What is rna seq

BMC Genomics 16 , Lamarre, S. Optimization of an RNA-Seq differential gene expression analysis depending on biological replicate number and library size. Plant Sci. Hansen, K. Sequencing technology does not eliminate biological variability. Required reading for anyone considering RNA-seq or other -omics technologies.

A well-written reminder of why quantitative RNA experiments will always need replicates, even if RNA assay technologies were perfect. The authors caution users against being over-enthusiastic about new technologies and discarding lessons learned about experimental design. Norton, S. Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates.

Bioinformatics 34 , — Busby, M. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 29 , — Wu, Z. Wu, H. Bioinformatics 31 , — Gaye, A. Schurch, N. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Montgomery, S. Transcriptome genetics using second generation sequencing in a Caucasian population. Conesa, A. A survey of best practices for RNA-seq data analysis. An overview of computational tools and methods used in RNA-seq analysis. Lei, R. Diminishing returns in next-generation sequencing NGS transcriptome data. Gene , 82—87 Li, B. BMC Bioinformatics 12 , Chhangawala, S. The impact of read length on quantification of differentially expressed genes and splice junction detection. Katz, Y.

Analysis and design of RNA sequencing experiments for identifying isoform regulation. Alamancos, G. Methods to study splicing from high-throughput RNA sequencing data. Seyednasrollah, F. Comparison of software packages for detecting differential expression in RNA-seq studies. Williams, C. Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-seq.

BMC Bioinformatics 18 , 38 A useful overview of several popular computational analysis tools and how they can be used in combination. Cock, P. Kim, D. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Dobin, A. Bioinformatics 29, 15—21 HISAT: a fast spliced aligner with low memory requirements.

Methods 12 , — Trapnell, C. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.

Pertea, M. Xie, Y. Bioinformatics 30 , — Patro, R. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Bray, N. Near-optimal probabilistic RNA-seq quantification. Salmon provides fast and bias-aware quantification of transcript expression. Wu, D. Limitations of alignment-free tools in total RNA-seq quantification.

A useful comparison of popular mRNA-seq analysis methods, with particular emphasis on alignment-free tools. Yang, C. The impact of RNA-seq aligners on gene expression estimation. Errors in RNA-Seq quantification affect genes of relevance to human disease. An experimental demonstration of the importance of read mapping and quantification in the computational analysis of mRNA-seq experiments.

This paper clearly describes the impact that different alignments and quantification methods can have on biological conclusions. Zytnicki, M. BMC Bioinformatics 18 , McDermaid, A.

A new machine learning-based framework for mapping uncertainty analysis in RNA-Seq read alignment and gene expression estimation. Fonseca, N. RNA-Seq gene profiling — a systematic empirical comparison. Teng, M. A benchmark for RNA-seq quantification pipelines. Quinn, T. Benchmarking differential expression analysis tools for RNA-Seq: normalization-based versus log-ratio transformation-based methods. BMC Bioinformatics 19 , Vijay, N. Challenges and strategies in transcriptome assembly and differential gene expression quantification.

A comprehensive in silico assessment of RNA-seq experiments. Soneson, C. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. Turro, E. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads.

Anders, S. HTSeq — a Python framework to work with high-throughput sequencing data. Liao, Y. Risso, D. GC-content normalization for RNA-seq data.

Wagner, G. Theory Biosci. Normalization of RNA-seq data using factor analysis of control genes or samples. Bourgon, R. Independent filtering increases detection power for high-throughput experiments. Bullard, J. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC 11 , 94— Dillies, M. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Li, X. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.

Robinson, M. A scaling normalization method for differential expression analysis of RNA-seq data. Bioinformatics 26 , — Chen, K. The overlooked fact: fundamental need for spike-in control for virtually all genome-wide analyses.

Hardwick, S. Reference standards for next-generation sequencing. A review of the use of spike-in controls and their associated statistical principles. It introduces readers to the concept of commutability: the ability of a spike-in control to perform comparably to experimental RNA samples.

Pine, P. BMC Biotechnol. Paul, L. Spliced synthetic genes as internal controls in RNA sequencing experiments. Methods 13 , — Revisiting global gene expression analysis. Qing, T. China Life Sci. Leshkowitz, D. Using synthetic mouse spike-in transcripts to evaluate RNA-seq analysis tools.

Lun, A. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.

Ritchie, M. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Love, M. Law, C. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Frazee, A. Ballgown bridges the gap between transcriptome assembly and expression analysis. Rapaport, F. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.

Montoro, D. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Asp, M. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Tang, F. Methods 6 , — Stegle, O. Computational and analytical challenges in single-cell transcriptomics. This review provides an overview and in-depth discussion of scRNA-seq transcript quantitation methods. The authors highlight the analytical challenges that are unique to single-cell experiments. Svensson, V.

Exponential scaling of single-cell RNA-seq in the past decade. This review is an excellent introduction to the full range of single-cell sequencing methods. Leelatian, N. Single cell analysis of human tissues and solid tumors with mass cytometry.

