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The Galaxy Team announced yesterday that the first free public resource for RNA-seq analysis is now available through the Galaxy public server at http://usegalaxy.org .

Galaxy now supports both Tophat and Cufflinks and also provides useful utilities for manipulating and visualizing GTF files, which are common outputs for a Tophat-Cufflinks pipeline.

Here is an exercise for learning about how to use Galaxy for RNA-seq analysis.

Galaxy is an open and free web-based platform for performing accessible, reproducible, and transparent NGS analyses. Users can start using Galaxy by going to http://usegalaxy.org ; alternatively, Galaxy can be downloaded and run on any *NIX machine: http://bitbucket.org/galaxy/galaxy-c…wiki/GetGalaxy or run on cloud computing resources such as Amazon: http://usegalaxy.org/cloud

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Bacillus anthracis genome organization in light of whole transcriptome sequencing

Emerging knowledge of whole prokaryotic transcriptomes could validate a number of theoretical concepts introduced in the early days of genomics. What are the rules connecting gene expression levels with sequence determinants such as quantitative scores of promoters and terminators? Are translation efficiency measures, e.g. codon adaptation index and RBS score related to gene expression? We used the whole transcriptome shotgun sequencing of a bacterial pathogen Bacillus anthracis to assess correlation of gene expression level with promoter, terminator and RBS scores, codon adaptation index, as well as with a new measure of gene translational efficiency, average translation speed. We compared computational predictions of operon topologies with the transcript borders inferred from RNA-Seq reads. Transcriptome mapping may also improve existing gene annotation. Upon assessment of accuracy of current annotation of protein-coding genes in the B. anthracis genome we have shown that the transcriptome data indicate existence of more than a hundred genes missing in the annotation though predicted by an ab initio gene finder. Interestingly, we observed that many pseudogenes possess not only a sequence with detectable coding potential but also promoters that maintain transcriptional activity.

Martin J, Zhu W, Passalacqua KD, Bergman N, Borodovsky M. (2010) Bacillus anthracis genome organization in light of whole transcriptome sequencing. BMC Bioinformatics 11 Suppl 3:S10. [article]

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Stampy is a package for the mapping of short reads from illumina sequencing machines onto a reference genome. It’s recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions. It can map reads from a highly divergent species to a reference genome for instance. Stampy achieves high sensitivity and speed by using a fashashing algorithm and a detailed statistical model. Read more

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The DeRisi lab is pleased to release HMMSplicer to the community. This open-source software package discovers splice junctions in RNA-Seq datasets without using gene models. HMMSplicer was benchmarked on publicly available A. thaliana, H. sapiens, and P. falciparum datasets and performed well in all cases. The software was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. Information about the datasets tested, including the exact command parameters and the final results, is provided. HMMSplicer is implemented in Python and is freely available for all. The manuscript is currently under review.

The code and documentation can be found at: http://derisilab.ucsf.edu/software/hmmsplicer

Highly consistent, fully representative mRNA-Seq libraries from ten nanograms of total RNA

Biotechniques – Reports
Sengupta, S et al.

Preparation of an Illumina sequencing library for gene expression analysis (mRNA-Seq) requires microgram amounts of starting total RNA or PCR-based amplification. Here we describe a protocol based on T7 linear RNA amplification that does not introduce significant bias, requires only 10 ng total RNA, and generates a directional, fully representative, whole-transcript mRNA-Seq Illumina library that is highly consistent across over three orders of magnitude of input RNA.

(read more… )

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• SEQ OF TOTAL

Which sequencing platform/technology is best suited for RNA-Seq applications?

Platform                                  Votes

Roche/454 – GS FLX                        4
Illumina – Genome Analyzer            12
Illumina – HiSeq                             16
Applied Biosystems – SOLiD            6
Helicos – HeliScope                         2
Pacific Biosciences                          2

Total Votes                                   42

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from GEN by Richard A. Stein, M.D., Ph.D.

Which Tool Is Best Depends on the Question Asked and the System Being Surveyed

While techniques available in the past allowed only a limited number of genes to be examined at one time, genome-based microarray platforms opened the possibility to analyze entire cellular transcriptomes under specific sets of conditions. This global approach enables the visualization of discrete pathways or groups of genes, which can be surveyed under various conditions, such as a specific disease or treatment with a certain therapeutic agent.

Despite their advantages, microarrays have several limitations. “Microarrays are a great tool to start with, but to understand the biology of the system one needs to subsequently conduct more focused experiments,” advises Dr. Stephen Walker, assistant professor at Wake Forest University School of Medicine.

The recent push toward RNA sequencing is being driven not only by the need to learn about changes in the expression of a particular gene, but also by the opportunity to visualize the differential regulation of multiple splice variants for one specific gene.

“By sequencing the transcriptome, rather than labeling RNA and hybridizing on microarrays, there is a higher likelihood to pick up individual splice variants and get a much more comprehensive gene-expression profile,” explains Dr. Walker. “This provides a more comprehensive analysis but adds, at the same time, another layer of complexity, because the size of the files is quite large, and the approach, at the present time, can be cost prohibitive,” he adds.

(read more… )

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Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates. Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels. MISO also detects differentially regulated exons or isoforms. Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV cross-linking–immunoprecipitation sequencing (CLIP-seq) analysis in human cells. Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq experiments for studies of gene and isoform expression.

Katz Y, Wang ET, Airoldi EM, Burge CB. (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods [Epub ahead of print].  [abstract]

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Classical approaches to determine structures of noncoding RNA (ncRNA) probed only one RNA at a time with enzymes and chemicals, using gel electrophoresis to identify reactive positions. To accelerate RNA structure inference, the authors developed fragmentation sequencing (FragSeq), a high-throughput RNA structure probing method that uses high-throughput RNA sequencing of fragments generated by digestion with nuclease P1, which specifically cleaves single-stranded nucleic acids.

In experiments probing the entire mouse nuclear transcriptome, they accurately and simultaneously mapped single-stranded RNA regions in multiple ncRNAs with known structure. They probed in two cell types to verify reproducibility and also identified and experimentally validated structured regions in ncRNAs with, to our knowledge, no previously reported probing data.

Underwood JG, Uzilov AV, Katzman S, Onodera CS, Mainzer JE, Mathews DH, Lowe TM, Salama SR, Haussler D. (2010) FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat Methods [Epub ahead of print].  [abstract]

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Sequencing Project Aims to Uncover New Knowledge About Ocean Microeukaryotes

SANTA FE, N.M.–(BUSINESS WIRE)–The National Center for Genome Resources (NCGR) and the Gordon and Betty Moore Foundation’s (GBMF) Marine Microbiology Initiative (MMI) today announced a new research program to sequence the transcriptomes of approximately 750 marine microbial eukaryotes.

This program is intended to increase the research community’s baseline of scientific knowledge by creating catalogues of genes that specify how these interesting organisms thrive in diverse marine habitats and how they influence the atmosphere and marine ecosystems. These catalogues are also expected to improve metagenomic analyses of complex marine microbial eukaryotic communities.

Nominations for transcriptome sequencing will be solicited from the international scientific community in November 2010, and over the next eighteen months NCGR will sequence the transcripts of approximately 750 samples that are expected to represent hundreds of diverse species and strains. MMI has convened an advisory committee to assist with review of sample nominations. Sequence data and associated environmental and experimental metadata will be deposited in the Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis and the Sequence Read Archive at the National Center for Biotechnology Information, which will be the long-term homes of the sequences, assemblies and annotations.

(read the entire release… )

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From GenomeWeb

Illumina’s Q3 Revenues Jump 50 Percent on Strength of HiSeq
Illumina reported after the close of the market Tuesday that its third-quarter revenues jumped 50 percent, easily beating analysts’ consensus estimate.

Life Technologies’ Q3 Revenues Rise 8 Percent on Record SOLiD 4 Sales
The company’s CE and next-generation sequencing business saw strong results, while its PCR business suffered from difficult year-ago comparison figures, it said.

Roche’s Sequencing Business Grows 12 Percent YTD as Applied Science Sales Shrink in Q3
Roche said it has seen “robust uptake” in the European Union and Asia Pacific for the 454 GS Junior sequencer, a benchtop sequencer launched in May that is geared at small laboratories.

Helicos BioSciences Cuts 14 More Positions in Q3
The firm also cut 40 jobs in May amid its plans to restructure and focus on the molecular diagnostics market. Part of its restructuring includes the departure of J. William Efcavitch as chief technology officer.

