Chapter One - Introduction to Sequencing the Brain Transcriptome
Introduction
Next-generation sequencing (NGS) refers to a variety of related technologies, often termed massively parallel sequencing. The first NGS platform (Roche 454) was introduced in 2004. Subsequently, other platforms were released by several manufacturers: Illumina (Solexa), Helicos, Pacific Biosciences, and Life Technologies (ABI). Although the instruments differ in the underlying chemistry and technical approach, the platforms are similar in their capability of producing very large numbers of simultaneous reads relative to traditional methods. Thus, it is now possible to sequence whole genomes, exomes, and transcriptomes for a reasonable cost and effort. The technology of transcriptome sequencing, also known as RNA-Seq, has matured to the point that it is reasonable to propose substituting RNA-Seq for microarray-based assessments of global gene expression. Of particular importance to our laboratories are the advantages RNA-Seq has over microarray platforms when analyzing complex rodent crosses, e.g., heterogeneous stocks (HSs). However, the same argument can be made when analyzing any outbred population, including humans. Of particular relevance to the brain transcriptome are the advantages RNA-Seq has over microarrays in analyzing alternative splicing. This chapter provides a starting point for understanding the emergence of RNA-Seq and emphasizes transcriptome/behavior relationships.
Section snippets
From Microarrays to RNA-Seq
Cirelli and Tononi (1999) were among the first to report genome-wide brain gene expression profiling associated with a behavioral phenotype; both mRNA differential display and cDNA arrays were used to examine the effects of sleep deprivation on rat prefrontal cortex gene expression. Sandberg et al. (2000) used Affymetrix microarrays to detect differences in brain gene expression between two inbred mouse strains (C57BL/6J [B6] and 129SvEv [129; now 129S6/SvEvTac]). Importantly, these authors
NGS Platforms
There are several excellent reviews of the various NGS platforms (e.g., Mardis, 2008, Mardis, 2011, Martin and Wang, 2011, Metzker, 2010, Ozsolak and Milos, 2011, Rothberg et al., 2011). Understanding in some depth how the platforms work is critical to understanding where errors develop and are propagated from sample preparation to alignment to data analysis. The differences in platforms will not be discussed here. We simply note that for RNA-Seq experiments, the majority have used the Illumina
RNA-Seq Overview
The first and perhaps the most important step of an RNA-Seq experiment is the same as that for a microarray experiment, the isolation of high-quality RNA. Although both RNA-Seq and microarrays can be used on fragmented RNA such as that found in formalin-fixed-paraffin-embedded samples, the biases present in such samples for genome-wide sequencing are difficult to assess. RNA quality is routinely examined on the Agilent BioAnalyzer or a similar instrument; an RNA integrity number (RIN) of ≥ 8 is
RNA-Seq and Data Analysis
Before commenting on the analysis of RNA-Seq data, it is useful to recount the analysis controversies that arose with the introduction of microarrays. In 1999, Nature Genetics devoted an entire issue (volume 21—January) to microarrays. Cautionary concerns were raised around issues of data analysis (Lander, 1999). Microarray experiments, at the time, were generally expensive, limiting sample sizes. Small sample sizes and thousands of independent observations per sample were seen as a
Sequencing the Brain Transcriptome
PubMed lists 2702 RNA-Seq publications (6/1/14) with the first appearing in June 2008 (Nagalakshmi et al., 2008); the number has steadily increased from 11 in 2008, to 34 in 2009, to 127 in 2010, to 339 in 2011, to 639 in 2012, and to 1123 in 2013. Of these publications, 162 are also coded as “RNA-Seq and Brain” (~ 6% of total). However, this number most certainly represents a low estimate of the number of publications where RNA-Seq is used to assess the brain transcriptome or brain surrogates
Conclusions
Historically, the main arguments against using RNA-Seq (as opposed to using microarrays) have been cost and difficulties with data analysis. Over the past 6 years, technical improvements have and will continue to reduce costs; if the primary goal is gene-wide summarization, transcriptome samples can now be multiplexed and sequenced at adequate depth for less than $200/sample (not including the cost of library preparation). RNA-Seq data analysis remains substantially more complex than a
Acknowledgments
This study was supported in part by grants MH 51372, AA 11034, AA 13484, and a grant from the Veterans Affairs Research Service. The authors want to thank Dr. Kristin Demarest and Kris Thomason for editorial assistance in preparing the review.
