RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

The SEQC2 epigenomics quality control (EpiQC) study.

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Origin of the Induced Pluripotent Stem Cells Affects Their Differentiation into Dopaminergic Neurons.

Neuronal differentiation of human induced pluripotent stem (iPS) cells, both in 2D models and 3D systems in vitro, allows for the study of disease pathomechanisms and the development of novel therapies. To verify if the origin of donor cells used for reprogramming to iPS cells can influence the differentiation abilities of iPS cells, peripheral blood mononuclear cells (PBMC) and keratinocytes were reprogrammed to iPS cells using the Sendai viral vector and were subsequently checked for pluripotency markers and the ability to form teratomas in vivo. Then, iPS cells were differentiated into dopaminergic neurons in 2D and 3D cultures. Both PBMC and keratinocyte-derived iPS cells were similarly reprogrammed to iPS cells, but they displayed differences in gene expression profiles and in teratoma compositions in vivo. During 3D organoid formation, the origin of iPS cells affected the levels of FOXA2 and LMX1A only in the first stages of neural differentiation, whereas in the 2D model, differences were detected at the levels of both early and late neural markers FOXA2, LMX1A, NURR1, TUBB and TH. To conclude, the origin of iPS cells may significantly affect iPS differentiation abilities in teratomas, as well as exerting effects on 2D differentiation into dopaminergic neurons and the early stages of 3D midbrain organoid formation.