RNA-Seq
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Rescooped by Nathan Johnson from Bioinformatics Software: Sequence Analysis
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RNA-Skim: a rapid method for RNA-Seq quantification at transcript level

RNA-Skim: a rapid method for RNA-Seq quantification at transcript level | RNA-Seq | Scoop.it
RT @genetics_blog: RNA-Skim: a rapid method for RNA-Seq quantification at transcript level http://t.co/WYOgDJHOGc #Bioinformatics #ź

Via Mel Melendrez-Vallard
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VIZBI 2013: RNA sequencing - Cole Trapnell

Copyright Broad Institute, 2013. All rights reserved. VIZBI 2013: RNA sequencing - Cole Trapnell VIZBI 2013, the 4th international meeting on Visualizing Bio...
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Analyzing TCGA's RNA-seq data of 240 prostate cancer patients

The Cancer Genome Atlas (TCGA) is a large data repository of multi-omics and clinical data of cancers.This movie tutorial shows how to get RNA-seq data and a...
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BroadE: The General Approach to De novo RNA-Seq Assembly Using De Bruijn Graphs

Copyright Broad Institute, 2013. All rights reserved. Trinity, developed at the Broad Institute, represents a novel method for the efficient and robust de no...
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RNA-Seq in the Genebuild

What are the RNA-Seq models in Ensembl, and how were they determined? How does RNA-Seq data contribute to Ensembl gene sets? Can I upload my own RNA-Seq data...
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Rescooped by Nathan Johnson from Databases & Softwares
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Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads

Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads | RNA-Seq | Scoop.it

Abstract

Motivation: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored.

Results: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with qRT-PCR data.

Availability: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html


Via Biswapriya Biswavas Misra
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Rescooped by Nathan Johnson from Bioinformatics Software: Sequence Analysis
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Isoform Resolution Analysis of RNA-Seq

Isoform Resolution Analysis of RNA-Seq | RNA-Seq | Scoop.it
VIZBI 2013: RNA sequencing – Cole Trapnell
VIZBI 2013, the 4th international meeting on Visualizing Biological Data was held March 20-22, at the Broad Institute.

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PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution | RNA-Seq Blog

PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution | RNA-Seq Blog | RNA-Seq | Scoop.it
Correctly estimating isoform-specific gene expression is important for understanding complicated biological mechanisms and for mapping disease susceptibility

Via Mel Melendrez-Vallard
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Rescooped by Nathan Johnson from Plant Genomics
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Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads

Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads | RNA-Seq | Scoop.it

Abstract

Motivation: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored.

Results: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with qRT-PCR data.

Availability: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html


Via Biswapriya Biswavas Misra
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Rescooped by Nathan Johnson from Bioinformatics Software: Sequence Analysis
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Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows

RT @SAGRudd: Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows http://t.co/rj0VtQ5bEn

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Analysis of a simple RNA-Seq experiment

This tutorial shows the analysis of a simple RNA-Seq experiment containing two samples and goes through the process of quantitating and normalising the data,...
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RNA-seq Analysis Simplified

Upload - Configure - Launch! Three simple steps to get started with your RNA-seq analysis on the Maverix Analytic Platform. Visit Maverix Biomics to learn mo...
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Rescooped by Nathan Johnson from Tools and tips for scientific tinkers and tailors
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SSP: An interval integer linear programming for De novo transcriptome assembly and isoform discovery of RNA-seq reads | RNA-Seq Blog

SSP: An interval integer linear programming for De novo transcriptome assembly and isoform discovery of RNA-seq reads | RNA-Seq Blog | RNA-Seq | Scoop.it
Recent advances in the sequencing technologies have provided a handful of RNA-seq datasets for transcriptome analysis. However, reconstruction of full-length

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NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data - up-to-the-minute news and headlines.

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Biswapriya Biswavas Misra's curator insight, July 13, 2013 10:13 PM

RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models.

Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models.

The method has advantages in handling different read distributions, but there hasn't been an efficient program to implement this algorithm.

Results: We developed an efficient implementation of the algorithm in the program NURD. It uses a binary interval search algorithm.

The program can correct both the global tendency of sequencing bias in the data and local sequencing bias specific to each gene. The correction makes the isoform expression estimation more reliable under various read distributions.

