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Genome-wide analyses and high-throughput screening was long reserved for biomedical applications and genetic model organisms. With the rapid development of massively parallel sequencing nanotechnology (or next-generation sequencing) and simultaneous maturation of bioinformatic tools, this situation has dramatically changed. Many in the eco-evolutionary sciences will be working with large-scale genomic data sets, and a basic understanding of the concepts and underlying methods is necessary to judge the work of others.

Here, the author briefly introduces next-generation sequencing and then focuses on transcriptome shotgun sequencing (RNA-Seq). This article gives a broad overview and provides practical guidance for the many steps involved in a typical RNA-Seq work flow from sampling, to RNA extraction, library preparation and data analysis. He focuses on principles, presents useful tools where appropriate and point out where caution is needed or progress to be expected. This tutorial is mostly targeted at beginners, but also contains potentially useful reflections for the more experienced.

• Wolf JB. (2013) Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol Ecol Resour [Epub ahead of print]. [article]

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The whole-genome sequences of many non-model organisms have recently been determined. Using these genome sequences, next-generation sequencing based experiments such as RNA-Seq and ChIP-seq have been performed and comparisons of the experiments between related species have provided new knowledge about evolution and biological processes. Although these comparisons require transformation of the genome coordinates of the reads between the species, current software tools are not suitable to convert the massive numbers of reads to the corresponding coordinates of other species’ genomes.

Now, researchers at Ochanomizu University and the Tokyo Institute of Technology, Japan have developed a set of programs, called REad COordinate Transformer (RECOT), which is useful to compare RNA-seq, ChIP-seq and CLIP-seq sequences between closely-related species. RECOT can be used to transform the coordinates of short reads obtained from the genome of a query species being studied to that of a comparison target species after aligning the query and target gene/genome sequences. RECOT generates output in SAM format that can be viewed using recent genome browsers capable of displaying next-generation sequencing data. RECOT

They demonstrate the usefulness of RECOT in comparing ChIP-seq results between two closely-related fruit flies. The results indicate position changes of a transcription factor binding site caused sequence polymorphisms at the binding site.

Availability – RECOT is available at: http://sesejun.github.com/recot/

Izawa A, Sese J. (2013) A tool for the coordinate transformation of next-generation sequencing reads for comparative genomics and transcriptomics. Source Code Biol Med 8(1), 6. [Epub ahead of print]. [abstract]

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Up to date research in biology, biotechnology, and medicine requires fast genome and transcriptome analysis technologies for the investigation of cellular state, physiology, and activity. Here, microarray technology and next generation sequencing of transcripts (RNA-Seq) are state of the art. This chapter presents a detailed description of next-generation sequencing (NGS), describes the impact of this technology on transcriptome analysis and explains its possibilities to explore the modern RNA world.

• Mutz KO, Heilkenbrinker A, Lönne M, Walter JG, Stahl F. (2012) Transcriptome analysis using next-generation sequencing. Curr Opin Biotechnol [Epub ahead of print]. [abstract]

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Transcriptome Analysis by High-Throughput Sequencing (RNA-Seq) – Mark Reimers – Virginia Institute for Psychiatric and Behavioral Genetics.

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The transcriptome of the human pathogen Trypanosoma brucei at single-nucleotide resolution.
Kolev NG, Franklin JB, Carmi S, Shi H, Michaeli S, Tschudi C. (2010)
PLoS Pathog. 2010 Sep 9;6(9). pii: e1001090.

Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq.
Bruno VM, Wang Z, Marjani SL, Euskirchen GM, Martin J, Sherlock G, Snyder M. (2010)
Genome Res. 2010 Sep 1. [Epub ahead of print].

RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.
Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, Graham MA, Cannon SB, May GD, Vance CP, Shoemaker RC. (2010)
BMC Plant Biol. 2010 Aug 5;10:160.

Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq.
Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Li W, Huang X, Han B. (2010)
Genome Res. 2010 Sep;20(9):1238-49.

 Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.
Larsen PE, Trivedi G, Sreedasyam A, Lu V, Podila GK, Collart FR. (2010)
PLoS One. 2010 Jul 6;5(7):e9780.

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From - Genomics Technologies: The Power of Genome-Scale Quantitative Data Resolution Profiling Transcriptomes, Plant Physiology 2010

…More recently, direct sequencing of transcripts by high-throughput sequencing technologies (RNA-Seq) has become an additional alternative to microarrays and is superseding SAGE and MPSS (Busch and Lohmann, 2007). Like SAGE and MPPS, RNA-Seq does not depend on genome annotation for prior probe selection and avoids biases introduced during hybridization of microarrays. On the other hand, RNA-Seq poses novel algorithmic and logistic challenges, and current wet-lab RNA-Seq strategies require lengthy library preparation procedures. Therefore, RNA-Seq is the method of choice in projects using nonmodel organisms and for transcript discovery and genome annotation. Because of their robust sample processing and analysis pipelines, often microarrays are still a preferable choice for projects that involve large numbers of samples for profiling transcripts in model organisms with well-annotated genomes. Read more

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The current revolution in sequencing technologies allows us to obtain a much more detailed picture of transcriptomes via RNA-Sequencing. We have developed the first integrative online platform, Oqtans, for quantitatively analyzing RNA-Seq experiments. It is based on the Galaxy-framework and provides tools for read mapping, transcript reconstruction and quantitation. Read more

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In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view. (read more… )

Costa V, Angelini C, De Feis I, Ciccodicola A. (2010) Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010, 853916. [article]

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• SEQanswers – RNA Sequencing

  • DESeq; can I omit timepoints during dispersal estimation? May 24, 2013
    I have a bacterial timecourse with 2 biological replicates per timepoint. There is a fair bit of variance between my replicates. I have spent the... […]
    amcloon
  • HT Seq Count stranded options May 24, 2013
    I am very new to bioinformatics, so I would be really grateful for some help! I have been using *HTSeq Count v0.5.3* and I am bit confused about... […]
    qwrissie
  • Tophat 2.0.8b installation error May 24, 2013
    I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
    canhu
  • reason for low mapping rate?? May 23, 2013
    we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
    miaom
  • cross-species data - questions about normalization May 23, 2013
    Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
    trelek2
  • CuffDiff strange output May 23, 2013
    Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
    Pruexel

• Biostar – RNA-Seq

  • Why am I getting so many unmapped reads in STAR, classified as "too short"?
    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 […]
  • What happened to -k in TopHat for multiple-mapping reads?
    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 […]
  • Purpose of Y-shaped adapters in Illumina Sequencing?
    Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]