• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,875 feed subscribers
• 
  

• Featured Events

  
  

• Media Partners

  
  

• 

• Polls

  What is the greatest immediate need facing the RNA Sequencing community? (Choose Only One)

  • Further advancement in sequencing technology capabilities (throughput, depth, accuracy, etc)
  • Better (more uniform, less bias, simpler, faster, easier, etc) library preparation protocols
  • A standardized data analysis pipeline
  • Skilled bioinformatics specialists
  • Continued reduction in cost of sequencing reagents/services
  • More sequencing capacity

  View Results

   Loading ...
• Search for:

• Data Analysis Tools

  Transcriptome Assembly Tools

  Expression & Quantification

  Unpliced Mapping Tools

  Splicing and Junction Mapping

  Analysis Pipelines

  Clouding Platforms

  Databases

  Other Tools

• Tag Cloud

  allele-specific expression alternative splicing bioinformatics breast cancer chip-seq cloud computing cufflinks Data Analysis data analysis pipeline deep sequencing de novo assembly differential expression encode ensembl expression analysis galaxy gene expression high-throughput sequencing illumina ion torrent junction mapping library preparation life technologies lncrna microarray microarrays microrna mirna next-generation sequencing next-gen sequencing nih pacific biosciences poll results RNA-Seq rnaseq rna sequencing roche small rna tophat transcriptome transcriptome analysis transcriptome assembly transcriptomes transcriptome sequencing transcriptomics

  WP Cumulus Flash tag cloud by Roy Tanck requires Flash Player 9 or better.

• Categories

  Select Category Advertisers Analysis Pipelines Annotation Clouding Platforms Controls Data Analysis Data Analysis Data Sets Data Visualization Databases Events Expression and Quantification Grants / Funding Industry News Information Jobs Library Preparation Methods News Other Tools Patents Pathway Analysis Poll Results Presentations Press Release Publications Reader Conributions Request for RNA-Seq Services Review Sequencing Protocols SNP Detection Splicing and Junction Mapping Statistical Analysis Transcriptome Assembly Tools Transcriptome Sequenced Uncategorized Unspliced Mapping Tools Web Tools Workflow
• 
  
  

• Recent Comments

  • Anthony on A Review of Computational Tools in microRNA Discovery
  • Nicolas on Computational approaches to the analysis of RNA-seq data
  • Ernesto on Novel amplification based RNA-seq methodology generates highly reproducible sequencing libraries from as low as 25 picograms of mRNA
  • Arrayers on RNA-Seq Data Analysis Tools
  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute

• Popular Search Terms

  • grape
  • tirthadipa pradhan
  • rna seq analysis
  • next generation sequencing ppt
  • rna-seq analysis
  • rna seq data analysis
  • rna seq vs microarray
  • RSEM
  • wine grapes
  • mangrove

• Recent Search Terms

  • strand specific rna seq illumina
  • specific allele expression
  • computational analysis of rna seq data
  • bacterial rna seq depth and coverage
  • bacterial rna seq protocol
  • transcriptiome sequencing wiki
  • rna capture seq
  • rna captureseq
  • dario\s experiment
  • darios experiment

• Archives

  • June 2013
  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • February 2010
  • January 2010
  • January 2009

The wide spread use of anoparticles (NPs) raises concern of their potential toxicological effects in humans and ecosystems. Here researchers at the Université de Montréal used RNA-Seq to evaluate the effects of exposure to four different metal-based NPs, nAg, nTiO2, and nZnO and CdTe/CdS quantum dots (QD), in the eukaryotic green alga Chlamydomonas reinhardtii. The transcriptome was characterized before and after exposure to each NP type. Specific toxicological effects were inferred from the functions of genes whose transcripts either increased or decreased. Data analysis resulted in important differences and also similarities among the NPs. Elevated levels of transcripts of several marker genes for stress were observed, suggesting that only nZnO caused non-specific global stress to the cells under environmentally relevant conditions. Genes with photosynthesis-related functions were decreased drastically during exposure to nTiO2 and slightly during exposures to the other NP types. This pattern suggests either toxicological effects in the chloroplast or effects that mimic a transition from low to high light. nAg exposure dramatically elevated the levels of transcripts encoding known or predicted components of the cell wall and the flagella, suggesting it damages structures exposed to the external milieu. Exposures to nTiO2, nZnO, and QD elevated transcripts encoding subunits of the proteasome, suggesting proteasome inhibition, a phenomenon believed to underlie the development and progression of several major diseases, including Alzheimer’s disease, and used in chemotherapy against multiple myeloma.