And the implementation is computationally efficient in both the memory cost and running time and can be readily scaled up for huge datasets.

Conclusion: NURD is an efficient and reliable tool for estimating the isoform expression level. Given the reads mapping result and gene annotation file, NURD will output the expression estimation result.

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Discovery of Novel Genes and Gene Isoforms by Integrating Transcriptomic and Proteomic Profiling | RNA-Seq Blog

Discovery of Novel Genes and Gene Isoforms by Integrating Transcriptomic and Proteomic Profiling | RNA-Seq Blog | RNA-Seq | Scoop.it
Comprehensively identifying gene expression in both transcriptomic and proteomic levels of one tissue is a prerequisite for a deeper understanding of its

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Improved Transcript Isoform Discovery using ORF Graphs | RNA-Seq Blog

Improved Transcript Isoform Discovery using ORF Graphs | RNA-Seq Blog | RNA-Seq | Scoop.it
High-throughput sequencing of RNA in vivo facilitates many applications, not the least of which is the cataloging of variant splice isoforms of protein-coding

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Sailfish: Alignment-free isoform quantification from RNA-seq reads

Sailfish: Alignment-free isoform quantification from RNA-seq reads | RNA-Seq | Scoop.it
Sailfish - the fastest and alignmnet-free isoform-level quantitation of RNA-seq data is now published on Nature Biotechnology. Sailfish enables alignment-free isoform quantification from RNA-seq re...

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RNA in single cells sequenced and up to 1000-fold variability in transcription levels found

RNA in single cells sequenced and up to 1000-fold variability in transcription levels found | RNA-Seq | Scoop.it

A team of scientists at the Klarman Cell Observatory at the Broad Institute recently completed an effort to read, or sequence, all the RNA — the “transcriptome” — in individual immune cells. Whereas DNA in a cell’s genome represents its blueprint for making the building blocks of cells, RNA is more like the cell’s contractor, turning that blueprint into proteins. By sequencing RNA in single cells, scientists can obtain a picture of what proteins each cell is actively making and in what amounts.

The Broad researchers sought to adapt a recently developed technique for single-cell RNA sequencing, known as SMART-Seq, and apply it to a model of immune cell response well-studied by Regev, Broad senior associate member Nir Hacohen, and their fellow researchers. In this model, immune cells known as bone-marrow derived dendritic cells (BMDCs) are exposed to a bacterial cell component that causes the cells to mount an immune response.

Working with scientists in the Broad’s Genomics Platform, notably research scientists Joshua Levin and Xian Adiconis, the team established the SMART-Seq method for use in their model system, using it to gather RNA sequence data from 18 BMDCs in this pilot phase.

The team first analyzed the data for differences in expression, or activity, of various genes among the cells, seen as alterations in RNA abundance. Although they were working with a single cell type — BDMCs — they did expect to see some variation in gene expression as cells activated various pathways during their immune response. But the team discovered that some genes varied greatly, with 1000-fold differences in the expression levels between cells. “We went after a narrowly defined cell type that has a specific function that we think of as being very uniform,” said Shalek. “What we saw was striking — a tremendous variability that wasn’t expected.”


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Jeroen Verheyen's curator insight, May 29, 2013 3:38 AM

Even at the bottom, enities are unique! However, to what extend is this a relevant observation? Does "some genes" refer to genes relevant to the triggered immune response or not? Of course one can imagine that one cell can encounter somewhat more or less stress during an assay, giving rise to different expression levels. Also events such as cell-cell contact in vitro can strongly alter gene expression. However, the observation would be very interesting if we could confirm that the the strong variance in expression was induced specifically by the immune trigger. Spatial and temporal variations in triggers could induce different expression patterns in identical cells. Imagine that a BMDC cell n° 1 is the first t encounter the bacterial components. This cell will then undergo changes in its expression levels and produce cytokines (chemical triggers that message to neighbouring cells). Now, cell n° 2 will get challenged with the bacterial components and the cytokines. And maybe cell n° 3 will never get into contact with the bacterial components, and only with the cytokines. Furthermore, at some time at some place, cytokine levels may be so high that a negative feedback loop is induced (for example expression of countering cytokine). So, one can imagine that in this complex temporal and spatial mixture of triggers and messages, every cell will respond somewhat different. It is possible that this differentiation and pattern formation is important to enhance the immune response by creating different cell with different purposes.