ALBERT

Description: Human housekeeping genes are often confused with essential human genes, and several studies regard both types of genes as having the same level of evolutionary conservation. However, this is not necessarily the case. To clarify this, we compared the differences between human housekeeping genes and essential human genes with respect to four aspects: the evolutionary rate (dN/dS), protein sequence identity, single-nucleotide polymorphism (SNP) density and level of linkage disequilibrium (LD). The results showed that housekeeping genes had lower evolutionary rates, higher sequence identities, lower SNP densities and higher levels of LD compared with essential genes. Together, these findings indicate that housekeeping and essential genes are two distinct types of genes, and that housekeeping genes have a higher level of evolutionary conservation. Therefore, we suggest that researchers should pay careful attention to the distinctions between housekeeping genes and essential genes. Moreover, it is still controversial whether we should substitute human orthologs of mouse essential genes for human essential genes. Therefore, we compared the evolutionary features between human orthologs of mouse essential genes and human housekeeping genes and we got inconsistent results in long-term and short-term evolutionary characteristics implying the irrationality of simply replacing human essential genes with human orthologs of mouse essential genes.

Description: Motivation: Computational prediction of compound–protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods. Results: This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein–protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans . We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound–protein databases. Availability: Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/ . Contact: sgzhou@fudan.edu.cn Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: The early stages of magmatic processes operating at mantle depths beneath continental arcs are poorly known. The chemical compositions of minerals and rocks, mineral Sr–Nd–Hf–O isotopes and zircon U–Pb ages of garnet clinopyroxenite dykes from the Shenglikou peridotite massif (North Qaidam Orogen, NE Tibet, China) were studied to constrain their sources and genesis, and the dynamic processes that controlled pyroxenite formation beneath an early Paleozoic active continental margin. Major-element compositions of bulkrocks suggest that the pyroxenitic protoliths were cumulates segregated from a melt, which was extracted from a peridotite-dominated mantle source. Bulk-rock and mineral trace-element patterns show strong enrichment in fluid-mobile elements (e.g. Cs, Rb, Ba, Th, U, K, Pb and Li) and marked negative anomalies in the high field strength elements relative to rare earth elements, similar to the characteristics of melts derived from a volatile-rich sub-arc mantle. Enriched Sr and Nd initial isotopic compositions at 500 Ma ( 87 Sr/ 86 Sr of 0·70919–0·71774 and Nd of –16·3 to –3·4) are in contrast to the highly radiogenic Hf isotope compositions (similar to those of the depleted-mantle reservoir) and to the uncontaminated upper-mantle 18 O V-SMOW (garnet: 5·6 ± 0·3, 2SD, n = 61; zircon: 5·9 ± 0·3, 2SD, n = 28). These decoupled isotopic signatures suggest that the melt source was located in a convective mantle wedge (controlling the Hf and O isotopes) that had been pervasively metasomatized by fluids from a subducted Proto-Tethys oceanic slab (controlling the Sr–Nd isotopes and highly incompatible elements). Zircons with two groups of U–Pb ages (430 ± 5 Ma and 401 ± 7 Ma) were generated by recrystallization events, corresponding to UHP metamorphism and a major uplift stage during the North Qaidam orogeny, respectively. The combined evidence reveals a picture of continental arc magmatism at mantle depths and subsequent continental collision. The subduction of the Proto-Tethys oceanic slab beneath the southern Qilian margin triggered flux melting of the metasomatized convective mantle wedge and generated hydrous arc magmas. These primitive magmas intruded into the overlying lithospheric mantle and segregated the cumulates parental to the Shenglikou pyroxenites. Subsequent continental subduction incorporated fragments of the mantle-wedge peridotite (containing pyroxenite dykes) at ~430 Ma and carried them to shallow depths during exhumation at ~400 Ma.

