Biology subjects

Vahed, M.

Publications and source records attributed to Vahed, 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↗

bioRxiv2021

BML: a versatile web server for bipartite motif discovery

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes effective use of motifs. Here we describe Bipartite Motifs Learning (BML), a web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix (PWM) and dinucleotide weight matrix (DWM), the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/.

bioinformatics↗

bioRxiv2021

Impacts of 203/204: RG>KR mutation in the N protein of SARS-CoV-2

We present a structure-based model of phosphorylation-dependent binding and sequestration of SARS-CoV-2 nucleocapsid protein and the impact of two consecutive amino acid changes R203K and G204R. Additionally, we studied how mutant strains affect HLA-specific antigen presentation and correlated these findings with HLA allelic population frequencies. We discovered RG>KR mutated SARS-CoV-2 expands the ability for differential expression of the N protein epitope on Major Histocompatibility Complexes (MHC) of varying Human Leukocyte Antigen (HLA) origin. The N protein LKR region K203, R204 of wild type (SARS-CoVs) and (SARS-CoV-2) observed HLA-A*30:01 and HLA-A*30:21, but mutant SARS-CoV-2 observed HLA-A*31:01 and HLA-A*68:01. Expression of HLA-A genotypes associated with the mutant strain occurred more frequently in all populations studied. ImportanceThe novel coronavirus known as SARS-CoV-2 causes a disease renowned as 2019-nCoV (or COVID-19). HLA allele frequencies worldwide could positively correlate with the severity of coronavirus cases and a high number of deaths.

bioinformatics↗

bioRxiv2020

Mutation in position of 32 (G>U) of S2M differentiate human SARS-CoV2 from Bat Coronavirus

The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a zoonotic pathogen that has rapidly mutated and become transmissible to humans. There is little existing data on the mutations in SARS-CoV-2 and the impact of these polymorphisms on its transmission and viral load. In this study, the SARS-CoV-2 genomic sequence was analyzed to identify variants within the 3UTR region of its cis-regulatory RNA elements. A 43-nucleotide genetic element with a highly conserved stem-loop II-like motif (S2M), was discovered. The research revealed 32 G>U and 16 G>U/A mutations located within the S2M sequence in human SARS-CoV-2 models. These polymorphisms appear to make the S2M secondary and tertiary structures in human SARS-CoV-2 models less stable when compared to the S2M structures of bat/pangolin models. This grants the RNA structures more flexibility, which could be one of its escape mechanisms from host defenses or facilitate its entry into host proteins and enzymes. While this S2M sequence may not be omnipresent across all human SARS-CoV-2 models, when present, its sequence is always highly conserved. It may be used as a potential target for the development of vaccines and therapeutic agents.

microbiology↗