The firm will cut another 14 positions, on top of the previously announced 40 jobs, as it aims to reduce its operating costs. Additionally, Lapidus steps down as Chairman.

Pacific Biosciences Files for IPO in Q3
The firm has raised around $370 million since its inception, and announced plans to go public in an offering that could potentially bring in $200 million.

The firm went on to sell 12.5 million shares at $16 per share, the mid-point of its estimated offering range, at the initial public offering in October.

10/14/2010 –  Mirna Therapeutics Announces Dr. David Johnson as Scientific Advisor

AUSTIN, Texas–(BUSINESS WIRE)–Mirna Therapeutics, Inc. (“Mirna”) announced today that David Johnson, M.D., F.A.C.P., of UT Southwestern in Dallas, has joined the Company as Scientific Advisor.

Dr. David Johnson recently moved from Vanderbilt University in Nashville, Tennessee, to take the helm as the Donald W. Seldin Distinguished Chair in Internal Medicine, and Chairman of the Department of Internal Medicine at the UT Southwestern Medical School in Dallas, Texas. Dr. Johnson is the recipient of numerous honors and awards and served on the Board of Directors of several key institutions and associations, including the American Society of Clinical Oncology (ASCO), the American Board of Internal Medicine, and the National Comprehensive Cancer Network (NCCN). (read the entire release… )

10/08/2010 -  Alnylam Scientists Present New Research on Expanded Delivery of RNAi Therapeutics

CAMBRIDGE, Mass.–(BUSINESS WIRE)–Alnylam Pharmaceuticals, Inc. (Nasdaq: ALNY), a leading RNAi therapeutics company presented new pre-clinical research today at the 8th International M. Judah Folkman Conference, New Frontiers in Therapeutic Development, held October 8-9, 2010 in Cambridge, Mass, highlighting significant advances in the systemic delivery of RNAi therapeutics. The new research demonstrated effective silencing of target genes in distinct cell types and tissues beyond the liver with systemic delivery of RNAi therapeutics. In particular, novel lipid nanoparticles (LNPs) were designed in collaboration with the laboratory of Daniel Anderson, Ph.D. at the Massachusetts Institute of Technology (MIT) to deliver siRNAs to the vascular endothelium, the cells that line blood vessels throughout the body. RNAi-mediated target gene silencing was observed in endothelial cells across a broad range of tissues, with duration of action lasting for over two months after a single dose. Delivery to the vascular endothelium creates new RNAi therapeutic approaches in disease areas including atherosclerosis, diabetes, inflammation, and cancer. Additional new results were also presented regarding delivery of RNAi therapeutics to immune cells. (read the entire release… )

09/30/2010 – miRagen Therapeutics’ Director of Biology to Give Plenary Lecture at Center for Heart Failure Research Symposium in Oslo, Norway

BOULDER, Colo.–(BUSINESS WIRE)–miRagen Therapeutics, Inc., a biopharmaceutical company focused on improving patients’ lives by developing innovative microRNA (miRNA)-based therapeutics for cardiovascular and muscle disease, announced that its Director of Biology, Eva van Rooij, Ph.D., will give a plenary lecture today at the 8th Annual Center for Heart Failure Research Symposium in Oslo, Norway. Dr. van Rooij’s talk, titled “Oligonucleotide-based modulation of microRNAs to control cardiovascular disease,” will review the basis for the development of a modified short nucleic acid sequence targeting microRNA-208 (antimiR-208) as a strategy for intervention in heart disease and introduce compelling new data supporting its potential as a therapeutic agent. Data to be highlighted include antimiR-208 in hypertensive rodent models of heart failure and preliminary dose ranging and toxicology analysis in non-human primates. (read the entire release… )

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• SEQanswers – RNA Sequencing

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    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... […]
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  • 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.... […]
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    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... […]
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  • 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... […]
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  • RNA Sequencing QC Error while using with Sequence_QC.sh file June 15, 2013
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  • 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... […]
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• Biostar – RNA-Seq

  • 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 […]
  • RNA-Seq: novel transcripts found. What next?
    If I were use cufflinks in de novo mode to find transcripts or genes in my data that did not align to known transcripts from UCSC or Ensembl, I wouldn't know what to do downstream of this. How would one go about confirming that these are indeed novel? What sort of validation steps would one take (computational or non-computational), what in-depth inform […]