Description: 〈span〉〈div〉SUMMARY〈/div〉It is essential to pick 〈span〉P〈/span〉-wave and 〈span〉S〈/span〉-wave arrival times rapidly and accurately for the microseismic monitoring systems. Meanwhile, it is not easy to identify the arrivals at a true phase automatically using traditional picking method. This is one of the reasons that many researchers are trying to introduce deep neural networks to solve these problems. Convolutional neural networks (CNNs) are very attractive for designing automatic phase pickers especially after introducing the fundamental network structure from semantic segmentation field, which can give the probability outputs for every labelled phase at every sample in the recordings. The typical segmentation architecture consists of two main parts: (1) an encoder part trained to extracting coarse semantic features; (2) a decoder part responsible not only for recovering the input resolution at the output but also for obtaining sparse representation of the objects. The fundamental segmentation structure performs well; however, the influence of the parameters in the structure on the pickers has not been investigated. It means that the structure design just depends on experience and tests. In this paper, we solve two main questions to give some guidance on network design. First, we show what sparse features will learn from the three-component microseismic recordings using CNNs. Second, the influence of two key parameters in the network on pickers, namely, the depth of decoder and activation functions, is analysed. Increasing the number of levels for a certain layer in the decoder will increase the burden of demand on trainable parameters, but it is beneficial to the accuracy of the model. Reasonable depth of the decoder can balance prediction accuracy and the demand of labelled data, which is important for microseismic systems because manual labelling process will decrease the real-time performance in monitoring tasks. Standard rectified linear unit (ReLU) and leaky rectified linear unit (Leaky ReLU) with different negative slopes are compared for the analysis. Leaky ReLU with a small negative slope can improve the performance of a given model than ReLU activation function by keeping some information about the negative parts.〈/span〉

Description: Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. However, we know little of mutational spectrum in the Chinese population. Thus, here we report the identification of somatic mutations for Chinese PTC using 402 tumor-normal pairs (Discovery: 91 pairs via exome sequencing; validation: 311 pairs via Sanger sequencing). We observed three distinct mutational signatures, evidently different from the two mutational signatures among Caucasian PTCs. Ten significantly mutated genes were identified, most previously uncharacterized. Notably, we found that long non-coding RNA (lncRNA) GAS8-AS1 is the secondary most frequently altered gene and acts as a novel tumor suppressor in PTC. As a mutation hotspot, the c.713A〉G/714T〉C dinucleotide substitution was found among 89.1% patients with GAS8-AS1 mutations and associated with advanced PTC disease ( P = 0.009). Interestingly, the wild-type lncRNA GAS8-AS1 (A 713 T 714 ) showed consistently higher capability to inhibit cancer cell growth compared to the mutated lncRNA (G 713 C 714 ). Further studies also elucidated the oncogene nature of the G protein-coupled receptor LPAR4 and its c.872T〉G (p.Ile291Ser) mutation in PTC malignant transformation. The BRAF c.1799T〉A (p.Val600Glu) substitution was present in 59.0% Chinese PTCs, more frequently observed in patients with lymph node metastasis ( P = 1.6 x 10 –4 ). Together our study defines a exome mutational spectrum of PTC in the Chinese population and highlights lncRNA GAS8-AS1 and LPAR4 as potential diagnostics and therapeutic targets.

Description: High-Mg andesites (HMAs) and adakitic rocks are purported to occur exclusively in subduction zones in the modern Earth. In the North China Craton, early Cretaceous HMAs and adakitic dacites were erupted in a continental setting, apparently unrelated to subduction given their location distal (〉1000 km) to the trench at that time. Here we report petrological, mineralogical and geochemical data for these rocks with the aim of constraining their petrogenesis and elucidating the role of water in intraplate magmatism and cratonic destruction. The HMAs can be subdivided into olivine (Ol-)HMAs and clinopyroxene (Cpx-)HMAs. The former have high MgO (〉9·8 wt %) and Mg# (〉71), with rare high-Fo (up to 91) olivine phenocrysts, corresponding to (near-)primary magmas that equilibrated with mantle peridotite. The latter have moderate MgO (7·8–8·8 wt %) and Mg# (mostly 〈70) and low-Fo (mostly 〈 83) olivine phenocrysts. The Cpx-HMAs are interpreted as magmas differentiated from the Ol-HMAs by olivine-dominated fractionation at lower-crust levels. P–T–X H2O estimations show that the primary HMAs are melts of shallow (1·1–1·2 GPa), hot (~1250°C) and wet (H 2 O 〉 3 wt %) lithospheric mantle. The coexisting adakitic dacites are hydrous (H 2 O ≥ 5 wt %) magmas with high SiO 2 (〉63 wt %), Sr/Y ratios (≥39) and Yb SN (source-normalized), low (Sm/Yb) SN , and negligible Eu anomalies. They also have unradiogenic whole-rock Nd [ Nd ( t ) = –19 to –9] and zircon Hf [ Hf ( t ) = –23 to –21] isotopic compositions consistent with derivation by melting of ancient lower crust at depths 〈 40 km. Melting may have been induced by heating and addition of H 2 O from underplated HMAs. Mixing between Cpx-HMAs and low-Mg adakitic dacites in magma chambers produced high-Mg adakitic rocks. The petrogenetic model presented here explains the occurrence of intraplate HMAs and adakitic magmas elsewhere in the North China Craton. The P–T–X H2O conditions inferred for HMA generation imply that the subcontinental lithospheric mantle beneath the craton was hot and hydrous in the early Cretaceous, which may have triggered the destruction of the cratonic root. The occurrence of young HMAs and adakitic rocks in an intraplate extensional environment also casts doubts on the common use of a similar igneous rock association as an indicator of subduction processes in Archean time.

