Mouse embryo CoCoPUTs: novel murine transcriptomic-weighted usage website featuring multiple strains, tissues, and stages.

Mouse (Mus musculus) models have been heavily utilized in developmental biology research to understand mammalian embryonic development, as mice share many genetic, physiological, and developmental characteristics with humans. New explorations into the integration of temporal (stage-specific) and transcriptional (tissue-specific) data have expanded our knowledge of mouse embryo tissue-specific gene functions. To better understand the substantial impact of synonymous mutational variations in the cell-state-specific transcriptome on a tissue's codon and codon pair usage landscape, we have established a novel resource-Mouse Embryo Codon and Codon Pair Usage Tables (Mouse Embryo CoCoPUTs).

Advances in methods for tRNA sequencing and quantification.

In the past decade tRNA sequencing (tRNA-seq) has attracted considerable attention as an important tool for the development of novel approaches to quantify highly modified tRNA species and to propel tRNA research aimed at understanding the cellular physiology and disease and development of tRNA-based therapeutics. Many methods are available to quantify tRNA abundance while accounting for modifications and tRNA charging/acylation. Advances in both library preparation methods and bioinformatic workflows have enabled developments in next-generation sequencing (NGS) workflows.

A synonymous variant is unmasked in thalassaemia.

The genetic underpinnings of beta-thalassaemia encompass a myriad of molecular mechanisms. The ability of synonymous mutations, an often-overlooked category of variants, to influence β-globin expression and phenotypic disease is highlighted by this report by Gorivale et al. Commentary on: Gorivale et al.

In silico methods for predicting functional synonymous variants.

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research.

Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies.

Background: Since the onset of the SARS-CoV-2 pandemic, bioinformatic analyses have been performed to understand the nucleotide and synonymous codon usage features and mutational patterns of the virus. However, comparatively few have attempted to perform such analyses on a considerably large cohort of viral genomes while organizing the plethora of available sequence data for a month-by-month analysis to observe changes over time. Here, we aimed to perform sequence composition and mutation analysis of SARS-CoV-2, separating sequences by gene, clade, and timepoints, and contrast the mutational profile of SARS-CoV-2 to other comparable RNA viruses.