Real-time and label-free detection of biomolecular interactions by oblique-incidence reflectivity difference method

We successfully conduct the label-free and real-time detection of the interactions between epoxy groups and rabbit IgG and 5＇ CTT CAG GTC ATG AGC CTG AT 3＇ oligonucleotide, and between the hybridization of 5＇ CTT CAG GTC ATG AGC CTG AT 3＇ and its complementary 3＇ GAA GTC CAC TAC TCG GAC TA 5＇ oligonucleotide, by the oblique-incidence reflectivity difference （OI-RD） method. The dynamic curves of OI-RD signals, corresponding to the kinetic processes of biomolecular combination or hybridization, are acquired. In our case, the combination of epoxy groups with rabbit IgG and 5＇ CTT CAC CTC ATG AGC CTG AT 3＇ oligonucleotide need almost one and a half hours and about two hundred seconds, respectively; and the hybridization of the two oligonucleotides needs about five hundred seconds. The experimental results show that the OI-RD is a promising method for the real-time and label-free detection of biomolecular interactions.

A Global Identification and Analysis of Small Nucleolar RNAs and Possible Intermediate-Sized Non-Coding RNAs in Oryza sativa

Accumulating evidence suggests that non-coding RNAs （ncRNAs） are both widespread and functionally important in many eukaryotic organisms. In this study, we employed a special size fractionation and cDNA library construction method followed by 454 deep sequencing to systematically profile rice intermediate-size ncRNAs. Our analysis resulted in the identification of 1349 ncRNAs in total, including 754 novel ncRNAs of an unknown functional category. Chromosome distribution of all identified ncRNAs showed no strand bias, and displayed a pattern similar to that observed in protein-coding genes with few chromosome dependencies. More than half of the ncRNAs were centered around the plus-strand of the 5＇ and 3＇ termini of the coding regions. The majority of the novel ncRNAs were rice specific, while 78% of the small nucleolar RNAs （snoRNAs） were conserved. Tandem duplication drove the expansion of over half of the snoRNA gene families. Furthermore, 90% of the snoRNA candidates were shown to produce small RNAs between 20-30 nt, 80% of which were associated with ARGONAUT proteins generally, and AGOlb in particular. Overall, our findings provide a comprehensive view of an intermediate-size non-coding transcriptome in a monocot species, which will serve as a useful platform for an in-depth analysis of ncRNA functions.

Genomic Features and Regulatory Roles of Intermediate-Sized Non-Coding RNAs in Arabidopsis

ABSTRACT Recent advances in genome-wide techniques allowed the identification of thousands of non-coding RNAs with various sizes in eukaryotes, some of which have further been shown to serve important functions in many biologi- cal processes. However, in model plant Arabidopsis, novel intermediate-sized ncRNAs （im-ncRNAs） （50-300 nt） have very limited information. By using a modified isolation strategy combined with deep-sequencing technology, we identified 838 im-ncRNAs in Arabidopsis globally. More than half （58%） are new ncRNA species, mostly evolutionary divergent. Interestingly, annotated protein-coding genes with 5＇-UTR-derived novel im-ncRNAs tend to be highly expressed. For intergenic im-ncRNAs, their average abundances were comparable to mRNAs in seedlings, but subsets exhibited signifi- cantly lower expression in senescing leaves. Further, intergenic im-ncRNAs were regulated by similar genetic and epige- netic mechanisms to those of protein-coding genes, and some showed developmentally regulated expression patterns. Large-scale reverse genetic screening showed that the down-regulation of a number of im-ncRNAs resulted in either obvious molecular changes or abnormal developmental phenotypes in vivo, indicating the functional importance of im-ncRNAs in plant growth and development. Together, our results demonstrate that novel Arabidopsis im-ncRNAs are developmentally regulated and functional components discovered in the transcriptome.

CloudLCA: finding the lowest common ancestor in metagenome analysis using cloud computing

Estimating taxonomic content constitutes a key problem in metagenomic sequencing data analysis.However,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available software.Here,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data analysis.Results show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin nodes.In comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat node.CloudLCA can be run on one multiprocessor node or a cluster.It is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following analysis.Moreover,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.