RNA-Seq is a high-throughput sequencing technology that allows researchers to analyze the genomes of individual cells, providing valuable insights into cellular heterogeneity. Together with metagenomics, it enables comprehensive evaluation of single-cell RNA sequencing data and the discovery of novel enzymes from diverse microbial metagenomes.
Explore the following trending articles in Frontiers in Genetics and Frontiers in Systems Biology.
Single-cell RNA sequencing (scRNA-seq) data are high-dimensional, noisy, and sparse data. This study presents a comprehensive evaluation of 10 dimensionality reduction methods of these data.
This article reviews metagenomics, emphasizing its significance in discovering novel enzymes from diverse microbial communities, discussing the application of Next-Generation Sequencing (NGS) and advanced bioinformatic techniques.
This article reviews evidence of the proximal and long-distance epigenetic mechanisms controlling circadian transcription in health and disease.
This article reviews deep learning techniques and various applications of deep learning to genomic studies. It also discusses current challenges and future perspectives on using emerging deep learning techniques for ongoing and future genomic research.
This review highlights the most important points necessary for successful genome-wide association studies of viral infectious diseases, focusing on the study design and various statistical methods used.
This review summarizes the recent advances in multimodal single-cell technologies and discusses the challenges and opportunities of the field.
Read other impactful articles published in Frontiers in Genetics and Frontiers in Systems Biology