miRTar: an integrated system for identifying miRNA-target interactions in human

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human protein-coding genes. The aim of this work is to consider various analyzing scenarios in the identification of miRNA-target interactions, as well as to provide an integrated system that will aid in facilitating investigation on the influence of miRNA targets by alternative splicing and the biological function of miRNAs in biological pathways.</p> <p>Results</p> <p>This work presents an integrated system, miRTar, which adopts various analyzing scenarios to identify putative miRNA target sites of the gene transcripts and elucidates the biological functions of miRNAs toward their targets in biological pathways. The system has three major features. First, the prediction system is able to consider various analyzing scenarios (1 miRNA:1 gene, 1:N, N:1, N:M, all miRNAs:N genes, and N miRNAs: genes involved in a pathway) to easily identify the regulatory relationships between interesting miRNAs and their targets, in 3'UTR, 5'UTR and coding regions. Second, miRTar can analyze and highlight a group of miRNA-regulated genes that participate in particular KEGG pathways to elucidate the biological roles of miRNAs in biological pathways. Third, miRTar can provide further information for elucidating the miRNA regulation, i.e., miRNA-target interactions, affected by alternative splicing.</p> <p>Conclusions</p> <p>In this work, we developed an integrated resource, miRTar, to enable biologists to easily identify the biological functions and regulatory relationships between a group of known/putative miRNAs and protein coding genes. miRTar is now available at <url>http://miRTar.mbc.nctu.edu.tw/</url>.</p

Guided-mode resonance biochip system for early detection of ovarian cancer

Biomedical Tissue Engineering, Biomaterials, & Medical Devices Poster SessionA high-accuracy, sensor system has been developed that provides near-instantaneous detection of biomarker proteins as indicators of ovarian serous papillary carcinoma. Based upon photonic guided-mode resonance technology, these high-resolution sensors employ multiple resonance peaks to rapidly test for relevant proteins in complex biological samples. This label-free sensor approach requires minimal sample processing and has the capability to measure multiple agents simultaneously and in real time. In this work, a sensor system that uses a fixed-wavelength source with a shaped input wavefront to auto-scan in angle has been developed. As binding events occur at the sensor surface, resonance reflection peak shifts are tracked as a function of incident angle on an integrated CMOS detector. The amount of angular shift is linearly correlated to the quantity of biomarker protein in a biological sample. Multiple resonance peaks provide increased detection information about the binding dynamics occurring at the sensor surface, thus decreasing false detection readings. Simultaneous detection of multiple biomarker proteins in parallel with sensitivities in the pM range contributes to the potential for differential real-time data analysis. A biochip system prototype has been developed and the system performance characterized. Identification and quantification of protein biomarkers that are up- or down- regulated in blood and serum as indicators of ovarian cancer will be presented

Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources

Dual expression recombinase based (DERB) single vector system for high throughput screening and verification of protein interactions in living cells

Identification of novel protein interactions and their mediators is fundamental in understanding cellular processes and is necessary for protein-targeted therapy. Evidently high throughput formatting of these applications in living cells would be beneficial, however no adequate system exists. We present a novel platform technology for the high throughput screening and verification of protein interactions in living cells. The platform&#x27;s series of Dual Expression Recombinase Based (DERB) destiny vectors individually encode two sets of recombinase recognizable sequences for inserting the protein open reading frame (ORF) of interest, two sets of promoters and reporter tags in frame with the ORFs for detecting interactions. Introduction into living cells (prokaryotic and eukaryotic) enables the detection of protein interactions by fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC). The DERB platform shows advantages over current commercialized systems by DERB vectors validated through proof-of-principle experiments and the identification of an unknown interaction

Comparative analysis of plant immune receptor architectures uncovers host proteins likely targeted by pathogens.

