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ChimEric tranScript detection algorithm (EricScript) is a novel computational framework, named, for the identification of gene fusion products in paired-end RNA-seq data. A simulation study on synthetic data demonstrates that EricScript enables one to achieve higher sensitivity and specificity than existing methods with noticeably lower running times. The method is also applied to publicly available RNA-seq tumour datasets to demonstrate its capability in rediscovering known gene fusions.

Availability: The EricScript package is freely available under GPL v3 license at http://ericscript.sourceforge.net.

• Benelli M, Pescucci C, Marseglia G, Severgnini M, Torricelli F, Magi A. (2012) Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript. Bioinformatics [Epub ahead of print]. [abstract]

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• bioconductor rna-seq
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Date & Time: Thursday, 15th November 2012 | 3:00pm (GMT)
Duration: 45 min
Presented by: Dr Daniel Swan – Senior NGS Computational Biologist, OGT

RNA-Seq is revolutionising the field of transcriptomics, delivering unprecedented biological insight into the way cells function. This can only be reliably achieved through good experimental design – ensuring the data generated represents the true biology – and rigorous data analysis procedures, allowing transcript-level visualisation and interrogation of results.

This latest Webinar, will detail how Oxford Gene Technology (OGT) addresses these challenges to allow you to rapidly access meaningful results for a range of model eukaryotes.

(more info and register here…)

The recent development of third generation sequencing (TGS) generates much longer reads than second generation sequencing (SGS) and thus provides a chance to solve problems that are difficult to study through SGS alone. However, higher raw read error rates are an intrinsic drawback in most TGS technologies.

Researchers at  Stanford University present a computational method, LSC, to perform error correction of TGS long reads (LR) by SGS short reads (SR). Aiming to reduce the error rate in homopolymer runs in the main TGS platform, the PacBio® RS, LSC applies a homopolymer compression (HC) transformation strategy to increase the sensitivity of SR-LR alignment without scarifying alignment accuracy. They applied LSC to 100,000 PacBio long reads from human brain cerebellum RNA-seq data and 64 million single-end 75 bp reads from human brain RNA-seq data. The results show LSC can correct PacBio long reads to reduce the error rate by more than 3 folds. The improved accuracy greatly benefits many downstream analyses, such as directional gene isoform detection in RNA-seq study. Compared with another hybrid correction tool, LSC can achieve over double the sensitivity and similar specificity.

Availability: LSC  is freely available for the research community and can be downloaded at http://www.stanford.edu/~kinfai/LSC/LSC.html.

•  Au KF, Underwood JG, Lee L, Wong WH. (2012) Improving PacBio Long Read Accuracy by Short Read Alignment. PLoS One 7(10), e46679. [article]

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RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored. Read more

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Transcriptomic sequence resources represent invaluable assets for research, in particular for non-model species without a sequenced genome. To date, the Next Generation Sequencing technologies 454/Roche and Illumina have been used to generate transcriptome sequence databases by RNA-Seq for more than fifty different plant species. While some of the databases were successfully used for downstream applications, such as proteomics, the assembly parameters indicate that the assemblies do not yet accurately reflect the actual plant transcriptomes. Two different assembly strategies have been used, overlap consensus based assemblers for long reads and Eulerian path/de Bruijn graph assembler for short reads.

In this review, researchers from the Heinrich Heine University, Germany discuss the challenges and solutions to the transcriptome assembly problem. A list of quality control parameters and the necessary scripts to produce them are provided.

• Schliesky S, Gowik U, Weber AP, Bräutigam A. (2012) RNA-Seq Assembly – Are We There Yet? Front Plant Sci [Epub ahead of print]. [article]

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Protein nanopores are under investigation as key components of rapid, low-cost platforms to sequence DNA molecules. Previously, it has been shown that the α-hemolysin (αHL) nanopore contains three recognition sites, capable of discriminating between individual DNA bases when oligonucleotides are immobilized within the nanopore. However, the direct sequencing of RNA is also of critical importance.

Now, researchers at Oxford have achieved sharply defined current distributions that enable clear discrimination of the four nucleobases, guanine, cytosine, adenine and uracil, in RNA. Further, the modified bases, inosine, N6-methyladenosine and N5-methylcytosine, can be distinguished.