• Simon DF, Domingos RF, Hauser C, Hutchins CM, Zerges W, Wilkinson KJ. (2013) RNA-Seq analysis of the effects of metal nanoparticle exposure on the transcriptome of Chlamydomonas reinhardtii. Appl Environ Microbiol [Epub ahead of print]. [abstract]

General info

Date - 05 Jun 2013 - 07 Jun 2013 

Location – Wageningen, the Netherlands

Keywords – RNA-seq, transcriptome, experimental design, statistics, data analysis, mapping, quantification, differential expression, interpretation, pathways

Organiser – Experimental Plant Sciences (EPS, Wageningen UR) & NBIC

Teacher(s)

Gabino Sanchez-Perez

Edouard Severing

Sandra Smit

Ole Madsen

Elio Schijlen

Contact(s)

Harm Nijveen

Patrick Koks

Sandra Smit

Description

The Power of RNA-seq is an introductory course for researchers who want to use RNA-seq in their research project.

The course will focus on questions like:

• Which questions can be addressed with RNA-seq?
• How many samples and replicates do I need?
• Which steps are involved in an RNA-seq experiment?
• What is Differential Expression?
• What can go wrong?

This is a 3-day course that will consist of lectures in the morning and extensive hands-on computer practicals in the afternoon. You’ll learn about all aspects of RNA-seq during the morning lectures on NGS & RNA-seq theory, but also the context, applicability, power and expected results of RNA-seq experiments. During the practicals, you’ll learn the basic steps an RNA-seq pipeline consist of, how to interpret your data and to put the results to use in your research project. We’ll use Galaxy, R and webtools for this.

topics:

• experiental design
• sequencing requirements
• steps in RNA-seq data analysis (data quality control, transcript identification, quantification, differential  expression, interpretation)

target audience:

Researchers in Life Sciences (‘biologists’) starting with application of NextGen Sequencing & RNA-seq.

• No previous NGS experience is needed
• No command line or Linux computer experience is required
• No R-experience is required

For more information, contact Patrick Koks

Incoming search terms:

• trinity transcriptome
• cuffdiff test statistics likelihood test
• samseq workflow
• RNAseq deduplication
• tomato genomics
• Transcriptome seq
• differential gene expression small sample size rna-seq
• tomato genome size rnaseq
• tools available for differential expression
• Introductory Course – The Power of RNA-seq

Next-Generation Sequencing and Analysis of Genomes

Session Type: Methods Workshop
Session Start/End Time:         Saturday, Apr 06, 2013, 1:00 PM – 3:00 PM
Location:         Ballroom A-B, Washington Convention Center
CME:   CME-Designated
CME/CE Hours:          2

Session Description:   High-throughput sequencing has enabled applications for understanding genomic alterations and aberrant expression in human cancers. Numerous computational methods have been developed to comprehensively analyze the rapidly increasing cancer genomics data thereby maximizing the discovery, interpretation, and prioritization of critical altered genes and pathways. This session will focus on methods for transcriptome and whole genome sequence analysis by highlighting current approaches, data analysis issues, and application to cancer research.

Presentations:

Chairperson
Saturday, Apr 06, 2013, 1:00 PM – 3:00 PM
Christopher A. Maher. Washington University, Saint Louis, MO

Gene fusion discovery from using transcriptome sequencing
Saturday, Apr 06, 2013, 1:00 PM – 1:20 PM
Christopher A. Maher. Washington University, Saint Louis, MO

Discussion
Saturday, Apr 06, 2013, 1:20 PM – 1:30 PM

RNA sequencing for analyzing expression of genes and isoforms
Saturday, Apr 06, 2013, 1:30 PM – 1:50 PM
Katherine Hoadley. University of North Carolina, Chapel Hill, NC

