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Genetically identical populations of cells grown in the same environmental condition show substantial variabilityin gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.

Researchers at the European Bioinformatics Institute, UK have developed a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying this model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, they find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Their results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency.

Furthermore, they show that their model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.

Availability – The MATLAB source code, and a compiled version of the same, are available at: http://genomebiology.com/imedia/6132151659020737/supp4.zip

• Kim JK, Marioni JC. (2013) Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol 14(1), R7. [Epub ahead of print]. [abstract]

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RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons?exons which are consistently conserved after splicing?offers an unbiased estimation of the overall transcription of a gene.

Scienctists at the University of Sydney, Australia have developed a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are “constitutive” in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq.

When used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, this method is shown to produce robust estimates of overall gene transcription.

Availability – exClust isimplemented in an openly available R function package, sydSeq - http://www.maths.usyd.edu.au/u/jeany/software.htm

• Patrick E, Buckley M, Yang YH. (2013) Estimation of data-specific constitutive exons with RNA-Seq data. BMC Bioinformatics 14(1):31. [abstract]

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Senior Scientist / Principal Scientist, Immunoregulation
Job Number:	971088
Company Name:	Pfizer Inc.
Location:	Cambridge, MA US
Career Focus:	Healthcare & Medical / Science & Biotech

Org Marketing Statement
All over the world, Pfizer colleagues are working together to positively impact health for everyone, everywhere. Each position at Pfizer touches and contributes to the success of our business and our world. That’s why, as one of the global leaders in the biopharmaceutical industry, Pfizer is committed to seeking out inspired new talent who share our core values and mission of making the world a healthier place.

Role Description
We seek an highly motivated immunologist to work with members of the Immunoregulation Group within the Immunology and Autoimmunity Research Unit. The primary focus of the position will be to participate in the identification, evaluation and development of the next generation of novel therapies for autoimmune disease. The successful candidate will have a deep understanding of immunological mechanisms, with a focus on adaptive immunity, in health and disease. Read more

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As next generation sequencing technologies are getting more efficient and less expensive, RNA-Seq is becoming a widely used technique for transcriptome studies. Computational analysis of RNA-Seq data often starts with the mapping of millions of short reads back to the genome or transcriptome, a process in which some reads are found to map equally well to multiple genomic locations (multimapping reads).

Researchers at the Karolinska Institutet, Sweden have developed the Minimum Unique Length Tool (MULTo), a framework for efficient and comprehensive representation of mappability information, through identification of the shortest possible length required for each genomic coordinate to become unique in the genome and transcriptome. Using the minimum unique length information, they have compared different uniqueness compensation approaches for transcript expression level quantification and demonstrate that the best compensation is achieved by discarding multimapping reads and correctly adjusting gene model lengths. They have also explored uniqueness within specific regions of the mouse genome and enhancer mapping experiments. Finally, by making MULTo available to the community they hope to facilitate the use of uniqueness compensation in RNA-Seq analysis and to eliminate the need to make additional mappability files.

Availability – http://sandberg.cmb.ki.se/multo

• Storvall H, Ramsköld D, Sandberg R. (2013) Efficient and comprehensive representation of uniqueness for next-generation sequencing by minimum unique length analyses. PLoS One 8(1), e53822. [article]

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• mappability

The capsaicinoids are a group of compounds produced by chili pepper fruits and are used widely in many fields, especially in medical purposes. The capsaicinoid biosynthetic pathway has not yet been established clearly. To understand more knowledge in biosynthesis of capsaicinoids, researchers at South China Agricultural University, Guangzhou applied RNA-seq for the mixture of placenta and pericarp of pungent pepper (Capsicum frutescens L.).

The researchers have assessed the effect of various assembly parameters using different assembly software, and obtained one of the best strategies for de novo assembly of transcriptome data. They obtained a total 54,045 high-quality unigenes (transcripts) using Trinity software. About 92.65% of unigenes showed similarity to the public protein sequences, genome of potato and tomato and pepper (C. annuum) ESTs databases. Their results predicted 3 new structural genes (DHAD, TD, PAT), which filled gaps of the capsaicinoid biosynthetic pathway predicted by Mazourek, and revealed new candidate genes involved in capsaicinoid biosynthesis based on KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. A significant number of SSR (Simple Sequence Repeat) and SNP (Single Nucleotide Polymorphism) markers were predicted in C. frutescens and C. annuum sequences, which will be helpful in the identification of polymorphisms within chili pepper populations.

These data will provide new insights to the pathway of capsaicinoid biosynthesis and subsequent research of chili peppers. In addition, this strategy of de novo transcriptome assembly is applicable to a wide range of similar studies.

• Liu S, Li W, Wu Y, Chen C, Lei J. (2013) De Novo Transcriptome Assembly in Chili Pepper (Capsicum frutescens) to Identify Genes Involved in the Biosynthesis of Capsaicinoids. PLoS One 8(1), e48156. [article]

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• asia kuczek
• chili pepper lncRNA
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Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.