BACKGROUND: Plants deploy immune receptors to detect pathogen-derived molecules and initiate defense responses. Intracellular plant immune receptors called nucleotide-binding leucine-rich repeat (NLR) proteins contain a central nucleotide-binding (NB) domain followed by a series of leucine-rich repeats (LRRs), and are key initiators of plant defense responses. However, recent studies demonstrated that NLRs with non-canonical domain architectures play an important role in plant immunity. These composite immune receptors are thought to arise from fusions between NLRs and additional domains that serve as "baits" for the pathogen-derived effector proteins, thus enabling pathogen recognition. Several names have been proposed to describe these proteins, including "integrated decoys" and "integrated sensors". We adopt and argue for "integrated domains" or NLR-IDs, which describes the product of the fusion without assigning a universal mode of action. RESULTS: We have scanned available plant genome sequences for the full spectrum of NLR-IDs to evaluate the diversity of integrations of potential sensor/decoy domains across flowering plants, including 19 crop species. We manually curated wheat and brassicas and experimentally validated a subset of NLR-IDs in wild and cultivated wheat varieties. We have examined NLR fusions that occur in multiple plant families and identified that some domains show re-occurring integration across lineages. Domains fused to NLRs overlap with previously identified pathogen targets confirming that they act as baits for the pathogen. While some of the integrated domains have been previously implicated in disease resistance, others provide new targets for engineering durable resistance to plant pathogens. CONCLUSIONS: We have built a robust reproducible pipeline for detecting variable domain architectures in plant immune receptors across species. We hypothesize that NLR-IDs that we revealed provide clues to the host proteins targeted by pathogens, and that this information can be deployed to discover new sources of disease resistance

dbOGAP - An Integrated Bioinformatics Resource for Protein O-GlcNAcylation

<p>Abstract</p> <p>Background</p> <p>Protein O-GlcNAcylation (or O-GlcNAc-ylation) is an O-linked glycosylation involving the transfer of β-<it>N</it>-acetylglucosamine to the hydroxyl group of serine or threonine residues of proteins. Growing evidences suggest that protein O-GlcNAcylation is common and is analogous to phosphorylation in modulating broad ranges of biological processes. However, compared to phosphorylation, the amount of protein O-GlcNAcylation data is relatively limited and its annotation in databases is scarce. Furthermore, a bioinformatics resource for O-GlcNAcylation is lacking, and an O-GlcNAcylation site prediction tool is much needed.</p> <p>Description</p> <p>We developed a database of O-GlcNAcylated proteins and sites, dbOGAP, primarily based on literature published since O-GlcNAcylation was first described in 1984. The database currently contains ~800 proteins with experimental O-GlcNAcylation information, of which ~61% are of humans, and 172 proteins have a total of ~400 O-GlcNAcylation sites identified. The O-GlcNAcylated proteins are primarily nucleocytoplasmic, including membrane- and non-membrane bounded organelle-associated proteins. The known O-GlcNAcylated proteins exert a broad range of functions including transcriptional regulation, macromolecular complex assembly, intracellular transport, translation, and regulation of cell growth or death. The database also contains ~365 potential O-GlcNAcylated proteins inferred from known O-GlcNAcylated orthologs. Additional annotations, including other protein posttranslational modifications, biological pathways and disease information are integrated into the database. We developed an O-GlcNAcylation site prediction system, OGlcNAcScan, based on Support Vector Machine and trained using protein sequences with known O-GlcNAcylation sites from dbOGAP. The site prediction system achieved an area under ROC curve of 74.3% in five-fold cross-validation. The dbOGAP website was developed to allow for performing search and query on O-GlcNAcylated proteins and associated literature, as well as for browsing by gene names, organisms or pathways, and downloading of the database. Also available from the website, the OGlcNAcScan tool presents a list of predicted O-GlcNAcylation sites for given protein sequences.</p> <p>Conclusions</p> <p>dbOGAP is the first public bioinformatics resource to allow systematic access to the O-GlcNAcylated proteins, and related functional information and bibliography, as well as to an O-GlcNAcylation site prediction tool. The resource will facilitate research on O-GlcNAcylation and its proteomic identification.</p

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues