Xie B.,CAS Beijing Institute of Genomics
PloS one | Year: 2013
Divergently paired genes (DPGs), also known as bidirectional (head-to-head positioned) genes, are conserved across species and lineages, and thus deemed to be exceptional in genomic organization and functional regulation. Despite previous investigations on the features of their conservation and gene organization, the functional relationship among DPGs in a given species and lineage has not been thoroughly clarified. Here we report a network-based comprehensive analysis on human DPGs and our results indicate that the two members of the DPGs tend to participate in different biological processes while enforcing related functions as modules. Comparing to randomly paired genes as a control, the DPG pairs have a tendency to be clustered in similar "cellular components" and involved in similar "molecular functions". The functional network bridged by DPGs consists of three major modules. The largest module includes many house-keeping genes involved in core cellular activities. This module also shows low variation in expression in both CNS (central nervous system) and non-CNS tissues. Based on analyses of disease transcriptome data, we further suggest that this particular module may play crucial roles in HIV infection and its disease mechanism.
Dai L.,CAS Beijing Institute of Genomics
Biology direct | Year: 2012
As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. REVIEWERS: This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.
Liu S.,CAS Beijing Institute of Genomics
Journal of Proteome Research | Year: 2010
A proteomic strategy combining 2DE, Western blot, and mass spectrometry was implemented to survey the status of tyrosine nitration in mouse heart mitochondria. Compared to normal mice, nitrated proteins in the heart mitochondria of the db/db mouse model were significantly augmented due to diabetic development. A total of 18 proteins were identified as the nitration targets. Of the nitrated proteins, succinyl-CoA:3-oxoacid CoA-transferase (SCOT) is a key enzyme involved in ketolysis and has yet to be explored how its catalysis is affected by nitration. We therefore initiated a systematic investigation toward the nitrated site(s) and the corresponding changes of SCOT catalysis. To monitor modification kinetics and nitrated residue(s), recombinant SCOT was incubated with peroxynitrite followed by examination of nitration development as well as catalytic activity changes. The nitration of recombinant SCOT steadily increased in response to increasing concentrations of peroxynitrite, while its catalysis was gradually attenuated. The nitrated sites of modified SCOT were further identified by LC-ESI-MS/MS. The MS/MS spectra indicated a +45 mass unit ion shift from [M + H]+ m/z at Tyr 4 and Tyr76. Through site-directed mutagenesis, we found that mutation of tyrosine residues at Tyr4 or Tyr76 did not only significantly protect SCOT from peroxynitrite modification, but it also dramatically prevented loss of enzymatic activity. Taken together, these results indicate that the two tyrosine residues of SCOT are the priority sites attacked by NO, and their nitration status is a causal factor leading to inhibition of SCOT catalysis. © 2010 American Chemical Society.
Agency: Cordis | Branch: FP7 | Program: CSA-CA | Phase: HEALTH.2010.2.1.1-2 | Award Amount: 2.29M | Year: 2011
A detailed understanding of human biology will require characterisation of the human-associated microorganisms, the human microbiome, and of the roles these microbes play in health and disease. Large projects in Europe, the United States, China and Canada target these objectives, using high throughput omics approaches. Given the complexity of our microbial communities, composed of thousands of species and differing considerably between individuals, as well as the multitude of effects they have on our biology, none of the projects can hope to achieve their comprehensive characterisation. To progress most efficiently towards this ambitious goal it is of utmost importance that the data generated in each individual project be optimally comparable across all the current projects and those yet to come. Our proposal seeks to coordinate development of standard operating procedures and protocols, which will optimize data comparisons in the human microbiome field and thus improve the synergy between all the projects. It focuses on three key aspects of data generation: (i) human sample collection, processing and identification via the associated metadata; (ii) DNA sequence quality obtained by the new generation methods from complex microbial mixtures; (iii) analysis of DNA sequence in conjunction with the metadata. Importantly, it organises public access to the standard operating procedures and protocols and enables exchanges between the users and providers of the standards. It gathers very strong international partnership that includes leaders in the field and represents the current large projects, which span three continents, Europe, Asia and America. Furthermore, it interfaces via the International Human Microbiome Consortium with additional projects from Africa and Australia. The proposal is thus highly congruent with the focus of the call, which targets omics, standards and international context.
Leache A.D.,University of Washington |
Harris R.B.,University of Washington |
Rannala B.,University of California at Davis |
Rannala B.,CAS Beijing Institute of Genomics |
And 2 more authors.
Systematic Biology | Year: 2014
Gene flow among populations or species and incomplete lineage sorting (ILS) are two evolutionary processes responsible for generating gene tree discordance and therefore hindering species tree estimation. Numerous studies have evaluated the impacts of ILS on species tree inference, yet the ramifications of gene flow on species trees remain less studied. Here, we simulate and analyse multilocus sequence data generated with ILS and gene flow to quantify their impacts on species tree inference. We characterize species tree estimation errors under various models of gene flow, such as the isolation-migration model, the n-island model, and gene flow between non-sister species or involving ancestral species, and species boundaries crossed by a single gene copy (allelic introgression) or by a single migrant individual. These patterns of gene flow are explored on species trees of different sizes (4 vs. 10 species), at different time scales (shallow vs. deep), and with different migration rates. Species trees are estimated with the multispecies coalescent model using Bayesian methods (BEST and *BEAST) and with a summary statistic approach (MPEST) that facilitates phylogenomic-scale analysis. Even in cases where the topology of the species tree is estimated with high accuracy, we find that gene flow can result in overestimates of population sizes (species tree dilation) and underestimates of species divergence times (species tree compression). Signatures of migration events remain present in the distribution of coalescent times for gene trees, and with sufficient data it is possible to identify those loci that have crossed species boundaries. These results highlight the need for careful sampling design in phylogeographic and species delimitation studies as gene flow, introgression, or incorrect sample assignments can bias the estimation of the species tree topology and of parameter estimates such as population sizes and divergence times. © 2013 The Author(s).