Hongchao Lu

NPInter: the noncoding RNAs and protein related biomacromolecules interaction database

The noncoding RNAs and protein related biomacromolecules interaction database (NPInter; or ) is a database that documents experimentally determined functional interactions between noncoding RNAs (ncRNAs) and protein related biomacromolecules (PRMs) (proteins, mRNAs or genomic DNAs). NPInter intends to provide the scientific community with a comprehensive and integrated tool for efficient browsing and extraction of information on interactions between ncRNAs and PRMs. Beyond cataloguing details of these interactions, the NPInter will be useful for understanding ncRNA function, as it adds a very important functional element, ncRNAs, to the biomolecule interaction network and sets up a bridge between the coding and the noncoding kingdoms.

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.

Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors

Transcription factors with a large number of target genes—transcription hub(s), or THub(s)—are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (AN) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns.

Phylogeny of SARS-CoV as inferred from complete genome comparison

SARS-CoV, as the pathogeny of severe acute respiratory syndrome (SARS), is a mystery that the origin of the virus is still unknown even a few isolates of the virus were completely sequenced. To explore the genesis of SARS-CoV, the FDOD method previously developed by us was applied to comparing complete genomes from 12 SARS-CoV isolates to those from 12 previously identified coronaviruses and an unrooted phylogenetic tree was constructed. Our results show that all SARS-CoV isolates were clustered into a clique and previously identified coronaviruses formed the other clique. Meanwhile, the three groups of coronaviruses depart from each other clearly in our tree that is consistent with the results of prevenient papers. Differently, from the topology of the phylogenetic tree we found that SARS-CoV is more close to group 1 within genus coronavirus. The topology map also shows that the 12 SARS-CoV isolates may be divided into two groups determined by the association with the SARS-CoV from the Hotel M in Hong Kong that may give some information about the infectious relationship of the SARS.

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.