Ayub M, Bayley H. (2012) Individual RNA Base Recognition in Immobilized Oligonucleotides using a Protein Nanopore. Nano Lett [Epub ahead of print]. [abstract]

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Sat, Oct 13, 4:00 – 5:00 55.04/H17 – Quantification of full length and exon 7 skipped SMN mRNAs in human tissues using transcriptome sequencing databases	J. ZHOU	Poster 055. Spinal Muscular Atrophy Sat, Oct 13, 1:00 – 5:00 PM
Tue, Oct 16, 4:00 – 5:00 686.20/TT8 – Transcriptome sequencing analysis of gene expression in the brains of HIV-1 transgenic rats	M. D. LI	Poster 686. Neuroimmunology Tue, Oct 16, 1:00 – 5:00 PM
Mon, Oct 15, 4:00 – 5:00 443.24/F24 – RNA sequencing in iPSC-derived neurons identifies gene expression changes associated with 22q11.2 microdeletion syndrome	Y. TIAN	Poster 443. Autism: Genetic and Animal Models II Mon, Oct 15, 1:00 – 5:00 PM
Sat, Oct 13, 1:00 – 2:00 51.17/F37 – Parkinson’s disease: Neuroprotection by nicotine studied via RNA-Seq	B. M. HENLEY	Poster 051. Parkinson’s Disease: Models and Behavior Sat, Oct 13, 1:00 – 5:00 PM
Sun, Oct 14, 2:00 – 2:15 228.05 – Comparison of RNA-Seq to Exon Microarrays using the spinal nerve transection model of neuropathic pain reveals enhanced sensitivity of RNA-Seq and suggests novel areas of expression in the rat genome	A. ANTUNES-MARTINS	Nanosymposium 228. Genomic Approaches to Neural Function Sun, Oct 14, 1:00 – 3:00 PM
Mon, Oct 15, 2:00 – 3:00 486.26/VV14 – Using RNA-Seq to evaluate the aged female Sprague-Dawley rat cortex transcriptome after repeated bouts of sleep deprivation induced by the gentle-handling method	A. S. ELLIOTT	Poster 486. Sleep: Molecular and Cellular Mechanisms Mon, Oct 15, 1:00 – 5:00 PM
Wed, Oct 17, 8:00 – 8:15 725.01 – RNA-Seq analysis of DRG neuronal subpopulation: Insights into the “nociceptome” and axotomy induced gene expression	S. CLOKIE	Nanosymposium 725. Neuropathic Pain: Mechanisms and Models Wed, Oct 17, 8:00 – 10:15 AM
Wed, Oct 17, 1:00 – 2:00 839.13/A60 – RNA-Seq and microRNA expression profiling reveal networks of RNA interactions in regenerating dorsal root ganglion neurons	D. MOTTI	Poster 839. PNS Regeneration II Wed, Oct 17, 1:00 – 5:00 PM

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Nanosymposium – 228. Genomic Approaches to Neural Function – Sun, Oct 14, 1:00 – 3:00 PM

 Sun, Oct 14, 2:00 – 2:15 PM
228.05 – Comparison of RNA-Seq to Exon Microarrays using the spinal nerve transection model of neuropathic pain reveals enhanced sensitivity of RNA-Seq and suggests novel areas of expression in the rat genome

*A. ANTUNES-MARTINS, J. R. PERKINS, J. M. DAWES, M. CALVO, J. GRIST, W. RUST, R. SCHMID, T. HILDEBRANDT, C. ORENGO, S. B. MCMAHON, D. L. BENNETT;

Nanosymposium – 725. Neuropathic Pain: Mechanisms and Models – Wed, Oct 17, 8:00 – 10:15 AM

 Wed, Oct 17, 8:00 – 8:15 AM
725.01 – RNA-Seq analysis of DRG neuronal subpopulation: Insights into the “nociceptome” and axotomy induced gene expression

*S. CLOKIE, S. GOSWAMI, G. GONNELLA, H. KOMINSKY, K. KASZAS, S. MISHRA, E. LEBOVITZ, M. HOON, M. J. IADAROLA;

This guide is meant to offer an easy to follow guide to the analysis of RNA-seq data, aimed at those without any prior experience analyzing next-gen data. However, a basic level of familiarity with R, the next-gen sequencing procedures and using the unix shell are assumed. It was primarily written by Matthew Young (myoung@wehi.edu.au) and is a work in progress. Most of the steps described here are outlined in our review article which can be cited if people are using this guide in their work… The pathogen example was provided by B. Usadel and makes use of a different set of tools.

http://seqanswers.com/wiki/How-to/RNASeq_analysis

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A researcher at University of California Davis questions the basic assumption that deeper sequencing is better. They utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNA-seq to capture the majority of this information. Shannon Entropy is a sensitive tool used to estimate the diversity of a system.

Their analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample.

Given that most modern sequencing technologies are conservatively giving 100 million reads per lane, this would suggest that multiplexing of up to several hundred samples per lane would still allow for over 90% of the transcriptomic information content to be obtained in each sample.

• Kliebenstein DJ. (2012) Exploring the shallow end; estimating information content in transcriptomics studies. Front Plant Sci. 3:213. Epub 2012 Sep 10. [article]

Incoming search terms:

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A team led by researchers at The St. Laurent Institute present data that challenges the notion that RNAs produced from the vast expanse of intronic space are just pieces of pre-mRNAs or excised introns en route to degradation. The function of RNA from the non-coding regions of the genome has been a subject of considerable recent debate. Perhaps the most controversy is regarding the function of RNAs found in introns of annotated transcripts, where most of the reads that map outside of exons are usually found.

