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Resistance to tamoxifen (Tam), a widely used antagonist of the estrogen receptor (ER), is a common obstacle to successful breast cancer treatment. While adjuvant therapy with Tam has been shown to significantly decrease the rate of disease recurrence and mortality, recurrent disease occurs in one third of patients treated with Tam within 5 years of therapy. A better understanding of gene expression alterations associated with Tam resistance will facilitate circumventing this problem.

Using an RNA-Seq approach and a new bioinformatics model, a team led by researchers at Penn State University compared the transcriptomes of Tam-sensitive and Tam-resistant breast cancer cells for identification of genes involved in the development of Tam resistance. They identified differential expression of 1215 mRNA and 513 small RNA transcripts clustered into ERα functions, cell cycle regulation, transcription/translation, and mitochondrial dysfunction. The extent of alterations found at multiple levels of gene regulation highlights the ability of the Tam-resistant cells to modulate global gene expression.

Alterations of small nucleolar RNA, oxidative phosphorylation, and proliferation processes in Tam-resistant cells present areas for diagnostic and therapeutic tool development for combating resistance to this anti-estrogen agent.

• Huber-Keener KJ, Liu X, Wang Z, Wang Y, Freeman W, Wu S, Planas-Silva MD, Ren X, Cheng Y, Zhang Y, Vrana K, Liu CG, Yang JM, Wu R.(2012) Differential Gene Expression in Tamoxifen-Resistant Breast Cancer Cells Revealed by a New Analytical Model of RNA-Seq Data. PLoS One 7(7), e41333. [article]

Incoming search terms:

• microRNA ppt
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• transcriptome breast cancer

Drought stress affects cereals especially during the reproductive stage. Researchers at Virginia Tech University studied the maize (Zea mays) drought transcriptome using RNA-Seq analysis to compare drought treated and well-watered fertilized ovary and basal leaf meristem tissue.

More drought responsive genes responded in the ovary compared to the leaf meristem. Gene Ontology (GO) enrichment analysis revealed massive decrease in transcript abundance of cell division and cell cycle genes in the drought stressed ovary only.

The data are discussed in the context of the susceptibility of maize kernel to drought stress leading to embryo abortion, and the relative robustness of dividing vegetative tissue taken at the same time from the same plant subjected to the same conditions. The scientists working hypothesis involves signaling events associated with increased ABA levels, decreased glucose levels, disruption of ABA/sugar signaling, activation of PCD/senescence through repression of a PLC-mediated signaling pathway, and arrest of the cell cycle in the stressed ovary at 1DAP. Increased invertase levels in the stressed leaf meristem, on the other hand, resulted in that tissue maintaining hexose levels at an “unstressed” level, and at lower ABA levels, which was correlated with successful resistance to drought stress.

• Kakumanu A, Ambavaram MM, Klumas CM, Krishnan A, Batlang U, Myers E, Grene R, Pereira A. (2012) Effects of Drought on Gene Expression in Maize Reproductive and Leaf Meristem Tissue Revealed by RNA-Seq. Plant Physiol [Epub ahead of print]. [article]

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SpliceSeq is a resource for RNA-Seq data that provides a clear view of alternative splicing and identifies potential functional changes that result from splice variation. It displays intuitive visualizations and prioritized lists of results that highlight splicing events and their biological consequences. SpliceSeq unambiguously aligns reads to gene splice graphs, facilitating accurate analysis of large, complex transcript variants that cannot be adequately represented in other formats.

Availability and Implementation: SpliceSeq is freely available at http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview

The application is a Java program that can be launched via a browser or installed locally. Local installation requires MySQL and Bowtie.

• Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. (2012) SpliceSeq: A Resource for Analysis and Visualization of RNA-Seq Data on Alternative Splicing and Its Functional Impacts. Bioinformatics [Epub ahead of print]. [article]

Date                October 15-17th 2012

Location          Amsterdam Medical Centre (AMC)
Keywords       Next-generation sequencing, NGS, data analysis, RNA-seq, transcriptome
Organiser         NBIC

Read more

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Date and Time

10:00AM – 3:00PM on July 27, 2012

Location

TW Keating Building Room 433, University of Arizona

Primary Contact

Please contact 411@iplantcollaborative.org with any questions.

