Biology subjects

Patel, Y. M.

Publications and source records attributed to Patel, Y. M..

4 recordsLinked to original sources

bioRxiv2023

Analytical code sharing practices in biomedical research

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71x10-9). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10-07). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

scientific communication and education↗

bioRxiv2022

Data availability of open T-cell receptor repertoire data, a systematic assessment

The improvement of next-generation sequencing technologies has promoted the field of immunogenetics and produced numerous immunogenomics data. Modern data-driven research has the power to promote novel biomedical discoveries through secondary analysis of such data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific and articulate writing, reporting and interpreting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability (p=0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCR-Seq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community.

scientific communication and education↗

bioRxiv2019

A Pro-Diabetogenic mtDNA Polymorphism in the Mitochondrial-Derived Peptide, MOTS-c

Type 2 Diabetes (T2D) is an emerging public health problem in Asia. An Asian mitochondrial DNA variation m.1382A>C (rs111033358) leads to a K14Q amino acid replacement in MOTS-c, an insulin sensitizing mitochondrial-derived peptide. Meta-analysis of three cohorts (n=27,527, J-MICC, MEC, and TMM) showed that males but not females with the C-allele exhibit a higher prevalence of T2D. Furthermore, in J-MICC, only males with the C-allele in the lowest tertile of physical activity increased their prevalence of T2D, demonstrating a kinesio-genomic interaction. High-fat fed, male mice injected with MOTS-c showed reduced weight and improved glucose tolerance, but not K14Q-MOTS-c treated mice. Like the human data, female mice were unaffected. Mechanistically, K14Q-MOTS-c leads to diminished insulin-sensitization in vitro. Thus, the m.1382A>C polymorphism is associated with susceptibility to T2D in men, possibly interacting with exercise, and contributing to the risk of T2D in sedentary males by reducing the activity of MOTS-c.

genetics↗

bioRxiv2017

Genetic Diversity Turns a New PAGE in Our Understanding of Complex Traits

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

genetics↗