Lun Cai

NONCODE: an integrated knowledge database of non-coding RNAs.

NONCODE is an integrated knowledge database dedicated to non-coding RNAs (ncRNAs), that is to say, RNAs that function without being translated into proteins. All ncRNAs in NONCODE were filtered automatically from literature and GenBank, and were later manually curated. The distinctive features of NONCODE are as follows: (i) the ncRNAs in NONCODE include almost all the types of ncRNAs, except transfer RNAs and ribosomal RNAs. (ii) All ncRNA sequences and their related information (e.g. function, cellular role, cellular location, chromosomal information, etc.) in NONCODE have been confirmed manually by consulting relevant literature: more than 80% of the entries are based on experimental data. (iii) Based on the cellular process and function, which a given ncRNA is involved in, we introduced a novel classification system, labeled process function class, to integrate existing classification systems. (iv) In addition, some 1100 ncRNAs have been grouped into nine other classes according to whether they are specific to gender or tissue or associated with tumors and diseases, etc. (v) NONCODE provides a user-friendly interface, a visualization platform and a convenient search option, allowing efficient recovery of sequence, regulatory elements in the flanking sequences, secondary structure, related publications and other information. The first release of NONCODE (v1.0) contains 5339 non-redundant sequences from 861 organisms, including eukaryotes, eubacteria, archaebacteria, virus and viroids. Access is free for all users through a web interface at http://noncode.bioinfo.org.cn.

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm

On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP‐core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGOs improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.

Date of origin of the SARS coronavirus strains

BackgroundA new respiratory infectious epidemic, severe acute respiratory syndrome (SARS), broke out and spread throughout the world. By now the putative pathogen of SARS has been identified as a new coronavirus, a single positive-strand RNA virus. RNA viruses commonly have a high rate of genetic mutation. It is therefore important to know the mutation rate of the SARS coronavirus as it spreads through the population. Moreover, finding a date for the last common ancestor of SARS coronavirus strains would be useful for understanding the circumstances surrounding the emergence of the SARS pandemic and the rate at which SARS coronavirus diverge.MethodsWe propose a mathematical model to estimate the evolution rate of the SARS coronavirus genome and the time of the last common ancestor of the sequenced SARS strains. Under some common assumptions and justifiable simplifications, a few simple equations incorporating the evolution rate (K) and time of the last common ancestor of the strains (T0) can be deduced. We then implemented the least square method to estimate K and T0 from the dataset of sequences and corresponding times. Monte Carlo stimulation was employed to discuss the results.ResultsBased on 6 strains with accurate dates of host death, we estimated the time of the last common ancestor to be about August or September 2002, and the evolution rate to be about 0.16 base/day, that is, the SARS coronavirus would on average change a base every seven days. We validated our method by dividing the strains into two groups, which coincided with the results from comparative genomics.ConclusionThe applied method is simple to implement and avoid the difficulty and subjectivity of choosing the root of phylogenetic tree. Based on 6 strains with accurate date of host death, we estimated a time of the last common ancestor, which is coincident with epidemic investigations, and an evolution rate in the same range as that reported for the HIV-1 virus.

Systematic identification of non-coding RNA 2,2,7-trimethylguanosine cap structures in Caenorhabditis elegans

BackgroundThe 2,2,7-trimethylguanosine (TMG) cap structure is an important functional characteristic of ncRNAs with critical cellular roles, such as some snRNAs. Here we used immunoprecipitation with both K121 and R1131 anti-TMG antibodies to systematically identify the TMG cap structures for all presently characterized ncRNAs in C. elegans.ResultsThe two anti-TMG antibodies precipitated a similar group of the C. elegans ncRNAs. All snRNAs known to have a TMG cap structure were found in the precipitate, indicating that our identification system was efficient. Other ncRNA families related to splicing, such as SL RNAs and Sm Y RNAs, were also found in the precipitate, as were 7 C/D box snoRNAs. Further analysis showed that the SL RNAs and the Sm Y RNAs shared a very similar Sm binding site element (AAU4–5GGA), which sequence composition differed somewhat from those of other U snRNAs. There were also 16 ncRNAs without an Sm binding site element in the precipitate, suggesting that for these ncRNAs, TMG formation may occur independently of Sm proteins.ConclusionOur results showed that most ncRNAs predicted to be transcribed by RNA polymerase II had a TMG cap, while those predicted to be transcribed by RNA plymerase III or located in introns did not have a TMG cap structure. Compared to ncRNAs without a TMG cap, TMG-capped ncRNAs tended to have higher expression levels. Five functionally non-annotated ncRNAs also have a TMG cap structure, which might be helpful for identifying the cellular roles of these ncRNAs.

Assessing TF regulatory relationships of divergently transcribed genes.

Ambiguously located transcription factor (TF) binding sites may introduce a large number of potentially erroneous regulatory associations into models of transcriptional regulatory networks. We have used a two-step expression similarity strategy to distinguish between likely and unlikely regulatory associations for TFs located between divergently transcribed genes in the yeast genome. Most regulatory associations of divergently transcribed genes could be assigned to either high-confidence (HC) or low-confidence (LC) groups. In support of our result, we found that most of the previously characterized regulatory associations reported in the literature fell into the HC group rather than the LC group. Moreover, genomic distance analysis showed that TF binding sites tend to be located in relative proximity to the gene that is most likely to be regulated by this TF. Finally, removal of low-confidence (i.e., most probably erroneous) regulatory associations from the transcriptional regulatory network barely affected its basic architecture.

Analysis of correlations between protein complex and protein-protein interaction and mRNA expression

Protein-protein interaction is a physical interaction of two proteins in living cells. In budding yeastSaccharomyces cerevisiae, large-scale protein-protein interaction data have been obtained through high-throughput yeast two-hybrid systems (Y2H) and protein complex purification techniques based on mass-spectrometry. Here, we collect 11855 interactions between total 2617 proteins. Through seriate genome-wide mRNA expression data, similarity between two genes could be measured. Protein complex data can also be obtained publicly and can be translated to pair relationship that any two proteins can only exist in the same complex or not. Analysis of protein complex data, protein-protein interaction data and mRNA expression data can elucidate correlations between them. The results show that proteins that have interactions or similar expression patterns have a higher possibility to be in the same protein complex than randomized selected proteins, and proteins which have interactions and similar expression patterns are even more possible to exist in the same protein complex. The work indicates that comprehensive integration and analysis of public large-scale bioinformatical data, such as protein complex data, protein-protein interaction data and mRNA expression data, may help to uncover their relationships and common biological information underlying these data. The strategies described here may help to integrate and analyze other functional genomic and proteomic data, such as gene expression profiling, protein-localization mapping and large-scale phenotypic data, both in yeast and in other organisms.