A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics.

The DEEP cohort is the first population-based cohort of pregnant population in China that longitudinally documented drug uses throughout the pregnancy life course and adverse pregnancy outcomes. The main goal of the study aims to monitor and evaluate the safety of drug use through the pregnancy life course in the Chinese setting. The DEEP cohort is developed primarily based on the population-based data platforms in Xiamen, a municipal city of 5 million population in southeast China. Based on these data platforms, we developed a pregnancy database that documented health care services and outcomes in the maternal and other departments. For identifying drug uses, we developed a drug prescription database using electronic healthcare records documented in the platforms across the primary, secondary and tertiary hospitals. By linking these two databases, we developed the DEEP cohort. All the pregnant women and their offspring in Xiamen are provided with health care and followed up according to standard protocols, and the primary adverse outcomes - congenital malformations - are collected using a standardized Case Report Form. From January 2013 to December 2021, the DEEP cohort included 564,740 pregnancies among 470,137 mothers, and documented 526,276 live births, 14,090 miscarriages and 6,058 fetal deaths/stillbirths and 25,723 continuing pregnancies. In total, 13,284,982 prescriptions were documented, in which 2,096 chemicals drugs, 163 biological products, 847 Chinese patent medicines and 655 herbal medicines were prescribed. The overall incidence rate of congenital malformations was 2.0% (10,444/526,276), while there were 25,526 (4.9%) preterm births and 25,605 (4.9%) live births with low birth weight.

A real-world data analysis of acetylsalicylic acid in FDA Adverse Event Reporting System (FAERS) database.

BACKGROUND: Acetylsalicylic acid (Aspirin), one of the oldest medicines, is widely used in various clinical fields. However, numerous adverse events (AEs) have been reported. In this study, we aimed to investigate adverse drug reactions (ADRs) of aspirin using real-worlddata from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: We assessed the disproportionality of aspirin-related AEs by calculating measures such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagationneural network (BCPNN), and Gamma-Poisson Shrinker (GPS). RESULTS: Out of 7,510,564 casereports in the FAERS database, 18644 reports of aspirin as the 'primary suspected (PS)' AEs were recorded. Disproportionality analyses identified 493 aspirin-related preferred terms (PTs) across 25 organ systems. Notably, unexpected significant AEs such as pallor (p=5.66E-33), dependence (p=6.45E-67), and compartment syndrome (p=1.95E-28) were observed, which were not mentioned in the drug's instructions. CONCLUSION: Our findings align with clinical observations, highlighting potential new and unexpected ADR signals associated with aspirin. Further prospective clinical studies are necessary to confirm and elucidate the relationship between aspirin and these ADRs. This study offers a fresh and unique perspective for studying drug-AEs.

A real-world data analysis of topotecan in the FDA Adverse Event Reporting System (FAERS) database.

OBJECTIVES: The objective of this study was to monitor and identify adverse events (AEs) associated with topotecan, a medication used for the treatment of solid tumors, in order to improve patient safety and guide medication usage. METHODS: To assess the disproportionality of topotecan-related AEs in real-world data, four algorithms (ROR, PRR, BCPNN, and EBGM) were employed as measures to detect signals of topotecan-associated AEs. RESULTS: A statistical analysis was conducted using data from the FAERS database, encompassing 9,511,161 case reports from 2004Q1 to 2021Q4. Among these reports, 1,896 were identified as primary suspected (PS) AEs related to topotecan, and 155 topotecan-related adverse drug reactions (ADRs) at the preferred terms (PTs) level were selected. The occurrence of topotecan-induced ADRs was analyzed across 23 organ systems. The analysis revealed several expected ADRs, such as anemia, nausea, and vomiting, which were consistent with the drug labels. Additionally, unexpected significant ADRs associated with eye disorders at the system organ class (SOC) level were identified, indicating potential adverse effects not currently mentioned in the drug instructions. CONCLUSION: This study identified new and unexpected signals of adverse drug reactions (ADRs) related to topotecan, providing valuable insights into the relationship between ADRs and topotecan usage. The findings highlight the importance of ongoing monitoring and surveillance to detect and manage AEs effectively, ultimately improving patient safety during topotecan treatment.

A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data.

Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.

Modeling of Genome-Wide Polyadenylation Signals in Xenopus tropicalis.

Alternative polyadenylation (APA) is an important post-transcriptional modification event to process messenger RNA (mRNA) for transcriptional termination, transport, and translation. In the present study, we characterized poly(A) signals in Xenopus tropicalis using 70,918 highly confident poly(A) sites derived from 16,511 protein-coding genes to understand their roles in the regulation of embryo development and gender difference. We examined potential factors, including the gene length, the number of introns in a gene, and the intron length, that may affect the prevalence of APA. We observed 12 prominent poly(A) signal patterns, which accounted for approximately 92% of total APA sites in Xenopus tropicalis. Among them, three patterns are specific to X. tropicalis, so they are absent in other animals such as humans or mice. We catalogued APA sites based on their genomic regions and developed a bioinformatics pipeline to identify over-represented signal patterns for each class. Then the schema of cis elements for APA sites in each genomic region was proposed. More importantly, APA usage is dramatically dynamic in embryos along five developmental stages and well-coordinated with the maternal-to-zygotic transition event. We used an entropy-based method to identify developmental stage-specific APA sites and identified significant signal patterns around specific sites and constitutive sites. We found that the APA frequency in different genomic regions varies with developmental stages and that those sites located in intron or coding sequence regions contribute most to the dynamics of gene expression during developmental stages. This study deciphers the characteristics and poly(A) signal patterns for both canonical APA sites and non-canonical APA sites across different developmental stages and gender dimorphisms in X. tropicalis, providing new insights into the dynamic regulation of distal and proximal APA.

AStrap: identification of alternative splicing from transcript sequences without a reference genome.

SUMMARY: Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. AVAILABILITY AND IMPLEMENTATION: AStrap is available for download at https://github.com/BMILAB/AStrap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TSAPA: identification of tissue-specific alternative polyadenylation sites in plants.

Summary: Alternative polyadenylation (APA) is now emerging as a widespread mechanism modulated tissue-specifically, which highlights the need to define tissue-specific poly(A) sites for profiling APA dynamics across tissues. We have developed an R package called TSAPA based on the machine learning model for identifying tissue-specific poly(A) sites in plants. A feature space including more than 200 features was assembled to specifically characterize poly(A) sites in plants. The classification model in TSAPA can be customized by selecting desirable features or classifiers. TSAPA is also capable of predicting tissue-specific poly(A) sites in unannotated intergenic regions. TSAPA will be a valuable addition to the community for studying dynamics of APA in plants. Availability and implementation: https://github.com/BMILAB/TSAPA. Supplementary information: Supplementary data are available at Bioinformatics online.

AEGS: identifying aberrantly expressed gene sets for differential variability analysis.

Motivation: In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g. the changes in expression variance) between groups may reveal underlying genetic heterogeneity and undetected interactions, which has great implications in many fields of biology. An easy-to-use tool for DV analysis is needed. Results: We develop AEGS for DV analysis, to identify aberrantly expressed gene sets in diseased cases but not in controls. AEGS can rank individual genes in an aberrantly expressed gene set by each gene's relative contribution to the total degree of aberrant expression, prioritizing top genes. AEGS can be used for discovering gene sets with disease-specific expression variability changes. Availability and implementation: AEGS web server is accessible at http://bmi.xmu.edu.cn:8003/AEGS, where a stand-alone AEGS application can also be downloaded. Contact: glji@xmu.edu.cn.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.