Biology subjects

Ping, P.

Publications and source records attributed to Ping, P..

7 recordsLinked to original sources

bioRxiv2024

Data-driven insights into the association between oxidative stress and calcium-regulating proteins in cardiovascular disease.

A growing body of biomedical literature suggests a bidirectional regulatory relationship between cardiac calcium (Ca2+)-regulating proteins and reactive oxygen species (ROS), which is integral to the pathogenesis of various cardiac disorders via oxidative stress signaling. To address the challenge of finding hidden connections within the growing volume of biomedical research, we developed a data science pipeline for efficient data extraction, transformation, and loading. Employing the CaseOLAP (Context-Aware Semantic Analytic Processing) algorithm, our pipeline quantifies interactions between 128 human cardiomyocyte Ca2+-regulating proteins and eight cardiovascular disease (CVD) categories. Our machine learning analysis of CaseOLAP scores reveals that the molecular interfaces of Ca2+-regulating proteins uniquely associate with cardiac arrhythmias and diseases of the cardiac conduction system, distinguishing them from other CVDs. Additionally, a knowledge graph analysis identified 59 of the 128 Ca2+-regulating proteins as involved in OS-related cardiac diseases, with cardiomyopathy emerging as the predominant category. By leveraging a link prediction algorithm, our research illuminates interactions between Ca2+-regulating proteins, OS, and CVDs. The insights gained from our study provide a deeper understanding of the molecular interplay between cardiac ROS and Ca2+-regulating proteins in the context of CVDs. Such understanding is essential for the innovation and development of targeted therapeutic strategies.

bioinformatics↗

bioRxiv2024

How Error Correction Affects PCR Deduplication: A Survey Based on UMI Datasets of Short Reads

Next-Generation Sequencing (NGS) data is widely utilised for various downstream applications in bioinformatics, and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing. This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads. In particular, we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solely-computational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication. Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods. The existing solely-computational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected. All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCR-deduplication process. Upon these discoveries, we offer practical suggestions to enhance the existing computational approaches for improving the quality of short-read sequencing data.

bioinformatics↗

bioRxiv2024

Turn `noise' to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances

Although the per-base erring rate of NGS is very low at 0.1% to 0.5%, the percentage/probability of erroneous reads in a short-read sequencing dataset can be as high as 10% to 15% or in the number of millions. Correction of these wrongly sequenced reads to retrieve their huge missing value will improve many downstream applications. As current methods correct only some of the errors at the cost of introducing many new errors, we solve this problem by turning erroneous reads into their original states, without bringing up any non-existing reads to keep the data integrity. The novelty of our method is originated in a computable rule translated from PCR erring mechanism that: a rare read is erroneous if it has a neighbouring read of high abundance. With this principle, we construct a graph to link every pair of reads of tiny edit distances to detect a solid part of erroneous reads; then we consider them as training data to learn the erring mechanisms to identify possibly remaining hard-case errors between pairs of high-abundance reads. Compared with state-of-the-art methods on tens of datasets of UMI-based ground truth, our method has made a remarkably better performance under 19 metrics including two entropy metrics that measure noise levels in a dataset. Case studies found that our method can make substantial impact on genome abundance quantification, isoform identification, SNP profiling, and genome editing efficiency estimation. For example, the abundance level of the reference genome of SARS-CoV-2 can be increased by 12% and that of Monkeypox can be boosted by 52.12% after error correction. Moreover, the number of distinct isomiRs is decreased by 31.56%, unveiling there are so many previously identified isomiRs that are actually sequencing errors. Author summaryDetecting short-read sequencing errors and correcting the related erroneous reads is a long-standing problem in bioinformatics. Current error correction algorithms correct only small parts of the errors but simultaneously introduce thousands of non-existing sequences. We present a new method to rectify erroneous reads under 300 bp produced by PCR-involved miRNA-sequencing, small RNA sequencing, or paired-end RNA sequencing, regardless of platform or sample type. Our method is the first kind considering the PCR erring mechanism and machine learning technique to improve sequencing data quality by turning millions of erroneous short reads into their original state without bringing up any non-existing sequences into the read set. Our error correction method can make a significant impact on a wide range of cutting-edge downstream applications. The observations and advantages in the case studies lay down strong evidence to question the accuracies of current downstream research outcomes and open new avenues to conduct downstream analysis whenever short-read data are adopted.

bioinformatics↗

bioRxiv2020

Heterogeneity of proteome dynamics between connective tissue phases of adult tendon

