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Given the results of the last poll (most folks are using RNA-Seq for gene expression analysis), I thought we could dive a little deeper on the subject and see just what y’all think about some of the specifics of using RNA-Seq for gene expression analysis.

Based on what I’ve seen, there seems to be quite a range when discussing the appropriate sequence depth for gene expression analysis. i.e. the instrument manufacturer people say one thing and the genomics core people recommend quite another. Let us know what you think!

The poll is located in the right side-bar. I’ve had to move it down below the fold due to the addition of some advertising space. Sorry about that but got to pay the bills.

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The authors have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus Workflow Management System, which maps the analysis modules onto available computational resources, automatically executes the steps in the appropriate order and supervises the whole running process. RseqFlow is available as a Virtual Machine (VM) with all the necessary software, which eliminates any complex configuration and installation steps.

Availability and implementation: http://genomics.isi.edu/rnaseq

Wang Y et al. (2011) RseqFlow: Workflows for RNA-Seq data analysis Bioinformatics [Epub ahead of print]. [abstract]

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Being one of the largest families in Angiosperm, Orchidaceae displays a great biodiversity resulted from adaptation to diverse habitats. Genomic information of orchids is rather limited regardless of their unique and interesting biological features, thus impeding advanced molecular research. Here the authors report a strategy to integrate sequence outputs of the moth orchid, Phalaenopsis aphrodite, from two high-throughput sequencing platform technologies, Roche 454 and Illumina/Solexa, in order to maximize assembly efficiency. Tissues collected for cDNA library preparation included wide range of vegetative and reproductive tissues. Read more

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Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified Mapper (RUM).

A critical task in high throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data is discrete in nature; therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not previously been performed. Read more

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My thanks to a reader who pointed out this publication covering a recent topic of discussion on the RNA-Seq Blog.  (see The Magic of RNA-Seq)

This study found that for experiments performed with a small number of biological replicates, significant results may be due to biological variation and may not be reproducible; and it is impossible to know whether expression patterns are specific to the individuals in the study or are a characteristic of the study populations.

These ideas are now widely accepted for DNA microarray experiments, where a large number of biological replicates are now required to justify scientific conclusions.

Their analysis suggests that as biological variability is a fundamental characteristic of gene expression, sequencing experiments should be subject to similar requirements.

• Hansen, K.D., Wu, Z., Irizarry, R.A. & Leek, J.T. Sequencing technology does not eliminate biological variability. Nat Biotechnol 29, 572–573 (2011). [abstract]

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High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates. Read more

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Slides of the talk on “Quantitatively deconvolving alternative RNA secondary structures” by Regina Bohnert at the HiTSeq-SIG of ISMB/ECCB 2011 in Vienna, Austria, on July 15, 2011 can be found at:

http://www.fml.tuebingen.mpg.de/raetsch/lectures/HiTSeq-SIG-sQuant.pdf/at_download/file

The package is focused on finding differential exon usage using RNA-seq exon counts from samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates. The package also provides functions for the visualization and exploration of the results.

The software is available at: http://watson.nci.nih.gov/bioc_mirror/packages/2.9/bioc/html/DEXSeq.html

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• 71 Total Respondents
• More than twice as many of you performing RNA-Seq for gene expression as for the next highest application, novel transcript detection.

Given all that RNA-Seq is capable of providing for us, it is interesting to me that most of you are using RNA-Seq for gene expression analysis; something for which microarrays and PCR have proven more than adequate to provide for us for the past two decades.  However, since this is the case, we thought it would be beneficial to provide some info.  (See post below – http://rna-seqblog.com/information/the-magic-of-rna-seq/)

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There certainly is a lot of excitement and much buzz surrounding RNA-Seq’s forecasted replacement of microarrays for gene expression analysis. (I wonder… who could be generating this hype?) From speaking to those interested in RNA-Seq for gene expression profiling,  it seems there is somewhat of a frenzy, and a notion that RNA-Seq has some kind of magical power, so that the normal rules of good experimental design and practice don’t seem to apply here (i.e. need for replicate samples).  This is possibly partly due to the facts that it is still so new and still very expensive. We did a quick scan of some recent reviews to put together the following points for discussion. Read more

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The legume pod borer, Maruca vitrata (Lepidoptera: Crambidae), is an insect pest species of crops grown by subsistence farmers in tropical regions of Africa. We present the de novo assembly of 3729 contigs from 454- and Sanger-derived sequencing reads for midgut, salivary, and whole adult tissues of this non-model species. Read more

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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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