Discussion
Saturday, Apr 06, 2013, 1:50 PM – 2:00 PM

CRAVAT: A next-generation application for cancer mutation analysis
Saturday, Apr 06, 2013, 2:00 PM – 2:20 PM
Rachel Karchin. Johns Hopkins Univ., Baltimore, MD

Discussion
Saturday, Apr 06, 2013, 2:20 PM – 2:30 PM

Network and pathway analysis of genomic aberrations in cancer
Saturday, Apr 06, 2013, 2:30 PM – 2:50 PM
Benjamin J. Raphael. Brown University, Providence,, RI

Discussion
Saturday, Apr 06, 2013, 2:50 PM – 3:00 PM

Incoming search terms:

• aacr 2013 program rna-seq
• mouse rna-seq on ion PGM
• powerpoint rna-seq
• history of rna seq ppt
• transcriptomics germany
• transcriptomics lecture notes ppt
• RNA seq NGS
• weill cornell medical college/helicos
• rna seq data presentation
• presentation slides on rna

Populus euphratica, a typical hydro-halophyte, is ideal for studying salt stress responses in woody plants. MicroRNAs (miRNAs) are endogenous non-coding small RNAs that fulfilled an important post-transcriptional regulatory function. MiRNA may regulate tolerance to salt stress but this has not been widely studied in P. euphratica.

In this investigation, the small RNAome, degradome and transcriptome were studied in salt stress treated P. euphratica by deep sequencing. Two hundred and eleven conserved miRNAs between Populus trichocarpa and P. euphratica have been found. In addition, 162 new miRNAs, belonging to 93 families, were identified in P. euphratica. Degradome sequencing experimentally verified 112 targets that belonged to 51 identified miRNAs, few of which were known previously in P. euphratica. Transcriptome profiling showed that expression of 15 miRNA-target pairs displayed reverse changing pattern under salt stress.

Together, these results indicate that, in P. euphratica under salt stress, a large number of new miRNAs could be discovered, and both known and new miRNA were functionally cleaving to their target mRNA. Expression of miRNA and target were correspondingly induced by salt stress but that it was a complex process in P. euphratica.

• Li B, Duan H, Li J, Deng XW, Yin W, Xia X. (2013) Global identification of miRNAs and targets in Populus euphratica under salt stress. Plant Mol Biol [Epub ahead of print]. [abstract]

Incoming search terms:

• RNA-seq populus
• rna seq populus
• degradome or rna-seq
• rnaseq drosophila immunity
• rna-seq transcriptome
• rna and small rna transcriptome profile
• populus trichocarpa degradome
• populus euphratica
• populus degradome sequencing
• pgm library preparation micro rna

Researchers at the Rajiv Gandhi Center for Biotechnology, India carried out the de novo sequencing using an Illumina HiSeq™ 2000 to generate the first leaf transcriptome of black pepper (Piper nigrum L.), an important spice variety native to South India and also grown in other tropical regions. Despite the economic and biochemical importance of pepper, a scientifically rigorous study at the molecular level is far from complete due to lack of sufficient sequence information and cytological complexity of its genome.

The 55 million raw reads obtained, when assembled using Trinity program generated 2,23,386 contigs and 1,28,157 unigenes. Reports suggest that the repeat-rich genomic regions give rise to small non-coding functional RNAs. MicroRNAs (miRNAs) are the most abundant type of non-coding regulatory RNAs. In spite of the widespread research on miRNAs, little is known about the hair-pin precursors of miRNAs bearing Simple Sequence Repeats (SSRs). They used the array of transcripts generated, for the in silico prediction and detection of ‘43 pre-miRNA candidates bearing different types of SSR motifs’. The analysis identified 3913 different types of SSR motifs with an average of one SSR per 3.04 MB of thetranscriptome. About 0.033% of the transcriptome constituted ‘pre-miRNA candidates bearing SSRs’.

The abundance, type and distribution of SSR motifs studied across the hair-pin miRNA precursors, showed a significant bias in the position of SSRs towards the downstream of predicted ‘pre-miRNA candidates’. The catalogue of transcripts identified, together with the demonstration of reliable existence of SSRs in the miRNA precursors, permits future opportunities for understanding the genetic mechanism of black pepper and likely functions of ‘tandem repeats’ in miRNAs.

