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AltAnalyze is a freely available, open-source and cross-platform program that allows you to take RNASeq or relatively raw microarray data (CEL files or normalized), identify predicted alternative splicing or alternative promoter changes and view how these changes may affect protein sequence, domain composition, and microRNA targeting. AltAnalyze is compatible with any RNASeq data that can be aligned to junctions, several Affymetrix splicing sensitive array types (Gene 1.0, Exon 1.0, junction) as well as many conventional array-types (e.g., Affymetrix, Illumina, Agilent). This software requires no advanced knowledge of bioinformatics programs or scripting. All you will need are your junction read or microarray files along with some simple descriptions of the conditions that you’re analyzing. RNASeq support is introduced in AltAnalyze 2.0. Read more

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A list of the RNA-Seq data analysis software packages available from commercial providers.  Again, if I’ve missed something, please send me a note and I will be happy to add to this list.

Avidis NGS – http://www.avadis-ngs.com/applications/rna_seq – Find fused genes via spliced and paired reads spanning the fused genes. Analyze coverage patterns to detect novel genes and exons not present in NCBI, UCSC, and Ensembl annotations. Seamlessly create and manipulate genomic region lists. Filter region lists based on different attributes. Determine gene and deconvoluted transcript expression profiles; identify alternative splicing patterns. Identify differential gene profiles via statitistical tests, cluster genes with similar profiles. Carry out GO analysis to identify significant GO terms.

CLC Genomics Workbench – http://www.clcbio.com/index.php?id=1240 – Our RNA-seq analysis now supports the use of paired-end data for RNA-seq. A combination of single reads and paired reads can also be used, and expression values can now be stratified into transcript level expression values, both for single and paired reads, allowing users to compare two different samples across transcripts. Another important new feature is our batching functionality of all our high-throughput sequencing tools, enabling researchers to perform the same analysis on several elements in one batch, which is an easy way to analyze multiple datasets in one go and thereby save time for setting up and running the same type of analysis multiple times.

DNAnexus - https://dnanexus.com/features/researcher/analyses – tag-based applications. RNA-seq is now a cost-effective way to measure gene expression, discover alternative splicing, and identify previously unassayed transcriptional activity. ChIP-seq provides a general way to study interactions between DNA and other molecules. With DNAnexus, these and other tag-based applications can be analyzed through our cloud-based infrastructure with the click of a button. Visualize the results in the DNAnexus browser, or export the results in tabular format and integrate your data across multiple experiment types, for example combining RNA-seq expression data with ChIP-seq binding affinity on the same genes.

DNASTAR QSeq – http://www.dnastar.com/t-products-qseq.aspx – QSeq is DNASTAR’s Next-Gen application for RNA-Seq, ChIP-Seq, and miRNA alignment and analysis. QSeq is fully integrated with ArrayStar, enabling you to take advantage of its powerful visualization and analytical tools, including using Gene Ontology (GO) annotations for ontology comparisons and gene characterization. Using QSeq, researchers can select gene sets and export associated reads through the rest of the DNASTAR software pipeline for sequence assembly, alignment, and detailed analysis.

GeneSifter – http://www.geospiza.com/Products/default.shtml – Analysis Kits offer a solution for scientists faced with large volumes of unprocessed data from AB SOLiD or Illumina Genome Analyzer DNA sequencing. Through Geospiza’s rapid analysis service, you can complete Next Generation Data Analysis as a service per file or per run. GeneSifter Analysis Edition now supports Whole Transcriptome Analysis with a more detailed view of each gene, including predicted splicing events and exon usage – increasing your access to the depth of next generation sequencing. This new analysis capability also includes an integrated gene viewer – to see your data as it relates to the genome and then tie it to publicly available information.

Geomatix Genome Analyzer – http://www.genomatix.de/en/produkte/genomatix-genome-analyzer.html – The Genomatix Genome Analyzer is our integrated solution for comprehensive second-level analysis of Next Generation Sequencing (NGS) data from ChIP-Seq, RNA-Seq or genotyping experiments. Each analyzer is brimming with state-of-the-art technology that sheds light on biological context – essential to help you understand the big picture.

GenomeQuest RNA Seq – http://www.genomequest.com/science/workflows/rna-seq/ – RNA-Seq stands to replace existing transcript profiling technologies as it measures all RNA in a sample, not just the RNA that can be probed for using traditional chips. GenomeQuest provides a powerful workflow that integrates best-of-breed open source technologies in a commercially supported environment to measure gene expression and to discover novel splice variants.

Ingenuity IPA 9.0 – http://www.ingenuity.com/products/pathways_analysis.html – IPA will support RNA-Seq processed datasets containing Ensembl, RefSeq or UCSC IDs. Researchers will now be able to analyze and interpret their RNA-Seq data. IPA helps you understand biology at multiple levels by integrating data from a variety of experimental platforms and providing insight into the molecular and chemical interactions, cellular phenotypes, and disease processes of your system. Even if you don’t have experimental data, you can use IPA to intelligently search the Ingenuity^® Knowledge Base for information on genes, proteins, chemicals, drugs, and molecular relationships to build biological models or get up to speed in a relevant area of research.

