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High-throughput sequencing technologies are currently revolutionizing the field of biology and medicine, yet bioinformatic challenges in analysing very large data sets have slowed the adoption of these technologies by the community of population biologists. We introduce the ‘Simple Fool’s Guide to Population Genomics via RNA-seq’ (SFG), a document intended to serve as an easy-to-follow protocol, walking a user through one example of high-throughput sequencing data analysis of nonmodel organisms.

The Simple Fool’s Guide

De Wit P, Pespeni MH, Ladner JT, Barshis DJ, Seneca F, Jaris H, Therkildsen NO, Morikawa M, Palumbi SR. (2102) The simple fool’s guide to population genomics via RNA-Seq: an introduction to high-throughput sequencing data analysis. Mol Ecol Resour [Epub ahead of print]. [abstract]

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Services – to provide transcriptome sequencing of approximately 317 samples.

RNA-seq has been widely used to transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the Gene Expression Omnibus (GEO) do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available. Read more

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By Molika Ashford – In Sequence

German researchers have shown that RNA-sequencing can be a potent tool to identify gene expression signatures that can distinguish livestock animals illegally treated with anabolic steroids.

The team, from three German institutions, published a report in Analytical Chemistry this month evaluating RNA-seq as a tool for finding gene expression changes associated with physiological effects of growth-promoting agents.

Using RNA-seq on just a few samples from a breed of heifer, and then profiling additional samples for the most promising candidate biomarkers using RT-qPCR, the researchers were able to precisely distinguish animals treated with anabolic steroids from controls, they reported.

(read more at In Sequence…)

• Riedmaier I, Benes V, Blake J, Bretschneider N, Zinser C, Becker C, Meyer HH, Pfaffl MW. (2012) RNA-Sequencing as Useful Screening Tool in the Combat against the Misuse of Anabolic Agents. Anal Chem 84(15), 6863-68. [abstract]

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RNA-Seq

The measurement of RNA expression is a foundation of many experiments done in biomedical research. It is therefore natural that the sequencing of long and short RNA both for quantification and discovery is the most popular functional sequencing assay (Fig. 1a). Quantification of mRNA transcripts in RNA-seq is performed by calculating values reported in units of reads per kilobase per million mapped reads (RPKM) with a paired-end fragment equivalent, fragments per kilobase per million reads (FPKM), also commonly used for each gene (Fig. 1b). RPKM normalizes for differences in gene size and makes the comparison of genes within the same sample meaningful in terms of molar equivalents (Fig. 1b). As RNA-seq is not based on predetermined DNA probes to known genes, it is a powerful tool for the discovery of new exons, splice junctions, transcripts and genes as well as new small RNAs (Fig. 1c). The reads can be used to assemble transcripts that result from gene rearrangements and can also help to identify disease-associated genomic abnormalities. Properly filtered RNA-seq reads can be mined for sequence variants and RNA-editing events with tuned analysis and filtering pipelines (Fig. 1d).

• Zeng W, Mortazavi A. (2012) Technical considerations for functional sequencing assays. Nat Immunol 13(9), 802-7. [abstract]

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Date: Monday September 24 and Tuesday September 25, 2012
Location: Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology,
Center for Life Science, 3 Blackfan Circle, 11th Floor, Conference Room 11081A

RNA-Seq is emerging as a new tool to study transcriptome changes through massively parallel sequencing. Direct sequencing of cDNA or RNA offers greater resolution, sensitivity, and dynamic range for gene expression over microarray technology. However, understanding and managing the Big Data generated from RNA-Seq experiments presents a novel analytical challenge. This 2-day workshop will introduce next-generation sequencing platforms for RNA-Seq experiments and analytical strategies to process the data. Read more

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A two-hour workshop offered in conjunction with the 2012 BG Retreat and featuring an introduction to next-gen sequencing and RNA-seq, demo on data processing in R, and hands-on RNA-seq analysis exercises

Get started processing raw Illumina data and learn how to get gene expression estimates, identify differentially expressed genes, and visualize your data — all this by practical class exercises! Read more

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High-throughput sequencing currently generates a wealth of small RNA (sRNA) data, making data mining a topical issue. Processing of these large data-sets is inherently multidimensional as length, abundance, sequence composition, and genomic location all hold clues to sRNA function. Analysis can be challenging because the formulation and testing of complex hypotheses requires combined use of visualization, annotation, and abundance profiling. To allow flexible generation and querying of these disparate types of information, we have developed the shortran pipeline for analysis of plant or animal short RNA sequencing data. It comprises nine modules and produces both graphical and MySQL format output.

AVAILABILITY: shortran is freely available at: http://users-mb.au.dk/pmgrp/shortran/

• Gupta V, Markmann K, Pedersen CN, Stougaard J, Andersen SU. (2012) shortran: A pipeline for small RNA-seq data analysis. Bioinformatics [Epub ahead of print]. [abstract]

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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 to Ensembl? Answers to these questions and more…

A team led by researchers at University of Iowa has developed a microarray enrichment methodology followed by long-read, next-generation sequencing for identification of unannotated transcript isoforms expressed in two Drosophila tissues, the ovary and the testis. They have dubbed the method: Microarray-Based Capture of Novel Expressed Cell Type-Specific Transfrags (CoNECT).

These studies introduce an efficient methodology for cataloging tissue-specific transcriptomes in which specific classes of genes or transcripts can be targeted for capture and sequence, thus reducing the significant sequencing depth normally required for accurate annotation. Read more

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For non-model organisms lacking well-defined genomes, de novo assembly is typically required for downstream RNA-Seq analyses, including SNP discovery and identification of genes differentially expressed by phenotypes. Although RNA-Seq has been successfully used to sequence many non-model organisms, the results of de novo assembly from short reads can still be improved by using recent bioinformatic developments. Read more

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Researchers at National Taiwan Ocean University and National Tsing Hua University, Taiwan proposed a workflow which integrated annotations from KEGG biological pathways and Gene Ontology associations for manipulating multiple RNA-seq datasets. The developed system started from mapping short reads onto reference genes, and then performed normalization procedures on read coverage to evaluate and compare expression levels within various gene clusters. Different levels of gene expression were indicated by diverse color shades and graphically shown in designed temporal pathways. Representative GO terms associated with differentially expressed gene cluster were also visually displayed by a GO tag cloud representation. Three different public RNA-Seq datasets were applied to demonstrate that the proposed workflow could provide effective and efficient analysis on differential gene expression for either cross-strain comparison or an identical sample sequenced at different time points. Read more

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A comprehensive understanding of host–pathogen interactions requires a knowledge of the associated gene expression changes in both the pathogen and the host. Traditional, probe-dependent approaches using microarrays or reverse transcription PCR typically require the pathogen and host cells to be physically separated before gene expression analysis. However, the development of the probe-independent RNA sequencing (RNA-seq) approach has begun to revolutionize transcriptomics.

Researchers at the University of Würzburg, Germany have assessed the feasibility of taking transcriptomics one step further by performing ‘dual RNA-Seq’, in which gene expression changes in both the pathogen and the host are analysed simultaneously.

They found that dual RNA-Seq will require high sequencing depth in order to provide accurate representations of the host and pathogen genomes and propose that this is highly likely to be attainable in the future given the potential for near-infinite sequencing power. However, current dual RNA-Seq on the population level appears to be costly but feasible. The latest sequencing platforms can generate an output of up to several hundred gigabases per experimental run, suggesting that the ~200–2,000 million reads required for dual RNA-Seq can be achieved.

• Westermann AJ, Gorski SA, Vogel J. (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10(9), 618-30. [article]

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• SEQanswers – RNA Sequencing

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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 […]