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.