Maintenance of connective tissue integrity is fundamental to sustain function, requiring protein turnover to repair damaged tissue. However, connective tissue proteome dynamics remain largely undefined, as do differences in turnover rates of individual proteins in the collagen and glycoprotein phases of connective tissue extracellular matrix (ECM). Here, we investigate proteome dynamics in the collagen and glycoprotein phases of connective tissues by exploiting the spatially distinct fascicular (collagen-rich) and interfascicular (glycoprotein-rich) ECM phases of tendon. Using isotope labelling, mass spectrometry and bioinformatics, we calculate turnover rates of individual proteins within rat Achilles tendon and its ECM phases. Our results demonstrate complex proteome dynamics in tendon, with ~1000-fold differences in protein turnover rates, and overall faster protein turnover within the glycoprotein-rich interfascicular matrix compared to the collagen-rich fascicular matrix. These data provide insights into the complexity of proteome dynamics in tendon, likely required to maintain tissue homeostasis.

biochemistry↗

bioRxiv2018

Quantifying the impact of public omics data

The amount of omics data in the public domain is increasing every year [1, 2]. Public availability of datasets is growing in all disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility), and/or it is mandated by funding agencies, scientific journals, etc. Science is now a data intensive discipline and therefore, new and innovative ways for data management, data sharing, and for discovering novel datasets are increasingly required [3, 4]. However, as data volumes grow, quantifying its impact becomes more and more important. In this context, the FAIR (Findable, Accessible, Interoperable, Reusable) principles have been developed to promote good scientific practises for scientific data and data resources [5]. In fact, recently, several resources [1, 2, 6] have been created to facilitate the Findability (F) and Accessibility (A) of biomedical datasets. These principles put a specific emphasis on enhancing the ability of both individuals and software to discover and re-use digital objects in an automated fashion throughout their entire life cycle [5]. While data resources typically assign an equal relevance to all datasets (e.g. as results of a query), the usage patterns of the data can vary enormously, similarly to other \"research products\" such as publications. How do we know which datasets are getting more attention? More generally, how can we quantify the scientific impact of datasets?

bioinformatics↗

bioRxiv2018

Quantitative Temporal Analysis of Protein Dynamics in Maladaptive Cardiac Remodeling

Maladaptive cardiac remodeling (MCR) is a complex dynamic process common to many heart diseases. MCR is characterized as a temporal progression of global adaptive and maladaptive perturbations. The complex nature of this process clouds a comprehensive understanding of MCR, but greater insight into the processes and mechanisms has the potential to identify new therapeutic targets. To provide a deeper understanding of this important cardiac process, we applied a new proteomic technique, PALM (Pulse Azidohomoalanine in Mammals), to quantitate the newly-synthesized protein (NSP) changes during the progression of isoproterenol (ISO)-induced MCR in the mouse left ventricle. This analysis revealed a complex combination of adaptive and maladaptive alterations at acute and prolonged time points including the identification of proteins not previously associated with MCR. We also combined the PALM dataset with our published protein turnover rate dataset to identify putative biochemical mechanisms underlying MCR. The novel integration of analyzing NSPs together with their protein turnover rates demonstrated that alterations in specific biological pathways (e.g., inflammation and oxidative stress) are produced by differential regulation of protein synthesis and degradation.

pathology↗

bioRxiv2017

Uncovering Medical Insights from Vast Amounts of Biomedical Data in Clinical Case Reports

Clinical case reports (CCRs) have a time-honored tradition in serving as an important means of sharing clinical experiences on patients presenting with atypical disease phenotypes or receiving new therapies. However, the huge amount of accumulated case reports are isolated, unstructured, and heterogeneous clinical data, posing a great challenge to clinicians and researchers in mining relevant information through existing indexing tools. In this investigation, in order to render CCRs more findable, accessible, interoperable, and reusable (FAIR) by the biomedical community, we created a resource platform, including the construction of a test dataset consisting of 1000 CCRs spanning 14 disease phenotypes, a standardized metadata template and metrics, and a set of computational tools to automatically retrieve relevant medical information and to analyze all published PubMed clinical case reports with respect to trends in publication journals, citations impact, MeSH Terms, drug use, distributions of patient demographics, and relationships with other case reports and databases. Our standardized metadata template and CCR test dataset may be valuable resources to advance medical science and improve patient care for researchers who are using machine learning approaches with a high-quality dataset to train and validate their algorithms. In the future, our analytical tools may be applied towards other large clinical data sources as well.

bioinformatics↗