, remain unclear. Here, we demonstrate discrete and opposing roles for the lncRNA transcript Haunt and its genomic locus in regulating the HOXA gene cluster during ESC differentiation. Reducing or enhancing Haunt expression, with minimal disruption of the Haunt locus, led to upregulation or downregulation of HOXA genes, respectively. In contrast, increasingly large genomic deletions within the Haunt locus attenuated HOXA activation. The Haunt DNA locus contains potential enhancers of HOXA activation, whereas Haunt RNA acts to prevent aberrant HOXA expression. This work reveals a multifaceted model of lncRNA-mediated transcriptional regulation of the HOXA cluster, with distinct roles for a lncRNA transcript and its genomic locus, while illustrating the power of rapid CRISPR/Cas9-based genome editing for assigning lncRNA functions.

The receptor tyrosine kinase/PI3K/AKT/mammalian target of rapamycin (RTK/PI3K/AKT/mTOR) pathway is frequently altered in cancer, but the underlying mechanism leading to tumorigenesis by activated mTOR remains less clear. Here we show that mTOR is a positive regulator of Notch signaling in mouse and human cells, acting through induction of the STAT3/p63/Jagged signaling cascade. Furthermore, in response to differential cues from mTOR, we found that Notch served as a molecular switch to shift the balance between cell proliferation and differentiation. We determined that hyperactive mTOR signaling impaired cell differentiation of murine embryonic fibroblasts via potentiation of Notch signaling. Elevated mTOR signaling strongly correlated with enhanced Notch signaling in poorly differentiated but not in well-differentiated human breast cancers. Both human lung lymphangioleiomyomatosis (LAM) and mouse kidney tumors with hyperactive mTOR due to tumor suppressor TSC1 or TSC2 deficiency exhibited enhanced STAT3/p63/Notch signaling. Furthermore, tumorigenic potential of cells with uncontrolled mTOR signaling was suppressed by Notch inhibition. Our data therefore suggest that perturbation of cell differentiation by augmented Notch signaling might be responsible for the underdifferentiated phenotype displayed by certain tumors with an aberrantly activated RTK/PI3K/AKT/mTOR pathway. Additionally, the STAT3/p63/Notch axis may be a useful target for the treatment of cancers exhibiting hyperactive mTOR signaling.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.