• Joy N, Asha S, Mallika V, Soniya EV (2013) De novo Transcriptome Sequencing Reveals a Considerable Bias in the Incidence of Simple Sequence Repeats towards the Downstream of ‘Pre-miRNAs’ of Black Pepper. PLoS ONE 8(3),  e56694. [article]

Incoming search terms:

• ssr 4840 novo

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

Incoming search terms:

• rna-seq’ is emerging as the method of choice for measuring transcriptomes of organisms though the older technique of dna microarrays is also still used
• rna seq vs microarray cost
• transcriptomics lecture ppt
• development of transcriptomics
• drug discovery rnasequencing
• microarray ppt of research paper in 2013
• microarray rna-seq platforms
• problems in prokaryotic transcriptomics
• Jiaqian Wu postdoc
• RNA-seq and HiSeq vs Affymetrix

RNA-based next-generation sequencing (RNA-Seq) provides a tremendous amount of new information regarding gene and transcript structure, expression and regulation. This is particularly true for non-coding RNAs where whole transcriptome analyses have revealed that the much of the genome is transcribed and that many non-coding transcripts have widespread functionality. However, uniform resources for raw, cleaned and processed RNA-Seq data are sparse for most organisms and this is especially true for non-human primates (NHPs).

Now, a team led by researchers at the Weill Cornell Medical College have developed a large-scale RNA-Seq data and analysis infrastructure, the NHP reference transcriptome resource (http://nhprtr.org); it presently hosts data from12 species of primates, to be expanded to 15 species/subspecies spanning great apes, old world monkeys, new world monkeys and prosimians. Data are collected for each species using pools of RNA from comparable tissues. They provide data access in advance of its deposition at NCBI, as well as browsable tracks of alignments against the human genome using the UCSC genome browser.

This resource will continue to host additional RNA-Seq data, alignments and assemblies as they are generated over the coming years and provide a key resource for the annotation of NHP genomes as well as informing primate studies on evolution, reproduction, infection, immunity and pharmacology.

• Pipes L, Li S, Bozinoski M, Palermo R, Peng X, Blood P, Kelly S, Weiss JM, Thierry-Mieg J, Thierry-Mieg D, Zumbo P, Chen R, Schroth GP, Mason CE, Katze MG. (2012) The non-human primate reference transcriptome resource (NHPRTR) for comparative functional genomics. Nucleic Acids Res [Epub ahead of print]. [article]

Incoming search terms:

• Hiseq

from The Scientist – by David Smith

In the largest microbial eukaryote genetic sequencing effort ever attempted, researchers are investigating the transcriptomes of 700 marine algae species.

Get your bioinformatic boots on because the National Center for Genome Research (NCGR), in collaboration with the Gordon and Betty Moore Foundation and the international scientific community, is sequencing the transcriptomes from hundreds of the planet’s most bizarre and fascinating marine algae. The results from this project, which is to be the largest of its kind ever attempted, will illuminate marine microbial diversity and its pivotal role in shaping ocean ecosystems and the air we breathe. Read more

Incoming search terms:

• deep sequencing principle
• rna-seq: a method for comprehensive transcriptome analysis
• RNA-seq algae

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]

Incoming search terms:

• trans-abyss tutorial
• reference based transcriptome assembly
• Next-generation transcriptome assembly

For non-model organisms lacking well-defined genomes, de novo assembly is typically required for downstream RNA-Seq analyses, including SNP discovery and identification of genes differentially expressed by phenotypes. Although RNA-Seq has been successfully used to sequence many non-model organisms, the results of de novo assembly from short reads can still be improved by using recent bioinformatic developments. Read more

Incoming search terms:

• RNA-seq wheat
• wheat RNA-SEQ
• ribodepletion
• rna seq de novo assembly
• RNA seq in wheat
• wheat rna seq
• how to analyse wheat RNA-seq datas
• de novo rna seq
• wheat rnaseq
• wheat rna seq database expression

from Genetic Engineering News

Incoming search terms:

• bioinformatics wallpaper
• bioinformatics wallpapers
• bioinformatics ppt
• transcriptomics drug target
• rnaseq drug sensitivity in cancer
• rna-seq and drug
• rnaseq after and before drug
• bioinformatics
• use rna seq to evaluate cancer treatment
• rna seq drug target

The tomato genome has been published in Nature on May 31, 2012, culminating years of work by the Tomato Genome Consortium, a multi-national team of scientists from 14 countries.

