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As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, the authors review current methods for eQTL mapping using ASE and discuss some future directions. They also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping.

• Sun W, Hu Y. (2013) eQTL Mapping Using RNA-seq Data. Stat Biosci 5(1), 198-219. [article]

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• www rna-seqblog com eqtl-mapping-using-rna-seq-data

by Josh P. Roberts at  Biocompare

When next-gen sequencing exploded onto the scene, it brought in its wake a host of innovations. Among these is the deep-sequencing of RNA (RNA-Seq), which is giving unprecedented breadth and depth to our understanding of the way cells develop, regulate themselves and each other, and respond to their environment. Although the study of cellular RNA is not new, the scale on which researchers are now undertaking transcriptomic investigations and many of the questions they are now able to ask, would not have been possible with earlier technologies. Read more

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Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and non-coding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g., non-synonymous coding single nucleotide polymorphisms.

Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

• Hitzemann R, Bottomly D, Darakjian P, Walter N, Iancu O, Searles R, Wilmot B, McWeeney S. (2012) Genes, Behavior, and Next-Generation RNA Sequencing. Genes Brain Behav [Epub ahead of print]. [abstract]

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Genome-wide profiling of alternative splicing is not new. Before the invent of RNA-Seq technologies, genome-wide profiling of RNA splicing in biological samples included exon arrays, splice junction arrays, and genome-wide tiling arrays. Use of these technologies to profile known splicing events in various biological contexts has already revealed the importance of splicing in cancer research. A recent review of genome-wide profiling of splicing in cancer using various microarray platforms suggests that splicing in cancer is prevalent, regulated and that novel therapeutic strategies are emerging.

The success of microarrays in profiling known splicing in cancer can be extended to identifying tumor specific splicing events in reads from RNA-Seq using virtual microarray experiments. In such an experiment, short RNA reads from RNA-Seq can be considered virtual equivalent of cellular RNA, in silico mapping of reads can be considered virtual equivalent of hybridization and the sequences of exon-exon junction probes equivalent of virtual microarray platform. Hence, a non-redundant reference database of known splice junctions can be used to directly map RNA reads to detect and measure expression levels of known splice events. Although such an approach is limited to detection, by augmenting the database with predicted junctions, one could also infuse discovery into this approach.

Here, researchers at the Institute of Bioinformatics and Applied Biotechnology, Bangalore, India have profiled less than a million known plus predicted splice events to identify tumor-specific splicing in prostate tumor using a RNA-Seq dataset of matched tumor-normal from ten individuals downloaded from NCBI public repository.

• Srinivasan S, Patil AH, Verma M, Bingham JL, Srivatsan R. (2012) Genome-wide Profiling of RNA splicing in prostate tumor from RNA-seq data using virtual microarrays. J of Clin Bioinform [Epub ahead of print]. [article]

Researchers at Chalmers University of Technology, Sweden set out to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. They investigated: the use of three different aligners for read-mapping (Gsnap, Stampy and TopHat) on the genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and they explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach.

Results derived from different statistical methods of RNA-seq gave similar biological interpretations as is demonstrated by GO enrichment analysis. Their results strongly supports the robustness and reliability of different processing and analysis of RNA-seq data. Furthermore, we identified high consistency between microarray and RNA-seq platforms, thus encouraging the continual use of microarray as a versatile tool for differential gene expression analysis.

• Nookaew I, Papini M, Pornputtpong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J. (2012) A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res [Epub ahead of print]. [article]

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from Genomeweb

74% of core labs are doing more sequencing than last year. 36% are doing less microarray work.

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Alternative RNA-Seq application schemas. (a) In an iterative approach, high-abundance transcripts can be identified in low-read sequencing runs, followed by iterative subtraction of the sequences dominating each sample. A profile from the combined runs promises higher measurement precision of expression levels for weakly to moderately expressed transcripts. (b) After normalization of an aliquot (top row), the strength of RNA-Seq in de novo sequence discovery can be exploited for the compilation of a comprehensive target library, against which a custom microarray can then be designed easily (Leparc et al., 2009) The remaining aliquot can then be quantitatively profiled on this optimized array (bottom row). The performance of both approaches of course depends on the quality of the subtraction or normalization step, respectively.

• Labaj PP, Leparc GG, Linggi BE, Markillie LM, Wiley HS, Kreil DP. (2011) Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics 27(13), i383-91. [abstract]

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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, The authors 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.

They conclude that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.

Malone JH, Oliver B. (2011) Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 9(1), 34. [article]

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When examining large datasets processed from four different studies, researchers at Johns Hopkins and Brown Universities found RNA-seq data to be affected by similar errors that originally plagued the development of microarrays as a tool for analyzing gene expression. Microarrays, the technology that first permitted measurement of gene expression, had problems due to unwanted sources of variability. While these problems are now mitigated after years of statistical methodology development, this study found that RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays.

They report on commonly observed data distortions that demonstrate the need for data normalization. In particular, they found GC-content has a strong sample specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results.  To remove these unwanted sources of variation they have developed a normalization procedure for RNA-seq data that greatly improves precision without affecting accuracy. Their conditional quantile normalization (CQN) algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content, and quantile normalization to correct for global distortions.