Description: RIG-I and MDA5 are the major intracellular immune receptors that recognize viral RNA species and undergo a series of conformational transitions leading to the activation of the interferon-mediated antiviral response. However, to date, full-length RLRs have resisted crystallographic efforts and a molecular description of their activation pathways remains hypothetical. Here we employ hydrogen/deuterium exchange coupled with mass spectrometry (HDX-MS) to probe the apo states of RIG-I and MDA5 and to dissect the molecular details with respect to distinct RNA species recognition, ATP binding and hydrolysis and CARDs activation. We show that human RIG-I maintains an auto-inhibited resting state owing to the intra-molecular HEL2i-CARD2 interactions while apo MDA5 lacks the analogous intra-molecular interactions and therefore adopts an extended conformation. Our work demonstrates that RIG-I binds and responds differently to short triphosphorylated RNA and long duplex RNA and that sequential addition of RNA and ATP triggers specific allosteric effects leading to RIG-I CARDs activation. We also present a high-resolution protein surface mapping technique that refines the cooperative oligomerization model of neighboring MDA5 molecules on long duplex RNA. Taken together, our data provide a high-resolution view of RLR activation in solution and offer new evidence for the molecular mechanism of RLR activation.

Description: Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu ) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.

Description: Motivation: In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug–target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known interactions available, its pure ‘local’ model is not able to be learned and hence BLM may fail to make correct prediction when involving such kind of new candidates . Results: We present a simple procedure called neighbor-based interaction-profile inferring (NII) and integrate it into the existing BLM method to handle the new candidate problem. Specifically, the inferred interaction profile is treated as label information and is used for model learning of new candidates. This functionality is particularly important in practice to find targets for new drug-candidate compounds and identify targeting drugs for new target-candidate proteins. Consistent good performance of the new BLM–NII approach has been observed in the experiment for the prediction of interactions between drugs and four categories of target proteins. Especially for nuclear receptors, BLM–NII achieves the most significant improvement as this dataset contains many drugs/targets with no interactions in the cross-validation. This demonstrates the effectiveness of the NII strategy and also shows the great potential of BLM–NII for prediction of compound–protein interactions. Contact: jpmei@ntu.edu.sg Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Punt, A. E., Siddeek, M. S. M., Garber-Yonts, B., Dalton, M., Rugolo, L., Stram, D., Turnock, B. J., and Zheng, J. 2012. Evaluating the impact of buffers to account for scientific uncertainty when setting TACs: application to red king crab in Bristol Bay, Alaska. – ICES Journal of Marine Science, 69: 624–634. Increasingly, scientific uncertainty is being accounted for in fisheries management by implementing an uncertainty buffer, i.e. a difference between the limit catch level given perfect information and the set catch. An approach based on simulation is outlined, which can be used to evaluate the impact of different buffers on short- and long-term catches, discounted revenue, the probability of overfishing (i.e. the catch exceeding the true, but unknown, limit catch), and the stock becoming overfished (i.e. for crab, mature male biomass, MMB, dropping below one-half of the MMB corresponding to maximum sustainable yield). This approach can be applied when only a fraction of the uncertainty related to estimating the limit catch level is quantified through stock assessments. The approach is applied for illustrative purposes to the fishery for red king crab, Paralithodes camtschaticus , in Bristol Bay, AK.