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Mar
30
RNA-Seq of Bacteria
Filed Under Publications | 1 Comment
A Team of researchers at the Broad Institute set out to establish a robust and scalable RNA-seq process applicable to cultured bacteria as well as to complex community transcriptomes.
They determined that an effective process should:
a) reduce rRNA sequences to very low levels
b) accurately maintain relative representation of transcript sequences
c) be equally successful for any species
d) work well with RNA of varying quality
e) be highly reproducible
Their resulting process does accommodate both intact and fragmented starting RNA and combines efficient rRNA removal with strand-specific RNA-seq. Application of this approach to an RNA mixture derived from three diverse cultured bacterial species and to RNA isolated from clinical samples resulted in highly reproducible expression profiles that correlated well with profiles representing undepleted total RNA.
- Giannoukos G, Ciulla DM, Huang K, Haas BJ, Izard J, Levin JZ, Livny J, Earl AM, Gevers D, Ward DV, Nusbaum C, Birren BW, Gnirke A. (2012) Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol 13(3), R23. [article]
Incoming search terms:
- bacterial rna seq analysis
- rnaseq bacteria
- bacterial transcriptome rna seq
- bacteria rna seq protocol
- rna-seq bacterial cost
- maize plant
- bacterial rnaseq data analysis
- bacterial rna seq protocol
- bacterial rna-seq alignment
- RNA seq and bacteria and illumina
Mar
30
RNA-Seq at AACR 2012
Filed Under Events | Leave a Comment
The annual meeting of the American Association of Cancer Research (AACR) will be held next week in Chicago. RNA-Seq is one of many new technologies enabling significant discoveries in the areas of cancer research, diagnosis and treatment. Here is just a partial list of some of the presentations related to RNA-Seq that you can attend at the meeting next week.
Sat, Mar 31, 9:30 – 9:50 AM
Global run on sequencing (GRO-seq) for nascent RNA detection
Incoming search terms:
- pancreatic cancer rnaseq
Mar
29
Roche vs Illumina – Round 3
Filed Under News | Leave a Comment
Roche is increasing its offer to purchase Illumina from $44.50 to $51 per share. This represents a deal valued at almost $6.6 billion, cash. Amazing!
Though no deal has yet been struck, because this is a cash deal that has been made public by Roche, the FTC is already investigating the acquisition for violations of antitrust laws.
Illumina has advised their stockholders to defer taking any action in response to Roche’s increased offer. (Illumina press release)
“Based on our discussions with Illumina shareholders we have seen interest to accelerate the takeover process,” says Severin Schwan, CEO of Roche. “As a result, we are increasing our offer price to $51.00 per share. Roche’s preference continues to be a negotiated transaction. We look forward to the possibility of a swift completion that offers immediate value to Illumina’s shareholders.” (Roche’s press release)
Below is the text of the letter Roche sent on March 29, 2012 to Jay Flatley, President and Chief Executive Officer of Illumina, Inc:
Dear Jay,
Over the past several weeks, we have had a number of productive discussions with Illumina’s shareholders and we have observed the market reaction to our offer to acquire Illumina. We are also cognizant of your statements that you regarded our offer price of $44.50 as insufficient to provide a basis for discussions between our companies.
In light of this, we are increasing our offer for all outstanding shares of Illumina to $51.00 per share. Our revised offer represents a 15% premium to our offer on January 25, 2012 and a substantial premium of 88% over Illumina’s closing stock price on December 21, 2011, the day before market rumors about a potential transaction between Roche and Illumina drove Illumina’s stock price significantly higher. It also represents a 34.1x multiple of Illumina’s projected forward earnings based upon analysts’ current consensus estimates for 2012.
As you know from our prior communications, it has been and remains Roche’s preference to conclude a negotiated transaction with Illumina. We hope that you will agree that our new price presents a very attractive opportunity to your shareholders and that the interests of your shareholders and the fiduciary responsibilities of you and your Board require that you agree to enter into discussions with us.
If you continue to decline to negotiate with us, we will have no choice but to continue our effort to effect a transaction unilaterally. However, I strongly hope that you will either agree to commence discussions with us now or remove all obstacles so that your shareholders can make their own determinations about the adequacy of our increased offer.
I look forward to hearing from you.
