Many animal species present sex differences. Sex-associated genes (SAGs), which have female-biased or male-biased expression, have major influences on the remarkable sex differences in important traits such as growth, reproduction, disease resistance and behaviors. However, the SAGs resulting in the vast majority of phenotypic sex differences are still unknown. To provide a useful resource for the functional study of SAGs, we manually curated public RNA-seq datasets with paired female and male biological replicates from the same condition and systematically re-analyzed the datasets using standardized methods. We identified 27,793 female-biased SAGs and 64,043 male-biased SAGs from 2,828 samples of 21 species, including human, chimpanzee, macaque, mouse, rat, cow, horse, chicken, zebrafish, seven fly species and five worm species. All these data were cataloged into SAGD, a user-friendly database of SAGs (http://bioinfo.life.hust.edu.cn/SAGD) where users can browse SAGs by gene, species, drug and dataset. In SAGD, the expression, annotation, targeting drugs, homologs, ontology and related RNA-seq datasets of SAGs are provided to help researchers to explore their functions and potential applications in agriculture and human health.

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Abstract:With the development and progress of society and economy in our country, the importance of national prosperity and people's well-being of the state-owned enterprises are more and more outstanding, strengthen economic management of state-owned enterprises, improve economic management and control system is a long-term management goal of state-owned enterprises. In recent years, although the state-owned enterprises have made great achievements in economic management, but with the change of the national economic policy, its shortcomings are gradually exposed. In view of this, this article first introduces the role of economic management in the state-owned enterprises in the development, and then makes an objective analysis of the economic management of the status quo, finally puts forward the measures to strengthen economic management of state-owned enterprises, in order to economic management of state-owned enterprises to successfully carry out help.1. The role of economic management in the development of state-owned enterprises Stability and coordination functionThe state-owned enterprises will follow the current market economy in the course of business rules, through stable and reasonable to carry out their own economic management work orderly reasonable allocation of productive forces of enterprises, in order to promote the optimal allocation of resources within the enterprise and between different regions of the economic activity coordination and dynamic optimization. The level of economic development in different regions often has gaps. Through the development of state-owned enterprises, the complementary advantages of different regions can be realized, thus narrowing the gap between regions. Innovation and breakthrough functionSince the implementation of the policy of reform and opening to the outside world, great progress has been made in our economic construction. At present, China's different regions have formed a unique model of economic development and expanding these features of the mode of economic development not only has great role in promoting the development of regional economy, the national economy progress also play an irreplaceable role. It is precisely because of the existence of this regional economic model that the national economy has maintained the status of innovation and progress and maintained rapid development under the guidance of the national macro-control policy.

Background Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. Results The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89–0.99. Conclusions The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining.

Motivation:Increasing disease causal genes have been identified through different methods, while there are still no uniform biomedical named entity (bio-NE) annotations of the disease phenotypes. Furthermore, semantic similarity comparison between two bio-NE annotations, like disease descriptions, has become important for data integration or system genetics analysis. Methods: The package pyMeSHSim realizes bio-NEs recognition using MetaMap, which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to MeSH, pyMeSHSim embedded a house made dataset containing the Medical Subject Headings (MeSH) main headings (MHs), supplementary concept records (SCRs) and relations between them. Based on the dataset, pyMeSHSim implemented four information content (IC) based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. Results: To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The inclusion of SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts used by pyMeSHSim improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of GWAS phenotypes, pyMeSHSim and previous manual work recognized the same MeSH terms from 276/461 GWAS phenotypes, and the correlation between their semantic similarity calculated by pyMeSHSim and another semantic analysis tool meshes was as high as 0.53-0.97. Conclusion: With the embedded dataset including both MeSH MHs and SCRs, the integrative MeSH tool pyMeSHSim realized the disease recognition, normalization and comparison in biomedical text-mining. Availability: Package's source code and test datasets are available under the GPLv3 license at https://github.com/luozhhub/pyMeSHSim