Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users’ further investigation.

RNA silencing, mediated by small RNAs including microRNAs (miRNAs) and small interfering RNAs (siRNAs), is a potent antiviral or antibacterial mechanism, besides regulating normal cellular gene expression critical for development and physiology. To gain insights into host small RNA metabolism under infections by different viruses, we used Solexa/Illumina deep sequencing to characterize the small RNA profiles of rice plants infected by two distinct viruses, Rice dwarf virus (RDV, dsRNA virus) and Rice stripe virus (RSV, a negative sense and ambisense RNA virus), respectively, as compared with those from non-infected plants. Our analyses showed that RSV infection enhanced the accumulation of some rice miRNA*s, but not their corresponding miRNAs, as well as accumulation of phased siRNAs from a particular precursor. Furthermore, RSV infection also induced the expression of novel miRNAs in a phased pattern from several conserved miRNA precursors. In comparison, no such changes in host small RNA expression was observed in RDV-infected rice plants. Significantly RSV infection elevated the expression levels of selective OsDCLs and OsAGOs, whereas RDV infection only affected the expression of certain OsRDRs. Our results provide a comparative analysis, via deep sequencing, of changes in the small RNA profiles and in the genes of RNA silencing machinery induced by different viruses in a natural and economically important crop host plant. They uncover new mechanisms and complexity of virus-host interactions that may have important implications for further studies on the evolution of cellular small RNA biogenesis that impact pathogen infection, pathogenesis, as well as organismal development.

In this paper, a solid molecular basket sorbent, 50 wt% PEI/SBA-15, was studied for CO(2) capture from gas streams with low CO(2) concentration under ambient conditions. The sorbent was able to effectively and selectively capture CO(2) from a gas stream containing 1% CO(2) at 75 °C, with a breakthrough and saturation capacity of 63.1 and 66.7 mg g(-1), respectively, and a selectivity of 14 for CO(2)/CO and 185 for CO(2)/Ar. The sorption performance of the sorbent was influenced greatly by the operating temperature. The CO(2)-TPD study showed that the sorbent could be regenerated under mild conditions (50-110 °C) and was stable in the cyclic operations for at least 20 cycles. Furthermore, the possibility for CO(2) capture from air using the PEI/SBA-15 sorbent was studied by FTIR and proved by TPD. A capacity of 22.5 mg g(-1) was attained at 75 °C via a TPD method using a simulated air with 400 ppmv CO(2) in N(2).

BackgroundEarly maturity is one of the most important and complex agronomic traits in upland cotton (Gossypium hirsutum L). To dissect the genetic architecture of this agronomically important trait, a population consisting of 355 upland cotton germplasm accessions was genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) approach, of which a subset of 185 lines representative of the diversity among the accessions was phenotypically characterized for six early maturity traits in four environments. A genome-wide association study (GWAS) was conducted using the generalized linear model (GLM) and mixed linear model (MLM).ResultsA total of 81,675 SNPs in 355 upland cotton accessions were discovered using SLAF-seq and were subsequently used in GWAS. Thirteen significant associations between eight SNP loci and five early maturity traits were successfully identified using the GLM and MLM; two of the 13 associations were common between the models. By computing phenotypic effect values for the associations detected at each locus, 11 highly favorable SNP alleles were identified for five early maturity traits. Moreover, dosage pyramiding effects of the highly favorable SNP alleles and significant linear correlations between the numbers of highly favorable alleles and the phenotypic values of the target traits were identified. Most importantly, a major locus (rs13562854) on chromosome Dt3 and a potential candidate gene (CotAD_01947) for early maturity were detected.ConclusionsThis study identified highly favorable SNP alleles and candidate genes associated with early maturity traits in upland cotton. The results demonstrate that GWAS is a powerful tool for dissecting complex traits and identifying candidate genes. The highly favorable SNP alleles and candidate genes for early maturity traits identified in this study should be show high potential for improvement of early maturity in future cotton breeding programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2875-z) contains supplementary material, which is available to authorized users.