• Animal Bioscience
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• v.34 no.9
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• pp.1439-1450
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• 2021

Objective: With the rapid development of proteomics sequencing and RNA sequencing technology, multi-omics analysis has become a current research hotspot. Our previous study indicated that Xinjiang brown cattle have better meat quality than Kazakh cattle. In this study, Xinjiang brown cattle and Kazakh cattle were used as the research objects. Methods: Proteome sequencing and RNA sequencing technology were used to analyze the proteome and transcriptome of the longissimus dorsi muscle of the two breeds of adult steers (n = 3). Results: In this project, 22,677 transcripts and 1,874 proteins were identified through quantitative analysis of the transcriptome and proteome. By comparing the identified transcriptome and proteome, we found that 1,737 genes were identified at both the transcriptome and proteome levels. The results of the study revealed 12 differentially expressed genes and proteins: troponin I1, crystallin alpha B, cysteine, and glycine rich protein 3, phosphotriesterase-related, myosin-binding protein H, glutathione s-transferase mu 3, myosin light chain 3, nidogen 2, dihydropyrimidinase like 2, glutamate-oxaloacetic transaminase 1, receptor accessory protein 5, and aspartoacylase. We performed functional enrichment of these differentially expressed genes and proteins. The Kyoto encyclopedia of genes and genomes results showed that these differentially expressed genes and proteins are enriched in the fatty acid degradation and histidine metabolism signaling pathways. We performed parallel reaction monitoring (PRM) verification of the differentially expressed proteins, and the PRM results were consistent with the sequencing results. Conclusion: Our study provided and identified the differentially expressed genes and proteins. In addition, identifying functional genes and proteins with important breeding value will provide genetic resources and technical support for the breeding and industrialization of new genetically modified beef cattle breeds.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.547-554
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• 2021

Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes is an important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods have been proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Network based Patient-specific Driver gene identification) for identifying patient-specific cancer driver genes. NPD consists of three steps, constructing a patient-specific gene network, applying the modified PageRank algorithm to assign scores to genes, and identifying cancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPD showed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.