BackgroundCurrently available microRNA (miRNA) target prediction algorithms require the presence of a conserved seed match to the 5' end of the miRNA and limit the target sites to the 3' untranslated regions of mRNAs. However, it has been noted that these requirements may be too stringent, leading to a substantial number of missing targets.ResultsWe have developed TargetS, a novel computational approach for predicting miRNA targets with the target sites located along entire gene sequences, which permits finding additional targets that are not located in the 3' un-translated regions. Our model is based on both canonical seed matching and non-canonical seed pairing, which discovers targets that allow one bit GU wobble. It does not rely on evolutionary conservation, so it allows the detection of species-specific miRNA-mRNA interactions and makes it suitable for analyzing un-conserved gene sequences. To test the performance of our approach, we have imported the widely used benchmark dataset revealing fold-changes in protein production corresponding to each of the five selected microRNAs. Compared to well-known miRNA target prediction tools, including TargetScanS, PicTar and MicroT_CDS, our method yields the highest sensitivity, while achieving a comparable level of accuracy. Human miRNA target predictions using our computational approach are available online at http://liubioinfolab.org/targetS/mirna.htmlConclusionsA simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites. Our method also expands the target search space to different gene regions, rather than limiting to 3'UTR only. This improves the sensitivity of miRNA target identification, while achieving a comparable accuracy with existing methods.

The source code is available at http://nba.uth.tmc.edu/homepage/liu/pLasso.

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Compressed sensing (CS) has been applied to magnetic resonance imaging for the acceleration of data collection. However, existing CS techniques usually produce images with residual artifacts, particularly at high reduction factors. In this paper, we propose a novel, two-stage reconstruction scheme, which takes advantage of the properties of k-space data and under-sampling patterns that are useful in CS. In this algorithm, the under-sampled k-space data is segmented into low-frequency and high-frequency domains. Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the high-frequency k-space data to complete the second stage reconstruction of the whole of k-space. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate and more homogeneous signal magnitudes than conventional approaches. Because the restricted isometric property is easier to satisfy, the reconstruction consequently produces lower residual errors at each step. Compared with a conventional CS reconstruction, for the cases of cardiac cine, brain and angiogram imaging, the proposed method achieves a more accurate reconstruction with an improvement of 2-4 dB in peak signal-to-noise ratio respectively, using reduction factors of up to 6.

An elevated serum IgG4 level is one of the most useful factors in the diagnosis of IgG4-related disease (IgG4-RD). In this study, we performed a meta-analysis of the published articles assessing the diagnostic accuracy of serum IgG4 concentrations for IgG4-RD. The databases of MEDLINE/PubMed, EMBASE and Web of Science were systematically searched for relevant studies. Sensitivities and specificities of serum IgG4 in each study were calculated, and the hierarchical summary receiver operating characteristic (HSROC) model with a random effects model were employed to obtain the individual and pooled estimates of sensitivities and specificities. In total, twenty-three studies comprising 6048 patients with IgG4-RD were included in the meta-analysis. The pooled sensitivity was 85% with a 95% confidence interval (CI) of 78–90%; the pooled specificity was 93% with a 95% CI of 90–95%. The HSROC curve for quantitative serum IgG4 lies closer to the upper left corner of the plot, and the area under the curve (AUC) was 0.95 (95% CI 0.93, 0.97), which suggested a high diagnostic accuracy of serum IgG4 for the entity of IgG4-RD. Our study suggests that serum IgG4 has high sensitivity and specificity in the diagnosis of IgG4-RD.

MicroRNAs (miRNAs) are small RNA molecules that play important roles in gene regulation and translational repression. The mechanisms that facilitate miRNA target binding and recognition have been extensively studied in recent years. However, it is still not well known how the miRNA regulation is affected by the location and the flanking sequences of miRNA target sites. In this study, we systematically quantify the contribution of a wide spectrum of target sites on miRNA-mediated gene expression regulation. Our study investigates target sites located in four different gene regions, including 3' untranslated regions, coding sequences, 5′ untranslated regions and promoter regions. We have also introduced four additional non-canonical types of seed matches beyond the canonical seed matches, and included them in our study. Computational analysis of quantitative proteomic data has demonstrated that target sites located in different regions impact the miRNA-mediated repression differently but synergistically. In addition, we have shown the synergistic effects among non-canonical seed matches and canonical ones that enhance the miRNA regulatory effects. Further systematic analysis on the site accessibility near the target regions and the secondary structure of the mRNA sequences have demonstrated substantial variations among target sites of different locations and of different types of seed matches, suggesting the mRNA secondary structure could explain some of the difference in the miRNA regulatory effects impacted by these different target sites. Our study implies miRNAs might regulate their targets under different mechanisms when target sites vary in both their locations and the types of seed matches they contain.

Seed traits are agronomically important for Cucurbita breeding, but the genes controlling seed size, seed weight and seed number have not been mapped in Cucurbita maxima (C. maxima). In this study, 100 F 2 individual derived from two parental lines, "2013-12" and "9-6", were applied to construct a 3,376.87-cM genetic map containing 20 linkage groups (LGs) with an average genetic distance of 0.47 cM using a total of 8,406 specific length amplified fragment (SLAF) markers in C. maxima. Ten quantitative trait loci (QTLs) of seed width (SW), seed length (SL) and hundred-seed weight (HSW) were identified using the composite interval mapping (CIM) method. The QTLs affecting SW, SL and HSW explained a maximum of 38.6%, 28.9% and 17.2% of the phenotypic variation and were detected in LG6, LG6 and LG17, respectively. To validate these results, an additional 150 F 2 individuals were used for QTL mapping of SW and SL with cleaved amplified polymorphic sequence (CAPS) markers. We found that two major QTLs, SL6-1 and SW6-1, could be detected in both SLAF-seq and CAPS markers in an overlapped region. Based on gene annotation and non-synonymous single-nucleotide polymorphisms (SNPs) in the major SWand SL-associated regions, we found that two genes encoding a VQ motif and an E3 ubiquitin-protein ligase may be candidate genes influencing SL, while an F-box and leucinerich repeat (LRR) domain-containing protein is the potential regulator for SW in C. maxima. This study provides the first high-density linkage map of C. maxima using SNPs developed by SLAF-seq technology, which is a powerful tool for associated mapping of important agronomic traits, map-based gene cloning and markerassisted selection (MAS)-based breeding in C. maxima.

Clinically-informative biomarkers of ischemic stroke are needed for rapid diagnosis and timely treatment. In the present study, APOA1 unique peptide (APOA1-UP), a novel peptide biomarker, was identified and quantified by multiple reaction monitoring (MRM) using labeled reference peptide (LRP). Serum samples of 94 patients in the ischemic stroke group and 37 patients in the non-stroke group were analyzed for the levels of total APOA1-UP, low density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). Median ratio of total APOA1-UP/LRP was 2.14 (interquartile range, 0.40) in the non-stroke group and 1.32 (0.44) in the ischemic stroke group (p < 0.0001). The serum level of total APOA1-UP was independently correlated with the presence of ischemic stroke by multivariate logistic regression analysis (p < 0.0001). From the receiver operating characteristic (ROC) curve, the area under the curve (AUC) was 0.9750 and the optimal cutoff value of the serum APOA1-UP level was 1.80, which yielded a sensitivity of 90.63% and a specificity of 97.14%. The diagnostic efficiency of HDL-C was lower, with an AUC of 0.7488. Therefore, the serum level of APOA1-UP is a diagnostic biomarker candidate for ischemic stroke in the early stage.