By performing a highly quantitative RNA-seq analysis of transcriptome changes during an inflammation time course, they show that intronic RNAs have a number of features that would be expected from functional, standalone RNA species. They suggest that the sequences encoded in the introns appear to harbor a yet unexplored reservoir of novel, functional RNAs. As such, they should not be ignored in surveys of functional transcripts or other genomic studies.

• St Laurent G 3rd, Shtokalo D, Tackett M, Yang Z, Eremina T, Wahlestedt C, Inchima SU, Seilheimer B, McCaffrey TA, Kapranov P. (2012) Intronic RNAs constitute the major fraction of the non-coding RNA in mammalian cells. BMC Genomics. 13(1), 504. [article]

Incoming search terms:

• RNA-seq intron
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The Simple Error Ratio Estimate (SERE) is a single-parameter test procedure for count data that can determine whether two RNA-Seq libraries are faithful replicates or globally different.

• Interpretation of SERE is unambiguous regardless of the total read count or the range of expression differences among bins (exons or genes), a score of 1 indicating faithful replication (i.e., samples are affected only by Poisson variation of individual counts), a score of 0 indicating data duplication, and scores >1 corresponding to true global differences between RNA-Seq libraries.
• On the contrary the interpretation of Pearson’s r is generally ambiguous and highly dependent on sequencing depth and the range of expression levels inherent to the sample (difference between lowest and highest bin count).
• Cohen’s simple Kappa results are also ambiguous and are highly dependent on the choice of bins.

For quantifying global sample differences SERE performs similarly to a measure based on the negative binomial distribution yet is simpler to compute. SERE can therefore serve as a straightforward and reliable statistical procedure for the global assessment of pairs or large groups of RNA-Seq datasets by a single statistical parameter.

Availability – ????????

• Schulze SK, Kanwar R, Gölzenleuchter M, Therneau TM, Beutler AS. (2012) SERE: Single-parameter quality control and sample comparison for RNA-Seq. BMC Genomics13(1), 524. [abstract]

 

Incoming search terms:

• rna seq quality parameter

Bamboo occupies an important phylogenetic node in the grass family with remarkable sizes, woodiness and a striking life history. However, limited genetic research has focused on bamboo partially because of the lack of genomic resources.  Now, a team led by researchers at the Chinese Academy of Forestry in Beijing performed de novo transcriptome sequencing for the first time to produce a comprehensive dataset for the Ma bamboo (Dendrocalamus latiflorus Munro). Read more

Incoming search terms:

• bamboo pipeline
• big node bamboo

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

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    I'm using edgeR in order to perform differential expression analysis from RNA-seq experiment. I have 6 samples of tumor cell, same tumor and same treatment: 3 patient with good prognosis and 3 patient with bad prognosis. I want to compare the gene expression among the two groups. I ran the edgeR pakage like follow: x […]
  • Normalising tag count to RPKM
    Hi! I was wondering if their is a way to normalise the number of reads in a region and the RPKM of the nearest gene to that region, so that a correlation could be computed. Like the following data shows number of tags in first column and RPKM in second column Tags RPKM 15 0.14619 11 0 203 0.2259 129 10.701 300 7.0772 122 2.3234 346 10.666 77 3.117 201 16.749 […]
  • a simple question on RNA-Seq terminology
    This question may be very simple and basic, but I just need to confirm that I understand the differences among those terminologies in the RNA-Seq context. Suppose I have a sample called SLR, and it is sequenced on 5 lanes, so I have (among other output files) BAM files like L1_SLR, L2_SLR, L3_SLR, L5_SLR and L7_SLR.bam. Here, the letter "L" denotes […]
  • FInding regions of interest with minimum coverage
    Hi, I have a bam file of all my accepted hits (tophat output) and an gtf file with my genes of interest for which I am trying to find potential antisense transcripts. I would like to create a list - preferably one that can be visualized in a genome browser - that shows all genes that have antisense reads in the accepted hits.bam file provided that there are […]
  • How to remove the intronic reads before counting
    I got RNASeq data in several samples. I checked the FastQC, seems the read quality are good (Hiseq 2000). But the problem is many reads are mapped to intronic region, and the regions have no any reference exons there (Refseq, ensembl, gencode). We don't know what they are. We guess the problem happend in library preparation, the concentration was low. N […]
  • Which strand of the mRNA molecule does the sequencer output as a "read"?
    In Illumina Stranded RNA-Seq (using the dUTP method), do the final reads in the fastq files correspond to the initial molecule (that was transcribed), or to the reverse complement of the molecule? C […]