OncomiRdbB: a comprehensive database of microRNAs and their targets in breast cancer

Abstract

BackgroundGiven the estimate that 30% of our genes are controlled by microRNAs, it is essential that we understand the precise relationship between microRNAs and their targets. OncomiRs are microRNAs (miRNAs) that have been frequently shown to be deregulated in cancer. However, although several oncomiRs have been identified and characterized, there is as yet no comprehensive compilation of this data which has rendered it underutilized by cancer biologists. There is therefore an unmet need in generating bioinformatic platforms to speed the identification of novel therapeutic targets.DescriptionWe describe here OncomiRdbB, a comprehensive database of oncomiRs mined from different existing databases for mouse and humans along with novel oncomiRs that we have validated in human breast cancer samples. The database also lists their respective predicted targets, identified using miRanda, along with their IDs, sequences, chromosome location and detailed description. This database facilitates querying by search strings including microRNA name, sequence, accession number, target genes and organisms. The microRNA networks and their hubs with respective targets at 3'UTR, 5'UTR and exons of different pathway genes were also deciphered using the 'R' algorithm.ConclusionOncomiRdbB is a comprehensive and integrated database of oncomiRs and their targets in breast cancer with multiple query options which will help enhance both understanding of the biology of breast cancer and the development of new and innovative microRNA based diagnostic tools and targets of therapeutic significance. OncomiRdbB is freely available for download through the URL link http://tdb.ccmb.res.in/OncomiRdbB/index.htm.

Structure of FUS Protein Fibrils and Its Relevance to Self-Assembly and Phase Separation of Low-Complexity Domains

“…S7). Aggrescan, FISH, FoldAmyloid, MetAmyl, and PASTA 2.0 predict a low propensity for amyloid formation across the FUS-LC sequence, presumably because these algorithms emphasize the importance of hydrophobic interactions (de Groot et al, 2012; Emily et al, 2013; Garbuzynskiy et al, 2010; Gasior and Kotulska, 2014; Walsh et al, 2014). ZipperDB, Zyggregator, and WALTZ, which include other physicochemical and structural properties of protein segments (Thompson et al, 2006) (Tartaglia and Vendruscolo, 2008) (Maurer-Stroh et al, 2010), predict that certain short segments of FUS-LC may form amyloidlike structures, but with no consensus on the identities of these segments.…”

Genome-scale modelling of microbial metabolism with temporal and spatial resolution

“…Despite their limitations with regard to intracellular organization, dynamics and regulation, these Spatiotemporal Flux Balance Analysis (SFBA) methods hold great promise for analysis of natural microbial systems as well as the design of engineered systems that exploit the evolved capabilities of microbes to optimally function in highly dynamic and spatially heterogeneous environments [70, 71]. Possible research directions for the nascent SFBA field are myriad and include: (1) continued identification and study of microbial systems such as the human microbiome [72, 73] and lignocellulosic degrading communities [74, 75] that would benefit from spatiotemporal analysis; (2) incorporation of higher fidelity intracellular models that extend beyond just reaction stoichiometry [76, 77]; (3) development of alternative methods for formulating SFBA models that are more amenable to efficient numerical solution; (4) development and testing of general purpose software such as DFBAlab [46] for the solution of the large-scale differential equation and linear program systems that result from SFBA models; and (5) experimental testing of SFBA model predictions through the collection of omics data with both temporal and spatial resolution [78–80]. Based on the overarching importance of the problem and the initial successes reported this review, SFBA can be expected to become the next major frontier for microbial metabolic modeling.…”

“…The PDEs were discretized with 100 spatial node points, and lexicographic optimization [47] with 6 LPs at each node point was used to ensure unique exchange fluxes. The large discretized model consisting of 900 ODEs in time and 600 LPs was efficiently solved within MATLAB (MathWorks, Natick, Massachusetts, USA) using the DFBAlab tool (Figure 4) [46]. …”

“…While some DFBA models can be effectively solved by a straightforward combination of ODE and LP solvers, more advanced techniques may be required due to complications associated with the LP unpredictably becoming infeasible or producing alternative optima characterized by non-unique exchange fluxes. Sophisticated simulation methods that explicitly address and effectively overcome these complications are now available [46, 47]. …”

Web-based tools for microRNAs involved in human cancer

“…OncomiRdbB (tdb.ccmb.res.in/OncomiRdbB/index.htm) (43) was built from 782 human and 246 mouse microRNAs that possessed known associations with breast cancer, which were retrieved from miRNA databases, including miRBase (18), miR2Disease (29) and PhenomiR 2.0 (28). The findings were validated using Taqman low density arrays that consisted of 667 human microRNAs and LNA™ arrays using human breast cancer samples.…”