Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis

Abstract

Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

Human Induced Pluripotent Stem Cells as a Platform for Personalized and Precision Cardiovascular Medicine

“…RNAseq is also not limited to the quantification of mRNA transcripts; depending on the methods used for RNA isolation and library preparation, it can sequence other RNA populations such as miRNA, tRNA, or ribosome-bound RNA to detect mRNA actively being translated into proteins (108). The ability to measure the abundance of different RNAs has in recent years refined the investigation of a variety of biological problems, including optimizing experimental conditions (e.g., drug dose and timing), time-course experiments (e.g., to monitor developmental processes, or disease progression), population-specific response profiling (e.g., in healthy and diseased individuals), studying response to treatment (e.g., to several different drugs), and performing large-scale RNA interference (RNAi) studies (249).…”

The design of experiments for the transcriptome studies by high-throughput sequencing methods

“…Для выявления целевых дифференциально экспресси-руемых генов (как и при количественной оценке экспрес-сии отдельных генов методом qRT-PCR) целесообразно оценивать необходимое число образцов в каждой группе, используя классический анализ статистической мощности и учитывая ожидаемую вариабельность уровня исследуе-мых транскриптов (Karlen et al, 2007;Spies, Ciaudo, 2015). Для идентификации практически всех мРНК, уровень которых изменился в ≥ 1,5 раза и средняя копийность которых выше, чем один транскрипт на клетку, оптималь-ным размером группы будет пять -восемь образцов на экспериментальную группу (Karlen et al, 2007).…”

“…Единственные правила проведения экспериментов (правила консорциума ENCODE -«Stan-dards, Guidelines and Best Practices for RNA-Seq», редак-ция 1.0) были сформированы на основе данных, полу-ченных при изучении транскриптомов у млекопитающих, и не обновлялись с 2011 г. (ENCODE 2011;Spies, Ciaudo, 2015). В этом обзоре проанализирован ряд важ ных во-просов планирования полногеномных экспериментов с использованием методов RNA-Seq и Ribo-Seq, а также консолидированы ранее опубликованные советы по пла-нированию транскриптомных экспериментов, которые до настоящего времени были представлены в разрозненном виде в ключевых публикациях и обзорах (например, Corney, 2013;Hart et al, 2013a, b).…”

DynOmics to identify delays and co-expression patterns across time course experiments

“…Targeted co-expression analysis can also be performed using various model-based applications to retrieve data sets from databases given specific query data171819. Finally, a third category of methods was proposed based on biological pathway analysis2021 see the detailed review of Spies et al 22…”