Register

http://www.iplantcollaborative.org/forms/registration-form-rnaseq-workshop-university-arizona

Details

THIS WORKSHOP IS FULL; we are keeping a waitlist if you would like to register.

This RNA-Seq Workshop will be a 4-hour workshop with two 2-hour sections and a 1-hour break.

More detailed information can be found on this event’s wiki page.

from GenomeWeb News

Illumina has countersued Intelligent Bio-Systems and Qiagen, claiming they infringe several of its sequencing-related patents, the company said today.

The suit follows several weeks after Qiagen acquired IBS, and a few months after Columbia University sued Illumina in March for allegedly infringing five sequencing patents licensed by IBS.

In counterclaims related to that suit, Illumina alleges that IBS and Qiagen infringe three of its patents — US Patent Nos. 7,057,026; 7,785,796; and 8,158,346; all entitled “Labelled Nucleotides” and all expiring in December 2022. Read more

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Ensembl gene annotation provides a comprehensive catalogue of transcripts aligned to the reference sequence. It relies on publicly available species specific and orthologous transcripts plus their inferred protein sequence. The accuracy of gene models is improved by increasing the species specific component which can be cost-effectively achieved using RNA-Seq. Two zebrafish gene annotations are presented in Ensembl version 62 built on the Zv9 reference sequence.

Firstly, RNA-Seq data from five tissues and seven developmental stages were assembled into 25,748 gene models. A 3′ end capture and sequencing protocol was developed to predict the 3′ ends of transcripts and 46.1% of the original models were subsequently refined. Read more

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The Cancer Genome Atlas project plans to profile genomic changes in 20 different cancer types and has so far published results on two cancer types. They now present results from multidimensional analyses of human colorectal carcinoma (CRC). To characterize somatic alterations in colorectal carcinoma, they conducted a genome-scale analysis of 276 samples, analysing exome sequence, DNA copy number, promoter methylation and messenger RNA and microRNA expression.

The Cancer Genome Atlas Network (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337. [article]

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International Conference on Bioinformatics and Computational Biology – BIOCOMP BG 2012

20-21 Sept – Varna, Bugaria

The conference aims to provide an ideal platform for people to share research ideas and experiences in the fields of Bioinformatics, Computational Biology and related areas.

(Conference Website)

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Chromosome rearrangements that result in gene fusions have important roles in the initial steps of tumorigenesis, especially in leukemias and lymphomas, but the biological and clinical impact of gene fusions in common solid tumors are less understood.

Researchers at the Mayo Clinic set out to discover novel translocations that could result in gene fusions in oropharyngeal squamous cell carcinomas (OPSCCs). They identified translocations using 2 different bioinformatics pipelines, SnowShoes-FTD and FusionHunter, examining data from 11 paired-end RNA sequencing (RNA-Seq) data in OPSCC. Translocations were validated by RT-PCR and Sanger sequencing analysis. Two novel cancer-specific translocations involving MGST3-ZMAT5 and MS4A7-C2CD3 were found in 2 of the tumor samples tested. However, these translocations were found only in the single tumor.

• Wang VW, Laborde RR, Asmann YW, Li Y, Ma J, Eckloff BW, Tombers NM, Olsen SM, Moore EJ, Olsen KD, Smith DI. (2012) Search for chromosome rearrangements: New approaches toward discovery of novel translocations in head and neck squamous cell carcinoma. Head Neck [Epub ahead of print]. [abstract]

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RNA-Sequencing Data Analysis – A Clinical Research Perspective  

You are invited to join us for a webinar on, “RNA-Sequencing Data Analysis – A Clinical Research Perspective.”