References (94)
- et al.
A comparison between ribo-minus RNA-sequencing and polya-selected RNA-sequencing
Genomics
(2010) - et al.
Ethanol modulation of gene networks: Implications for alcoholism
Neurobiology of Disease
(2012) - et al.
High-affinity GABA and glutamate transport in developing nerve ending particles
Brain Research
(1978) - et al.
Functional and evolutionary insights into human brain development through global transcriptome analysis
Neuron
(2009) - et al.
Altered gamma-aminobutyric acid type B receptor subunit 1 splicing in alcoholics
Biological Psychiatry
(2014) - et al.
Splicing regulation in neurologic disease
Neuron
(2006) The impact of next-generation sequencing technology on genetics
Trends in Genetics
(2008)The central role of RNA in human development and cognition
FEBS Letters
(2011)- et al.
A systems genetic analysis of alcohol drinking by mice, rats and men: Influence of brain GABAergic transmission
Neuropharmacology
(2011) - et al.
SNPs on chips: The hidden genetic code in expression arrays
Biological Psychiatry
(2007)
Microarray data analysis: From disarray to consolidation and consensus
Nature Reviews. Genetics
SplicingCompass: differential splicing detection using RNA-seq data
Bioinformatics
Brain transcriptome sequencing and assembly of three songbird model systems for the study of social behavior
PeerJ
Pathfinder: Mining signal transduction pathway segments from protein-protein interaction networks
BMC Bioinformatics
Expression profiling in alcoholism research
Alcoholism: Clinical and Experimental Research
Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays
PLoS One
Discovering genes involved in alcohol dependence and other alcohol responses: Role of animal models
Alcohol Research: Current Reviews
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
BMC Bioinformatics
Genome-wide gene expression profiling of nucleus accumbens neurons projecting to ventral pallidum using both microarray and transcriptome sequencing
Frontiers in Neuroscience
Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution
Science
The Collaborative Cross, a community resource for the genetic analysis of complex traits
Nature Genetics
Differences in brain gene expression between sleep and waking as revealed by mRNA differential display and cDNA microarray technology
Journal of Sleep Research
Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters
Science
Uncovering the complexity of transcriptomes with RNA-Seq
Journal of Biomedicine & Biotechnology
Integrating many co-splicing networks to reconstruct splicing regulatory models
BMC Systems Biology
Large scale analysis of positional effects of single-base mismatches on microarray gene expression data
BioData Mining
Global approaches to the role of miRNAs in drug-induced changes in gene expression
Frontiers in Genetics
MicroRNA-Seq reveals cocaine-regulated expression of striatal microRNAs
RNA
Base-calling of automated sequencer traces using phred. I. Accuracy assessment
Genome Research
Using the bioconductor gene answers package to interpret gene lists
Methods in Molecular Biology
Sequencing and characterization of the guppy (Poecilia reticulata) transcriptome
BMC Genomics
Computational methods for transcriptome annotation and quantification using RNA-Seq
Nature Methods
Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs
Nature Biotechnology
On the integration of alcohol-related quantitative trait loci and gene expression analyses
Alcoholism: Clinical and Experimental Research
Gene expression in brain: A window on ethanol dependence, neuroadaptation, and preference
Alcoholism: Clinical and Experimental Research
Characterization of the quantitative trait locus for haloperidol-induced catalepsy on distal mouse chromosome 1
Genes, Brain, and Behavior
Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target
Proceedings of the National Academy of Sciences of the United States of America
Genetic diversity and striatal gene networks: Focus on the heterogeneous stock-collaborative cross (hs-cc) mouse
BMC Genomics
Utilizing RNA-Seq data for de novo coexpression network inference
Bioinformatics
Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling
Science
Antisense transcription in the mammalian transcriptome
Science
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Nature Methods
Mouse genomic variation and its effect on phenotypes and gene regulation
Nature
Mapping a barbiturate withdrawal locus to a 0.44 Mb interval and analysis of a novel null mutant identify a role for Kcnj9 (GIRK3) in withdrawal from pentobarbital, zolpidem, and ethanol
Journal of Neuroscience
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling
Bioinformatics
Computational detection of alternative exon usage
Frontiers in Neuroscience
Array of hope
Nature Genetics
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Genes, behavior, and next-generation sequencing: The first 10 years
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