The Tomato Genome Consortium. (2012) The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485: 635–641. [article]

Read more in the Nature Editorial.

Read more at the Boyce Thompson Institute site.

Access the genome data on the SGN Tomato Genome Page.

Press Releases

China | Germany | Max Planck | Helmholtz | Israel | Japan | Korea | Spain | United Kingdom | USA: University of Oklahoma | Colorado State University | Boyce Thompson Institute

The Tomato Genome in the News

Nature | Reuters India | New York Times | BBC | Natural History Museum, London | VTM Het Nieuws (Belgium) | more Belgian links | Die Zeit (Germany) | Reuters UK | El Mundo (Spain) | Publico (Spain) | ABC (Spain) | Agenciasinc (Spain) | Le Figaro (France) | Sciences Et Avenir (France) | Lenta.ru (Russia)

Incoming search terms:

• Tomato RNA Seq
• chip seq and tomato
• tomato transcriptome RNA-seq
• tomato rna seq and microarray
• tomato chip-seq
• rna-seq tomato
• rna seq tomato
• questions on micrornas in tomato
• katja machemer-noonan
• cuffdiff tomato

Sugar beet (Beta vulgaris sp. vulgaris) crops account for about 30% of world sugar. Sugar yield is compromised by reproductive growth hence crops must remain vegetative until harvest. There is no sugar beet reference genome, or public expression array platforms. RNA-Seq has now enabled the generation of the first reference transcriptome for sugar beet and the study of global transcriptional responses in the shoot apex to vernalization and GA treatment, without the need for a reference genome or established array platforms. Comprehensive bioinformatic analysis identified transcriptional programmes associated with different sugar beet genotypes as well as biological treatments; thus providing important new opportunities for basic scientists and sugar beet breeders. Transcriptome-scale identification of agronomically important traits as used in this study should be widely applicable to all crop plants where genomic resources are limiting.

Mutasa-Gottgens ES, Joshi A, Holmes HF, Hedden P,  Gottgens B. (2012) A new RNASeq-based reference transcriptome for sugar beet and its application in transcriptome-scale analysis of vernalization and gibberellin responses. BMC Genomics [Epub ahead of print]. [abstract]

Incoming search terms:

• beta vulgaris
• sugar beet
• beta vulgaris ppt
• Beet sugar analysis
• gwas plant breeding sugar beet

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• SEQanswers – RNA Sequencing

  • RNAseq (SOLiD) from 18 - 200 nt June 18, 2013
    We are interested in small non-coding RNAs. Whomever you ask about the size range of small RNAs, you get a different answer. ;) Lets assume, small... […]
    GenomicIBK
  • Unmapped ratio very high on mouse genome June 17, 2013
    Hi, My problem regards RNA-Seq data. I've downloaded public data (SAGE libs w/ 6 different samples from mouse liver ) to analyse using ArrayStudio.... […]
    le.nono
  • RNASeq: Read length different from expected June 17, 2013
    Hello all, I have received paired-end reads for 40 samples. The reads are supposed to be 100bp per end. Instead, 20 of my samples are 101bp per... […]
    gogodidi
  • How to install xgawk June 16, 2013
    Hi, This is Shrujan, i have a problem while running RNA Sequencing QC. It shows an error that xgawk is not found. So please help me installing... […]
    shrujan
  • RNA Sequencing QC Error while using with Sequence_QC.sh file June 15, 2013
    Hi, This is Shrujan kumar Madadha, I had an error while running QC for Drosophila Yukuba fastq RNA file using Sequence_QC.sh file of FASTX... […]
    shrujan
  • Cuffmerge related query June 12, 2013
    I have a query regarding what samples should be merged using cuffmerge, when you have multiple phenotypes (each with replicates). Lets say my mouse... […]
    ParthavJailwala

• Biostar – RNA-Seq

  • edgeR: very low p-value and very high variance within the group of replicates. What's my problem??
    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 […]