Date:	Wednesday, July 18, 2012
Time:	1 p.m. Eastern, 10 a.m. Pacific, 7 p.m. Central Europe
Duration:	1 hour

Webinar Description:

Next Generation Sequencing applications allow biomedical researchers and clinicians to examine entire transcriptomes to identify both known and novel changes.  The breadth of information gained from one application, RNA-Seq, spans a multitude of levels including large structural changes (gene fusion), differential gene expression, differential splicing, and nucleotide changes.  A major challenge facing researchers and clinicians is efficiently analyzing massive NGS data sets with best practices bioinformatics while maintaining busy research and clinic responsibilities.

In this webinar Dr. Rebecca Laborde from the Mayo Clinic will present firsthand how GeneSifter Analysis Edition supports researchers and clinicians going from raw data to published results quickly.  Also, Dr. Eric Olson from Geospiza, PerkinElmer will discuss the bioinformatics challenges and solutions for RNA-Seq data analysis..

Speakers:

Rebecca Laborde, PhD.
Postdoctoral Research Fellow
Mayo Clinic

Eric Olson, PhD.
VP Product Development, Geospiza
PerkinElmer

Register Now

Incoming search terms:

• mayo clinic rna research
• Data analysis of RNA sequencing

One of the computational challenges in plant systems biology is to accurately infer the transcriptional regulation relationships based on the correlation analyses of gene expression patterns. Despite the several correlation methods that have been applied in biology to analyze microarray data, concerns regarding the compatibility of these methods to the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution property of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and if necessary, introduction of novel methods into biology have been expected.

In this study, researchers at the University of Arizona compared four existing correlation methods used in microarray analysis and one novel method called Gini correlation coefficient, on previously published microarray-based and sequencing-based gene expression data in Arabidopsis and maize. The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. The analyses pinpointed the strengths and weaknesses of each method, and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation and the Tukey’s biweight correlation.

The Gini correlation method, along with the other four evaluated methods in this study, was implemented as an R package named “rsgcc” that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

The rsgcc package is available at: http://cran.r-project.org/web/packages/rsgcc/index.html

• Ma C, Wang X. (2012) Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. Plant Physiol [Epub ahead of print]. [abstract]

Incoming search terms:

• rna-seq arabidopsis database
• arabidopsis chip-seq
• illumina rnaseq public data
• pearson correlation rna-seq
• rna-seq visualization ref-seq
• strengths and weaknesses of correlation analysi

Xpression automatically processes quality filtering, mapping, expression profiling, and visualization from short read-length NGS reads in nucleotide-space (e.g., Illumina). It currently uses BWA to map reads, which allows a specified number of mismatches between read and reference genome. The expression profile provides normalization for reads per million (pM) and per million-kilobase (pKM). It can also take read orientation of the reads into account if strand-specificity was maintained during the library-prep method.

This pipeline can be run on UNIX-like systems via the command-line or graphically via Java. To run Xpression on any system, such as Windows, a fully-configured virtualized environment is also provided. This environment needs software such as VirtualBox to run, which can be downloaded and installed with a standard point-and-click wizard interface.

The Integrative Genomics Viewer (Broad) is recommended for viewing the wiggle-format visualization files, and we have found Tablet to be a good choice for viewing the sorted bam files.

The project website:
http://depts.washington.edu/cshlab/x…l/rnaseq.shtml
The code’s home (including the issue tracker and source):
https://bitbucket.org/clparallel/xpr…able/wiki/Home

Additionally, the design of the tool allows flexible use and easy modification to suit specific needs.

• Phattarasukol S, Radey M, Lappala C, Oda Y, Hirakawa H, Brittnacher M, and Harwood CS. (2012) Identification of a p-coumarate degradation regulon in Rhodopseudomonas palustris using Xpression, an integrated tool for prokaryotic RNA-seq data processing. Appl Environ Microbiol [Epub ahead of print]. [abstract]

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• SEQanswers – RNA Sequencing

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• Biostar – RNA-Seq

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    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
  • What are the best practices for SNP identification in RNA seq transcriptome data
    I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
  • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
    Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
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    Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
  • Does tophat use the library-type information for mapping, or just for the XS flag?
    When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
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