To address this challenge, researchers at Hospital del Mar Medical Research Institute (IMIM),  Spain have developed Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. They demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise en-richment methods. Further, they provide examples of its utility in differential pathway activity and survival analysis. Lastly, the researchers show how GSVA works analogously with data from both microarray and RNA-seq experiments.

GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data.

Availability – GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org/packages/release/bioc/html/GSVA.html.

• Hänzelmann S, Castelo R, Guinney J. (2013) GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14(1), 7. [abstract]

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6th international qPCR & Next Generation Sequencing Event
Symposium  &  Industrial Exhibition  &  Application Workshops
18th – 22 March 2013, Technical University of Munich,  Freising, Weihenstephan,  Germany
Main topic:  Next Generation Thinking in Molecular Diagnostics

CALL for scientific TALK and POSTER contributions for qPCR & NGS 2013 Event
Deadline for abstract submission is 31st Jan 2013
http://CALL.qPCR-NGS-2013.net

Symposium Sessions:

• Main Topic:   Molecular diagnostics
• Main Topic:   Next Generation Sequencing  (NGS)
• Main Topic:   Transcriptional Biomarkers
• High throughput analysis in qPCR
• Systems biology
• Single-cells diagnostics
• MIQE & QM strategies in qPCR
• non-coding RNAs – microRNA, siRNA, long non-coding RNAs
• Digital PCR  &  Nano-fluidics
• Pre-analytical Steps
• BioStatistics & BioInformatics
• qPCR data analysis
• NGS data analysis

EARLY BIRD registration peroid ends on 31st Jan 2013

Please register yourself and submit your abstracts here => http://registration.qPCR-NGS-2013.net

Incoming search terms:

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Kasper Hansen gives an introduction to RNAseq and relevant computational and statistical issues

Incoming search terms:

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Stuart M. Brown. Zuojian Tang. Center for Health Informatics & Bioinformatics. NYU School of Medicine.

Degradome sequencing for identification of miRNA targets in plants

MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation.

In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as growth, development and responses to biotic and abiotic stresses. Hundreds of miRNAs have been identified in higher plants by direct cloning or more recently by next-gen sequencing. To determine the function of these miRNAs we must first identify their targets.

Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near-perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing.

Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has-been successfully established to screen for miRNA targets in plants. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs have been verified indicating that it is an efficient strategy to identify smRNA targets on a large scale in plants.

Degradome sequencing reveals miRNA targets by globally identifying the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs. Sequencing reads are mapped to mRNAs and the 5′ terminal nucleotide of miRNA-cleaved mRNA fragments corresponds to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript. Confirmed miRNA targets are presented in the form of target plots (t-plots).

Read more

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• degradome
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• illumina degradome sequencing
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GitHub helps people build software together.

yarden/MISO
MISO: Mixture of Isoforms model for RNA-Seq isoform quantitation

jrbustosm/rna-seq
rna-seq analysis utils

drli/RNA-seq
RNA-seq data analysis

andymckenzie/RNA-Seq
algorithms for analyzing rna-seq data

jnhutchinson/ensembl_based_RNA_seq
ensembl_based_RNA_seq

vsbuffalo/rna-seq-example
An analysis of Arabidopsis RNA-seq data (hy5 mutant and wt, two replicates each; SRA accession SRX029582)

fatPerlHacker/rna-seq-analysis-pipeline

sgivan/RNA-Seq-Toolkit
Collection of scripts to facilitate the analysis of RNA-Seq data

gusevfe/RnaSeqAB
Tool for detecting allele bias in Genome vs. RnaSeq data

luwening/RNA-Seq-RP-Pseudogenes

Lots more shared RNA-Seq Code…

Incoming search terms:

• rna-seq pipeline code
• microrna analysis from est github
• RNA-Seq Data Analysis (Tophat Cufflinks Pipeline)
• rnaseq github blog

Mapping, Visualization, Basic Analyses

May 13th – 14th 2013, Leipzig, Germany

Scope and Topics

The purpose of this workshop is to get a deeper understanding in High-Throughput Sequencing (HTS) with a special focus on bioinformatics issues. Advantages and disadvantages of current sequencing machines and their implications on data analysis will be discovered. The participants will be trained on understanding their own HTS data, finding potential problems/errors and finally start writing their own pipelines. In the course we will use a real-life RNA-seq dataset from the current market leader Illumina.

All analyses will be performed using cloud services. By saving their final cloud-images, the participants will be able to reuse all tools/pipelines and to continue their analyses after the workshop(platform independently: Windows, Mac OS, Linux).

This course will be limited to 15 participants, ideally with similar knowledge base, to allow personal assistance and efficient learning. After registration, participants will be selected on the basis of their background and in order of incoming registrations.

(find out more…)

For those who missed it… here are the slides from the recent short training class presented by the Bioinformatics & Research Computing group at the Whitehead Institute.

January 17, 2013 – RNA-seq Analysis in Galaxy

Hands-on 1 Quantification and assay for differential expression of reference annotation

Incoming search terms:

• RNA-seq training
• galaxy
• galaxy training course RNA
• glalaxy DNAseq
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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 […]