Cytometry B 92 , 68—78 A useful description of the pitfalls of tissue dissociation for users of single-cell sequencing to consider. Hines, W. Sorting out the FACS: a devil in the details. Cell Rep. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Brennecke, P. Accounting for technical noise in single-cell RNA-seq experiments.

Goldstein, L. Massively parallel nanowell-based single-cell gene expression profiling. Macosko, E. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Klein, A. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Cao, J. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Rosenberg, A. Single cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Hashimshony, T. Sena, J. Unique molecular identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis.

Dal Molin, A. How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives. Picelli, S. Full-length RNA-seq from single cells using Smart-seq2. Targeted RNA-Seq can be achieved via either enrichment or amplicon-based approaches. Use deep RNA-Seq to examine the signals and behavior of a cell in the context of its surrounding environment. This method is advantageous for biologists studying processes such as differentiation, proliferation, and tumorigenesis.

Achieve cost-effective RNA exome analysis using sequence-specific capture of the coding regions of the transcriptome. Ideal for low-quality samples or limited starting material. Accurately measure gene and transcript abundance and detect both known and novel features in coding and multiple forms of noncoding RNA. Isolate and sequence small RNA species, such as microRNA, to understand the role of noncoding RNA in gene silencing and posttranscriptional regulation of gene expression.

Deeply sequence ribosome-protected mRNA fragments to gain a complete view of the ribosomes active in a cell at a specific time point, and predict protein abundance. Transcriptomics and whole-genome shotgun sequencing provide researchers and pharmaceutical companies with data to refine drug discovery and development.

Learn about read length and depth requirements for RNA-Seq and find resources to help with experimental design. Advances in RNA-Seq library prep are revolutionizing the study of the transcriptome. Our enhanced RNA-Seq library prep portfolio spans multiple types of sequencing studies. These solutions offer rapid turnaround time, broad study flexibility, and sequencing scalability. A fast, flexible, and mobile-friendly tool, our Custom Protocol Selector helps you generate RNA sequencing protocols tailored to your experiment.

A simple, scalable, cost-effective, rapid single-day solution for analyzing the coding transcriptome leveraging as little as 25 ng input of standard non-degraded RNA.

These cost-efficient, user-friendly, mid-throughput benchtop sequencers offer extreme flexibility to support new and emerging applications. However, due to the technical noise of scRNA-seq and different subpopulations or sates of cells, attention should be paid to network reconstruction. To reduce spurious results, network inference should be carried out on each subpopulation or the cells with the same stage. Recently, Aibar et al. PIDC is another software designed to infer gene regulatory network from single-cell data using multivariate information theory Chan et al.

Such network inference tools facilitate the identification of expression regulatory network from single-cell transcriptomic data and provide critically biological insights into the regulatory relationships between genes. In the past 10 years, a great advancement has been achieved in scRNA-seq and a variety of scRNA-seq protocols have been developed.

The development and innovation of scRNA-seq largely facilitated single-cell transcriptomic studies, leading to insightful findings in cell expression variability and dynamics. Moreover, the throughput of scRNA-seq has significantly increased with the exciting progress in cellular barcoding and microfluidics. Meanwhile, scRNA-seq methods that can be used for fixation and frozen samples have also been proposed recently, which will greatly benefit the study of highly heterogeneous clinical samples.

However, currently available scRNA-seq approaches still have a high dropout problem, in which weakly expressed genes would be missed. The improvement of RNA capture efficiency and transcript coverage will definitely reduce the technical noise of scRNA-seq. Since the noise of scRNA-seq data is high, it is crucial to use appropriate methods to overcome the problem in analyzing scRNA-seq data. QC is necessary to exclude those low-quality cells to avoid involving artifacts in data interpretation.

Furthermore, batch effect correction if need , between sample normalization and imputation are also important and should be conducted before cell subpopulation identification, differential expression calling, and other downstream analyses.

Additionally, factors such as cell size and cell cycle state could play important roles in cell variability for certain types of cells, such biases are also need to be considered. Although an increasing number of methods have been specially designed to interpret scRNA-seq data, advances of novel methods that can effectively handle the technical noise and expression variability of cells are still required.

Specifically, the approaches that can accurately analyze AS and RNA-editing with scRNA-seq data are highly useful to unravel post-transcriptional mechanisms in individual cells. Overall, bioinformatics analysis of scRNA-seq data is still challenging, special attention should be paid in data interpretation, and more efficient tools are in urgent need.

Collectively, scRNA-seq and its related computational methods largely promote the development of single-cell transcriptomics. The continuous innovation of scRNA-seq technologies and concomitant advances in bioinformatics approaches will greatly facilitate biological and clinical researches, and provide deep insights into the gene expression heterogeneity and dynamics of cells. GC and TS designed the study and wrote the manuscript.

BN edited the manuscript and provided constructive comments. The information in these materials is not a formal dissemination of the United States Food and Drug Administration. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ahmed, S. Bioinformatics 35, 47— Aibar, S.

Methods 14, — Andrews, T. Identifying cell populations with scRNASeq. Aspects Med. Bioinformatics doi: Angerer, P. Bioinformatics 32, — Bacher, R. SCnorm: robust normalization of single-cell RNA-seq data. Design and computational analysis of single-cell RNA-sequencing experiments.