Sincerely,
Franz B. Humer
Chairman, Roche Holding Ltd
(read the Genomeweb News story… )
(read the Genetic Engineering News story… )
Incoming search terms:
- RNA seq illumina
- illumina rna seq
- illumina rna sequencing
- illumina roche purchase ppt
- info aedes org pe loc:US
Mar
28
from Genomeweb
74% of core labs are doing more sequencing than last year. 36% are doing less microarray work.
Incoming search terms:
- RNA microarray ppt
Mar
27
Prostate cancer remains a leading cause of cancer morbidity and mortality in men, accounting for approximately one million new cases and 260 000 deaths per year worldwide. Incidence and mortality rates vary widely across geographic regions and ethnic groups. In particular, Asian populations have a substantially lower incidence rate than Caucasians or African Americans. The mechanism underlying these differences remains unclear.
In their recent study of prostate cancer in Chinese patients, Ren and colleagues used RNA-seq to profile genetic aberrations in 14 patient tumor versus adjacent normal tissues and confirmed their findings in an independent cohort of 54 patient tumor samples. Their results provided a global view of the transcriptome, identified tumor-associated gene fusions and somatic single nucleotide mutations, and monitored the expression of long non-coding RNAs and alternatively spliced genes.
This study yielded new insights into the pathogenesis of prostate cancer in the Chinese population.
See the review: Insights into Chinese prostate cancer with RNA-seq at Nature. com
- Ren S, Peng Z, Mao JH, Yu Y, Yin C, Gao X, Cui Z, Zhang J, Yi K, Xu W, Chen C, Wang F, Guo X, Lu J, Yang J, Wei M, Tian Z, Guan Y, Tang L, Xu C, Wang L, Gao X, Tian W, Wang J, Yang H, Wang J, Sun Y. (2012) RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Res [Epub ahead of print]. [article]
Incoming search terms:
- prostate cancer rna seq blog
- chinese prostate cancer better results than caucasians
- prostate cancer chip
- prostate samples sequencing by RNA-seq
- prostate stsatistics for china
- prostatic cancer rnaseq
- RNA sequence prostrate cancer
Mar
26
Maize (Zea mays L.) Genome Diversity as Revealed by RNA-Sequencing
Filed Under Transcriptome Sequenced | Leave a Comment
Maize is rich in genetic and phenotypic diversity. Understanding the sequence, structural, and expression variation that contributes to phenotypic diversity would facilitate more efficient varietal improvement. RNA based sequencing (RNA-seq) is a powerful approach for transcriptional analysis, assessing sequence variation, and identifying novel transcript sequences, particularly in large, complex, repetitive genomes such as maize. In this study, the authors sequenced RNA from whole seedlings of 21 maize inbred lines representing diverse North American and exotic germplasm.
- Hansey CN, Vaillancourt B, Sekhon RS, de Leon N, Kaeppler SM, Buell CR . (2012) Maize (Zea mays L.) Genome Diversity as Revealed by RNA-Sequencing. PLoS One 7(3), e33071. [article]
Incoming search terms:
- Zea mays
- maize
- jagung
- Zea mays L
- rna-seq maize
- maize (zea mays l )
- (Zea mays L )
- RNA-Seq gene annotation maize
- whole transcriptome sequencing maize
Mar
26
RNA-Seq Tutorial from the iPlant Collaborative
Filed Under Analysis Pipelines, Presentations | Leave a Comment
iPlant Collaborative is a community of researchers, educators, and students working to enrich all plant sciences through the development of cyberinfrastructure – the physical computing resources, collaborative environment, virtual machine resources, and interoperable analysis software and data services– that are essential components of modern biology.
Incoming search terms:
- iplant rna-seq
Mar
23
ShortFuse 0.2 – Gene Fusion Detection using Ambiguously Mapping RNA-Seq Read Pairs
Filed Under Splicing and Junction Mapping | Leave a Comment
Paired-end whole transcriptome sequencing provides evidence for fusion transcripts. However, due to the repetitiveness of the transcriptome, many reads have multiple high-quality mappings. Previous methods to find gene fusions either ignored these reads or required additional longer single reads. This can obscure up to 30% of fusions and unnecessarily discards much of the data. We present a method for using paired-end reads to find fusion transcripts without requiring unique mappings or additional single read sequencing. Using simulated data and data from tumors and cell lines, we show that our method can find fusions with ambiguously mapping read pairs without generating numerous spurious fusions from the many mapping locations.
