Biology subjects

Guo, F.-B.

Publications and source records attributed to Guo, F.-B..

5 recordsLinked to original sources

bioRxiv2025

The Role of Primate-Specific Genes and Network Dynamics in Cancer Evolution

The atavistic evolutionary theory of cancer evolution highlights the role of ancient and unicell-originated genes, and comprehensive research demonstrated the evolutionary inference. Comparably, very few reports linked young genes with cancer development and maintenance. Here, we found that in cancer cell lines a higher proportion of core essential genes was present among primate-specific genes (PSG) than early metazoan-originated genes (Wilcoxon rank-sum test, p-value = 4.67e-12) and mammal-originated genes (Wilcoxon rank-sum test, p-value = 1.22e-16). Additionally, we found that the loss of many co-expression gene pairs in pan-cancer leads to their network becoming looser compared with normal tissue. However, PSGs particularly those being essential, exhibit a dynamic increase in co-expression, explaining their enhanced role in maintaining cancers. Furthermore, clustering of the gene co-expressions brings a cancer-exclusive module, which contains a large number of PSG connections. We also demonstrate the divergence between essential genes in healthy individuals and those in cancer cells. Our findings complement the atavistic theory in elucidating the evolutionary process of cancer.

evolutionary biology↗

bioRxiv2020

Precise detection of Acrs in prokaryotes using only six features

Anti-CRISPR proteins (Acrs) can suppress the activity of CRISPR-Cas systems. Some viruses depend on Acrs to expand their genetic materials into the host genome which can promote species diversity. Therefore, the identification and determination of Acrs are of vital importance. In this work we developed a random forest tree-based tool, AcrDetector, to identify Acrs in the whole genomescale using merely six features. AcrDetector can achieve a mean accuracy of 99.65%, a mean recall of 75.84%, a mean precision of 99.24% and a mean F1 score of 85.97%; in multi-round, 5-fold cross-validation (30 different random states). To demonstrate that AcrDetector can identify real Acrs precisely at the whole genome-scale we performed a cross-species validation which resulted in 71.43% of real Acrs being ranked in the top 10. We applied AcrDetector to detect Acrs in the latest data. It can accurately identify 3 Acrs, which have previously been verified experimentally. A standalone version of AcrDetector is available at https://github.com/RiversDong/AcrDetector. Additionally, our result showed that most of the Acrs are transferred into their host genomes in a recent stage rather than early.

bioinformatics↗

bioRxiv2018

CasLocusAnno: a web-based server for annotating Cas loci and their corresponding (sub)types

CRISPR-Cas systems are prevalent in bacterial and archaeal genomes, and these systems provide a powerful adaptive immune system against predation by phages and other mobile genetic elements (MGEs). They also contribute to other functions, such as gene regulation in prokaryotic organisms. Determining Cas proteins and Cas loci can help mine Cas proteins and facilitate the identification of Cas-associated accessory proteins. Therefore, the purpose of this work is to develop a web-based server, CasLocusAnno, to annotate Cas proteins and Cas loci and to classify them according to (sub)type based on a previous study. CasLocusAnno can annotate Cas proteins and Cas loci and assign their (sub)types within {bsim}28 seconds for whole protein sequence submissions, with protein sequence numbers ranging from {bsim}30 to {bsim}10500. Comparison with Makarova et al.s benchmark data demonstrates that CasLocusAnno can accurately identify Cas loci and (sub)types. In addition, CasLocusAnno can identify Cas proteins with higher accuracy and a lower additional prediction rate (APR) than two excellent software programs, CRISPRCasFinder and MacSyFinder. The domain alignment of a Cas protein can be easily browsed in the annotation results. Our server can be freely accessed at http://cefg.uestc.edu.cn/CasLocusAnno/.

bioinformatics↗

bioRxiv2017

Co-adaption of tRNA Gene Copy Number and Amino Acid Usage Influences Translation Rates in Three Life Domains

The cellular translation process should obey the principle of maximizing efficiency and minimizing resource and energy costs. Here, we validated this principle by focusing on the basic translation components of tRNAs and amino acids. To most efficiently utilize these components, we reasoned that the quantities of the 20 tRNAs and their corresponding amino acids would be consistent in an organism. The two values should match at both the organismal and protein scales. For the former, they co-vary to meet the need to translate more proteins in fast-growing or larger cells. For the latter, they are consistent to different extents for various proteins in an organism to comply with different needs of translation speed. In this work, 310 out of 410 genomes in three domains had significant co-adaptions between the tRNA gene copy number and amino acid composition, and thus validating the principle at the organism scale. Furthermore, fast-growing bacteria co-adapt better than slow-growing ones. Highly expressed proteins and those connected to acute responses have better co-adaption, illustrating the principle at the individual protein scale. Experimentally, manipulating the tRNA gene copy number to optimize co-adaption between enhanced green fluorescent protein (EGFP) and tRNA gene set of Escherichia coli indeed lifted the translation rate (speed). Our results also contribute to revealing a translation rate-associated factor with universal and global effects. From a practical perspective, our findings suggest a strategy to increase the expression of target proteins and have implications for designing chassis cells in the field of synthetic biology field.

genomics↗

bioRxiv2016

Accurate prediction of human essential genes using only nucleotide composition and association information

Three groups recently identified essential genes in human cancer cell lines using wet experiments, and these genes are of high values. Herein, we improved the widely used Z curve method by creating a {lambda}-interval Z curve, which considered interval association information. With this method and recursive feature elimination technology, a computational model was developed to predict human gene essentiality. The 5-fold cross-validation test based on our benchmark dataset obtained an area under the receiver operating characteristic curve (AUC) of 0.8814. For the rigorous jackknife test, the AUC score was 0.8854. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. However, previous classifiers in three eukaryotes can gave satisfactory prediction only combining sequence with other features. It is also demonstrated that although the information contributed by interval association is less than adjacent nucleotides, this information can still play an independent role. Integrating the interval information into adjacent ones can significantly improve our classifiers prediction capacity. We re-predicted the benchmark negative dataset by Pheg server (https://cefg.uestc.edu.cn/Pheg), and 118 genes were additionally predicted as essential. Among them, 21 were found to be homologues in mouse essential genes, indicating that at least a part of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the {lambda}-interval Z curve method could be effectively extended to classification issues of other DNA elements.

bioinformatics↗