QuaPra: Efficient transcript assembly and quantification using quadratic programming with Apriori algorithm

Gene expression variability across cells and species shapes the relationship between renal resident macrophages and infiltrated macrophages

BACKGROUND

Two main subclasses of macrophages are found in almost all solid tissues: embryo-derived resident tissue macrophages and bone marrow-derived infiltrated macrophages. These macrophage subtypes show transcriptional and functional divergence, and the programs that have shaped the evolution of renal macrophages and related signaling pathways remain poorly understood. To clarify these processes, we performed data analysis based on single-cell transcriptional profiling of renal tissue-resident and infiltrated macrophages in human, mouse and rat.

RESULTS

In this study, we (i) characterized the transcriptional divergence among species and (ii) illustrated variability in expression among cells of each subtype and (iii) compared the gene regulation network and (iv) ligand-receptor pairs in human and mouse. Using single-cell transcriptomics, we mapped the promoter architecture during homeostasis.

CONCLUSIONS

Transcriptionally divergent genes, such as the differentially TF-encoding genes expressed in resident and infiltrated macrophages across the three species, vary among cells and include distinct promoter structures. The gene regulatory network in infiltrated macrophages shows comparatively better species-wide consistency than resident macrophages. The conserved transcriptional gene regulatory network in infiltrated macrophages among species is uniquely enriched in pathways related to kinases, and TFs associated with largely conserved regulons among species are uniquely enriched in kinase-related pathways.

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients' compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).

AGTAR: A novel approach for transcriptome assembly and abundance estimation using an adapted genetic algorithm from RNA-seq data

BACKGROUND

Recently, the rapid development of RNA-seq technologies has accelerated transcriptomics research. The accurate identification and quantification of transcripts based on RNA-seq data will facilitate the exploration of various potential biological mechanisms. However, due to the limitations of the current data analysis tools and RNA-seq technologies, full and accurate reconstruction of the transcriptome still faces many challenges.

RESULTS

We developed the adapted genetic algorithm (AGTAR) program, which can reliably assemble transcriptomes and estimate abundance based on RNA-seq data with or without genome annotation files. We defined a new concept, isoform junction abundance, to help enhance the accuracy of isoform identification and quantification. Isoform abundance and isoform junction abundance are estimated by an adapted genetic algorithm. The crossover and mutation probabilities of the algorithm can be adaptively adjusted to effectively prevent premature convergence. Both simulated and real data indicated that AGTAR's comprehensive ability to assemble transcripts is significantly superior to that achievable by the currently widely used tools with similar functions.

CONCLUSIONS

AGTAR is a tool for identifying and quantifying transcripts from RNA-seq data. It has the advantages of higher accuracy and ease of use. The AGTAR package is freely available at https://github.com/v4yuezi/AGTAR.git.

A comprehensive rat transcriptome built from large scale RNA-seq-based annotation

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

DNMIVD: DNA methylation interactive visualization database

Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

Co-occurrence and Mutual Exclusivity Analysis of DNA Methylation Reveals Distinct Subtypes in Multiple Cancers

Co-occurrence and mutual exclusivity (COME) of DNA methylation refer to two or more genes that tend to be positively or negatively correlated in DNA methylation among different samples. Although COME of gene mutations in pan-cancer have been well explored, little is known about the COME of DNA methylation in pan-cancer. Here, we systematically explored the COME of DNA methylation profile in diverse human cancer. A total of 5,128,332 COME events were identified in 14 main cancers types in The Cancer Genome Atlas (TCGA). We also identified functional epigenetic modules of the zinc finger gene family in six cancer types by integrating the gene expression and DNA methylation data and the frequently occurred COME network. Interestingly, most of the genes in those functional epigenetic modules are epigenetically repressed. Strikingly, those frequently occurred COME events could be used to classify the patients into several subtypes with significant different clinical outcomes in six cancers as well as pan-cancer (p-value ≤ = 0.05). Moreover, we observed significant associations between different COME subtypes and clinical features (e.g., age, gender, histological type, neoplasm histologic grade, and pathologic stage) in distinct cancers. Taken together, we identified millions of COME events of DNA methylation in pan-cancer and detected functional epigenetic COME events that could separate tumor patients into different subtypes, which may benefit the diagnosis and prognosis of pan-cancer.

Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.