Availability: A C++ and Python implementation of the method demonstrated in this paper is available at http://exon.ucsd.edu/ShortFuse
Kinsella M, Harismendy O, Nakano M, Frazer KA, Bafna V. (2012) Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs. Bioinformatics 27(8), 1068-75. [abstract]
Incoming search terms:
- how to determine the proportions of alternatively differentiallly spliced transcripts
- scoring rna-seq splice site classifier
- shortfuse fusion
- statistical assessment of gene fusion detection algorithms using rna seq data
- tx-fuse gene fusion
Mar
22
Incoming search terms:
- rna seq analysis tutorial
- rna seq data analysis tutorial
- rna-seq data analysis tutorial
- rna-seq analysis tutorial
- mapping rna-seq
- rna mapping
- what is rna seq data
- RNA-seq Data Handling and Analysis
- rna-seq % mapping
- RNA-seq map
Mar
21
RNA-Seq Data Analysis – Poll Results
Filed Under Poll Results, Reader Conributions | Leave a Comment
Initially we asked: Do we yet have a firm handle on the most appropriate/accurate pipeline for analysis of RNA-Seq datasets? Almost 90% of our readers said NO. So in our last poll, we tried to dig a little deeper and asked: What is the biggest challenge when performing RNA-Seq data analysis? See results below. (N=100)
Check out or latest reader poll below in the right-hand sidebar and cast your vote!
Incoming search terms:
- find fusion transcripts from transcriptome assembly
- challenges in analysis of rnaseq data
Mar
20
Sugar Beet Transcriptome
Filed Under Transcriptome Sequenced | Leave a Comment
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
Mar
16
RNA Advances Reshape Prevailing Wisdom
Filed Under Information | Leave a Comment
Expanding on a concept originally coined by Walter Gilbert in 1986, Thomas Cech recently described two RNA worlds—a hypothetical, primordial world in which the same molecule combined informational and catalytic properties, and a contemporary world, forged by a spectrum of RNA-centered activities. While during the early days, following the discovery of DNA, RNA was thought to be the less interesting molecule, this view has dramatically changed in recent years.
Advances in RNA biology are reshaping concepts that have been prevailing for a long time. For example, in the field of developmental biology, decisions were traditionally envisioned mostly in terms of the progressive expression of different combinations of transcription factors that are induced by specific combinations and concentrations of growth differentiation factors. (read more…)
- Stein, RA. (2012) RNA Advances Reshape Prevailing Wisdom. Gen Eng News 32(6). [article]
Incoming search terms:
- illumina next generation sequencing whole exome
Mar
16
SAMSCOPE is a lightweight visualization system accelerated by OpenGL designed to visualize complex ChIP-Seq and RNA-Seq features such as polarity as well as coverage across whole 3Gbp genomes such as Human. The extensive preprocessing and fast OpenGL interface of SAMSCOPE provides instantaneous and intuitive browsing of complex data at all levels of detail across multiple experiments. SAMSCOPE overcomes the limitations of existing SAM visualization tools which may be limited to a small region of the genome or a relatively small number of reads.
Availability and Implementation: The SAMSCOPE software, implemented in C++ for Linux, with source code, binary packages, and documentation are freely available from http://samscope.dna.bio.keio.ac.jp
- Popendorf K, Sakakibara Y. (2012) SAMSCOPE: An OpenGL based real-time interactive scale-free SAM viewer. Bioinformatics [Epub ahead of print]. [abstract]
Incoming search terms:
- NGS RNA-seq visualization tool
- sam visualization rnaseq
- visualize sam
Social Networking Pages
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SEQanswers – RNA Sequencing- RNAseq (SOLiD) from 18 - 200 nt June 18, 2013We 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, 2013Hi, 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, 2013Hello 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, 2013Hi, 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, 2013Hi, 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, 2013I have a query regarding what samples should be merged using cuffmerge, when you have multiple phenotypes (each with replicates). Lets say my mouse... […]ParthavJailwala
- RNAseq (SOLiD) from 18 - 200 nt June 18, 2013
- Biostar – RNA-Seq
- Normalising tag count to RPKMHi! 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 terminologyThis 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 coverageHi, 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 countingI 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 […]
- Normalising tag count to RPKM