Genome Biol. Barrett, S. Circular RNAs: analysis, expression and potential functions. Development , — Becht, E. Dimensionality reduction for visualizing single-cell data using UMAP. Brennecke, P. Accounting for technical noise in single-cell RNA-seq experiments.

Methods 10, — Buettner, F. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Buttner, M. A test metric for assessing single-cell RNA-seq batch correction. Methods 16, 43— Cao, J.

Comprehensive single-cell transcriptional profiling of a multicellular organism. Science , — Chan, T. Gene regulatory network inference from single-cell data using multivariate information measures.

Cell Syst. Chen, G. Significant variations in alternative splicing patterns and expression profiles between human-mouse orthologs in early embryos.

China Life Sci. Characterizing and annotating the genome using RNA-seq data. Single-cell analyses of X Chromosome inactivation dynamics and pluripotency during differentiation. Genome Res. Identifying and annotating human bifunctional RNAs reveals their versatile functions. Overview of available methods for diverse RNA-Seq data analyses.

Chen, L. BCseq: accurate single cell RNA-seq quantification with bias correction. Nucleic Acids Res. Chen, X. From tissues to cell types and back: single-cell gene expression analysis of tissue architecture.

Data Sci. Delmans, M. Discrete distributional differential expression D3E —a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics Deng, Q. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Ding, J. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Dobin, A.

Bioinformatics 51, Engstrom, P. Systematic evaluation of spliced alignment programs for RNA-seq data. External, R. BMC Genomics Fan, H. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science Fan, X. Fan, J. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.

Methods 13, — Finak, G. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Frazee, A. Ballgown bridges the gap between transcriptome assembly and expression analysis. Garber, M. Computational methods for transcriptome annotation and quantification using RNA-seq. Methods 8, — Gierahn, T. II, Hughes, T. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.

Gong, W. DrImpute: imputing dropout events in single cell RNA sequencing data. Gott, J. Functions and mechanisms of RNA editing. Griffiths, J. Using single-cell genomics to understand developmental processes and cell fate decisions. Gross, A. Technologies for single-cell isolation. Grun, D. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature , — Habib, N. Haghverdi, L. Diffusion pseudotime robustly reconstructs lineage branching.

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

Haque, A. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.

Now that we have generated clusters, the reverse strands are cleaved and washed away. This leaves forward strands to begin sequencing. Sequencing begins with extension of the first sequencing primer to produce read 1, or the forward read.

Step 1 fluorescently tagged complementary nucleotides are added to the chain one base at a time based on the sequence of the template. Each nucleotide is tagged with a different color fluorescent signal. Each nucleotide is also a reversible terminator, meaning that after it is incorporated into the chain, another cannot be added. Step 2 after the nucleotide is added to the chain, a light source excites the clusters and a fluorescent signal is emitted and read by the sequencing machine.

The emission wavelength allows the computer to determine which base was added to the chain, which is a base call. The intensity of the signal produced will determine the confidence score for the accuracy of the base call. Step 3 after making the call, the reversible terminator is cleaved, and the chain is ready for the addition of the next nucleotide. This process of incorporating one nucleotide at a time and reading the signals is repeated and the number of cycles determines the length of the read.

All identical strands in a cluster are read simultaneously. Clusters are sequenced in a massively parallel process meaning that millions of reads are generated at once as opposed to the processing of single amplicons at a time like with Sanger Sequencing.

After completing read 1, the read product is washed away. Now, an index read primer is hybridized to the template and the index is read in the same fashion as the first read. This allows for the sorting of reads to particular samples. The second index is read like the first one. After the index is read, a polymerase extends the oligo once again forming a second stranded bridge. The strands are then linearized, and the forward strand is cleaved and washed away. The second sequencing primer is added, and read 2, or the reverse read, is generated through the cyclical adding of fluorescently nucleotides just like the first read.

This entire process generates millions of reads representing all the fragments in the flow cell. Now the reads generated by the sequencer are ready to be analyzed. Now that the samples have been sequenced, it is time to make sense of the massive amounts of raw data produced by the sequencing run.

These are plain text files and represent the data from the sequencing run using alphabetical, numerical, and punctuation characters. The sequence is reported as single character representations of the four nucleotides A, T, C, or G. Each base call generated is given a quality string, or quality score. This quality string refers to how accurately the sequencer made the correct base call in the sequence. The quality string represents a probability associated with the likelihood of an accurate base call.

The probability value is also referred to as a Phred quality score. Phred scores are numerical values given to every base determination in a sequencing run. Poor quality reads are removed or trimmed and are not used in the alignment process.

Now that our sequenced base calls have been quality checked, we can begin the bioinformatics process of alignment. First, sequences from pooled sample libraries are separated based on the unique barcodes introduced during the indexing stage of library preparation. For each sample, reads with similar or exact matched stretches of base calls are locally clustered. Then, the reads of the forward and reverse strands, these were read 1 and read 2 described above are paired creating contiguous sequences.