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Microarrays, deep sequencing and the true measure of the transcriptome.

Malone JH, Oliver B - BMC Biol. (2011)

Bottom Line: Microarrays first made the analysis of the transcriptome possible, and have produced much important information.Today, however, researchers are increasingly turning to direct high-throughput sequencing -- RNA-Seq -- which has considerable advantages for examining transcriptome fine structure -- for example in the detection of allele-specific expression and splice junctions.We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Cellular and Developmental Biology, National Institute of Digestive, Diabetes, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA. malonej@niddk.nih.gov

ABSTRACT
Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing -- RNA-Seq -- which has considerable advantages for examining transcriptome fine structure -- for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

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Related in: MedlinePlus

Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of reads that map to a scaled region of genome space are the index of the expression level of the gene.
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Figure 2: Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of reads that map to a scaled region of genome space are the index of the expression level of the gene.

Mentions: Figure 2 shows the same analysis performed by RNA-Seq, using an Illumina Genome Analyzer and a commonly deployed protocol for preparing libraries [44]. First, the transcriptomes for females and males are fragmented by alkaline hydrolysis, then reverse-transcribed to make double-stranded cDNAs using random hexamer primers. Next, the ends of transcript fragments are prepared to enable oligonucleotide adaptors to be ligated onto the ends. Fragments are then size-selected, amplified by PCR and injected into a flow cell. The flow cell is a glass slide that contains a lawn of oligonucleotides complementary to the adaptors ligated to transcripts and with a series of separate lanes in which sequencing reactions take place.


Microarrays, deep sequencing and the true measure of the transcriptome.

Malone JH, Oliver B - BMC Biol. (2011)

Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of reads that map to a scaled region of genome space are the index of the expression level of the gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3104486&req=5

Figure 2: Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of reads that map to a scaled region of genome space are the index of the expression level of the gene.
Mentions: Figure 2 shows the same analysis performed by RNA-Seq, using an Illumina Genome Analyzer and a commonly deployed protocol for preparing libraries [44]. First, the transcriptomes for females and males are fragmented by alkaline hydrolysis, then reverse-transcribed to make double-stranded cDNAs using random hexamer primers. Next, the ends of transcript fragments are prepared to enable oligonucleotide adaptors to be ligated onto the ends. Fragments are then size-selected, amplified by PCR and injected into a flow cell. The flow cell is a glass slide that contains a lawn of oligonucleotides complementary to the adaptors ligated to transcripts and with a series of separate lanes in which sequencing reactions take place.

Bottom Line: Microarrays first made the analysis of the transcriptome possible, and have produced much important information.Today, however, researchers are increasingly turning to direct high-throughput sequencing -- RNA-Seq -- which has considerable advantages for examining transcriptome fine structure -- for example in the detection of allele-specific expression and splice junctions.We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory of Cellular and Developmental Biology, National Institute of Digestive, Diabetes, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA. malonej@niddk.nih.gov

ABSTRACT
Microarrays first made the analysis of the transcriptome possible, and have produced much important information. Today, however, researchers are increasingly turning to direct high-throughput sequencing -- RNA-Seq -- which has considerable advantages for examining transcriptome fine structure -- for example in the detection of allele-specific expression and splice junctions. In this article, we discuss the relative merits of the two techniques, the inherent biases in each, and whether all of the vast body of array work needs to be revisited using the newer technology. We conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

Show MeSH
Related in: MedlinePlus