Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications.

RNA sequencing (RNA-seq) has emerged as a prominent resource for transcriptomic analysis due to its ability to measure gene expression in a highly sensitive and accurate manner. With the increasing availability of RNA-seq data analysis from clinical studies and patient samples, the development of effective visualization tools for RNA-seq analysis has become increasingly important to help clinicians and biomedical researchers better understand the complex patterns of gene expression associated with health and disease. This review aims to outline the current state-of-the-art data visualization techniques and tools commonly used to frame clinical inferences from RNA-seq data and point out their benefits, applications, and limitations.

An intermediate grade of finished genomic sequence suitable for comparative analyses.

Although the cost of generating draft-quality genomic sequence continues to decline, refining that sequence by the process of "sequence finishing" remains expensive. Near-perfect finished sequence is an appropriate goal for the human genome and a small set of reference genomes; however, such a high-quality product cannot be cost-justified for large numbers of additional genomes, at least for the foreseeable future. Here we describe the generation and quality of an intermediate grade of finished genomic sequence (termed comparative-grade finished sequence), which is tailored for use in multispecies sequence comparisons.

Comparative analyses of multi-species sequences from targeted genomic regions.

The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a complement to whole-genome sequencing efforts, we are sequencing and comparing targeted genomic regions in multiple, evolutionarily diverse vertebrates.