Comprehensive Peptide Mapping Is Crucial for Proteogenomics and Proteomics.

Proteogenomics enables the confirmation and refinement of gene models, the detection of new ones, and the proposition of alternative transcripts using support at the protein level. Such evidence is usually generated using mass spectrometry and subsequent result mapping to various sequence databases. This workflow entails several problems: (1) To speed up the analysis, only a small set of expected proteins is searched; (2) database search tools generally do not provide mapping to the genome; and (3) upon new releases of the sequence databases, expensive rerunning of all results would need to be performed.

De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics.

The changes in protein expression are hallmarks of development and disease. Protein expression can be established qualitatively and quantitatively using mass spectrometry (MS). Samples are prepared, proteins extracted and then analyzed using MS and MS/MS.

The Role of MicroRNAs in HIV Infection.

MicroRNAs (miRNAs), a class of small, non-coding RNAs, play a pivotal role in regulating gene expression at the post-transcriptional level. These regulatory molecules are integral to many biological processes and have been implicated in the pathogenesis of various diseases, including Human Immunodeficiency Virus (HIV) infection. This review aims to cover the current understanding of the multifaceted roles miRNAs assume in the context of HIV infection and pathogenesis.

Deep learning in bioinformatics.

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis.

Alternative polyadenylation and dynamic 3' UTR length is associated with polysome recruitment throughout the cardiomyogenic differentiation of hESCs.

Alternative polyadenylation (APA) increases transcript diversity through the generation of isoforms with varying 3' untranslated region (3' UTR) lengths. As the 3' UTR harbors regulatory element target sites, such as miRNAs or RNA-binding proteins, changes in this region can impact post-transcriptional regulation and translation. Moreover, the APA landscape can change based on the cell type, cell state, or condition.