Hansen KD, Irizarry RA , and Wu Z,  (2011) Removing Technical Variability In Rna-Seq Data Using Conditional Quantile Normalization. Collection of Biostatistics Research Archive [Epub ahead of print]. [article]

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Affymetrix CEO, Kevin King, remarked about the role microarrays continue to play despite the advancement of new sequencing technologies, such as RNA-Seq, during their recent Q1 2011 earnings call.

…a team led by Ron Davis at the Stanford Genome Technology Center, compared a custom Affy exon array to next-generation sequencing for understanding gene expression and Genome 1 identification of alternative splicing as well as infecting coding snips and non-coding transcripts. The performance of the array was examined and compared with miRNA sequencing, or RNA-Seq, over multiple independent replicate samples, and the results were published in February in the proceedings of the National Academy of Science.

To achieve comparable levels of sensitivity, the sequencing experienced cost 10x more than arrays and took much longer to complete. Even at that increased level of effort, the array had higher levels of sensitivity at the exon level. Even deeper sequencing will be required to address low abundance transcripts as well as the array did. The array has already been implemented in a multi-center clinical program involving thousands of samples, and as the author states, this platform is anticipated to have a wide range of applications and high throughput clinical studies. We believe the market for RNA arrays continues to be helping despite financial communities’ concerns over potential displacement by new sequencing technologies.

Affymetrix’s CEO Discusses Q1 2011 Results – Earnings Call Transcript

Q1 2011 Affymetrix Inc Earnings Conference Call
Wed, April 27, 2011 5:00 p.m. ET  Webcast Presentation

Click here for webcast

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The winner and new heavyweight champion is?… It’s a draw.

by Scott Peterson, Scientific Director, PFGRC at JCVI

In the past year or so there have been several articles stating that the death of microarray technology is growing near. These proclamations are due to the more recently introduced methodology referred to as RNAseq. At first glance I wrote these claims off as being silly and premature. Over time though I am starting to appreciate that while the claim is still clearly wrong, the issue isn’t about technology displacement at all. My group works on a wide variety of gene expression problems ranging from the simple in vitro microbial gene expression studies to problems involving metagenomic samples of enormous complexity (http://pfgrc.jcvi.org). In my experience, the decision of whether to use DNA microarrays or RNAseq seems straight-forward and unambiguous. In reality the two technologies couldn’t be more complementary. Given the simple in vitro gene expression study as an example, the low cost, short turn-around time, exceptional quantitative accuracy and ease of data generation all make the glass slide microarray the clear choice. (Read more… )

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From - Genomics Technologies: The Power of Genome-Scale Quantitative Data Resolution Profiling Transcriptomes, Plant Physiology 2010

…More recently, direct sequencing of transcripts by high-throughput sequencing technologies (RNA-Seq) has become an additional alternative to microarrays and is superseding SAGE and MPSS (Busch and Lohmann, 2007). Like SAGE and MPPS, RNA-Seq does not depend on genome annotation for prior probe selection and avoids biases introduced during hybridization of microarrays. On the other hand, RNA-Seq poses novel algorithmic and logistic challenges, and current wet-lab RNA-Seq strategies require lengthy library preparation procedures. Therefore, RNA-Seq is the method of choice in projects using nonmodel organisms and for transcript discovery and genome annotation. Because of their robust sample processing and analysis pipelines, often microarrays are still a preferable choice for projects that involve large numbers of samples for profiling transcripts in model organisms with well-annotated genomes. Read more

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• SEQanswers – RNA Sequencing

  • CuffDiff strange output May 23, 2013
    Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
    Pruexel
  • cannot away with cuffdiff,incredible May 23, 2013
    Hi,all I have 4(A,B,C,D) sample in 4 times(increasing time),I got diff result in 3 different cuffdiff 1.cuffdiff 3(A,B,C) individual... […]
    upper
  • TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013
    Hey guys, I have some problems with my paried-end RNA seq analysis on Galaxy. As you can see in the bam flagstat output, my tophat alignment rate is... […]
    Felix.Lee
  • Identifying small RNA sequence within whole genome sequence May 21, 2013
    Hi all, I want to know if there are any useful bioinformatic tool to find small RNA sequence within a whole bacteria genome. Thank you in... […]
    Inma
  • standard of clean data May 21, 2013
    Hi all I recently got my prokaryotes RNA-seq data report back. the standard filter steps of the raw data set by our local sequencing center is as... […]
    Pengfei Liu
  • Problem with cummeRbund diffData() May 20, 2013
    Hi all, I'm running Tophat/cufflinks/cuffdiff for differential gene expression and analysis with cummeRbund (v 2.0.0). I'm having an issue with... […]
    Enrique Zudaire

• Biostar – RNA-Seq

  • Why am I getting so many unmapped reads in STAR, classified as "too short"?
    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
  • What are the best practices for SNP identification in RNA seq transcriptome data
    I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
  • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
    Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
  • What happened to -k in TopHat for multiple-mapping reads?
    Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
  • Does tophat use the library-type information for mapping, or just for the XS flag?
    When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
  • Purpose of Y-shaped adapters in Illumina Sequencing?
    Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]