Partek Genomics Suite (Partek GS) – http://www.partek.com/ngs#rnaseq Using a whole-transcriptome sequencing approach, sequence data are analyzed for differential expression and alternative splicing based on known mRNA annotation. Sequencing reads that are not mapped to any known mRNA annotation are used to uncover the novel transcriptional regions. Partek GS will identify and quantify sequence variants (coding SNPs) within RNA-seq samples. With a collection of SNPs identified, Partek GS will then find allele specific expression that drive phenotypic change within the transcriptome. Both paired end and junction reads are supported.

RNA Baser Sequence Assembler – http://www.rnabaser.com/ – RNA Baser Sequence Assembler represents an extension of DNA Baser Sequence Assembler, specialized in processing rRNA sequences. It is optimized for early integration of contextual metadata. This way, the metadata will stay attached to the primary sequence information throughout the complete workflow of sequence analysis, until final submission. In addition, RNA Baser Sequence Assembler is optimized for data exchange with standard non-commercial software used for rRNA sequence analysis and submission.

SeqSolve – http://www.integromics.com/ngs – for Next Generation Sequencing: a unique approach for the downstream analysis of RNA-seq data. – characterization of the reads, distributions into annotated genic regions, coverage profile within genes, read density over genomic sites, statistical support for differential gene expression, antisense transcription analysis, quality controls of the samples with library complexity, strand specificity, coverage asymmetry… and much more! Get the best from your RNA-seq data with SeqSolve.

Softgenetics NextGENe – http://www.softgenetics.com/NextGENe_11.html – NextGENe Software includes analysis tools designed for RNA-Seq analysis of data DNA sequencing data from Roche/454 GS FLX, FLX Titanium & Junior, Applied Biosystems’ SOLiD System and Illumina GA & Hi-Seq systems. Transcriptome sequencing data is aligned to the reference sequence to allow for detection of alternative splice sites, gene fusions, exon skipping and intron retention. NextGENe detects which exons are present in the sample data and chooses the appropriate reference transcripts. Accurate expression levels can be evaluated and mutations are reported. Alignment of RNA-Seq data with NextGENe uses a specialized algorithm to accurately align reads along exon boundaries.

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We have been trying to keep track of all the data analysis tools available for handling RNA-Seq data that are freely available on the web.  We just categorized these tools in the left sidebar at the suggestion of one of our readers.  Also, you can reach the tools from the links below.

Mapping Tools

Assembly Tools

Expression and Quantification Tools

Splicing and Junction Mapping Tools

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Please feel free to send us a note if we have missed something.

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Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, more than 460 packages, and an active user community. Read more

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An international team led by investigators at Umeå University in Sweden describes a “novel pathway of small guide RNA maturation and the first example of a host factor — RNase III — required for bacterial RNA-mediated immunity against invaders.” Using RNA-seq on Streptococcus pyrogenes, the team identified a “trans-encoded small RNA with 24-nucleotide complementarity to the repeat regions of crRNA precursor transcripts” — a tracrRNA — which the team says guides the maturation of crRNAs via RNase III and the CRISPR-associated Csn1 protein.

CRISPR/Cas systems constitute a widespread class of immunity systems that protect bacteria and archaea against phages and plasmids, and commonly use repeat/spacer-derived short crRNAs to silence foreign nucleic acids in a sequence-specific manner. Although the maturation of crRNAs represents a key event in CRISPR activation, the responsible endoribonucleases (CasE, Cas6, Csy4) are missing in many CRISPR/Cas subtypes.

Here, differential RNA sequencing of the human pathogen Streptococcus pyogenes uncovered tracrRNA, a trans-encoded small RNA with 24-nucleotide complementarity to the repeat regions of crRNA precursor transcripts. The authors show that tracrRNA directs the maturation of crRNAs by the activities of the widely conserved endogenous RNase III and the CRISPR-associated Csn1 protein; all these components are essential to protect S. pyogenes against prophage-derived DNA.

This study reveals a novel pathway of small guide RNA maturation and the first example of a host factor (RNase III) required for bacterial RNA-mediated immunity against invaders.

• Deltcheva E, Chylinski K, Sharma CM, Gonzales K, Chao Y, Pirzada ZA, Eckert MR, Vogel J, Charpentier E. (2011) CRISPR RNA maturation by trans-encoded small RNA and host factor RNase III. Nature 471(7340), 602-07. [abstract]

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New Software
Estimation of alternative splicing isoform frequencies from RNA-Seq data

This algorithm, referred to as IsoEM, is based on disambiguating of information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/

New Species
BMC Genomics reports the transcriptome sequencing of two new species: Alfalfa and Guppy

Alfalfa, (Medicago sativa [L.] sativa), a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy.  Using the Illumina GA-II platform, a total of 198,861,304 expression sequence tags (ESTs, 76 bp in length) were generated from cDNA libraries derived from elongating stem (ES) and post-elongation stem (PES) internodes of 708 and 773. [abstract]

The guppy (Poecilia reticulata) transcriptome, assembled do novo using 454 sequence reads, is presented and the authors evaluate potential uses of this transcriptome, including detection of sex-specific transcripts and deployment as a reference for gene expression analysis in guppies and a related species. Guppies have been model organisms in ecology, evolutionary biology, and animal behaviour for over 100 years. An annotated transcriptome and other genomic tools will facilitate understanding the genetic and molecular bases of adaptation and variation in a vertebrate species with a uniquely well known natural history. [abstract]

New Deals
Integromics and Ingenuity expand their co-operation with the integration of a fourth Integromics product to Ingenuity¹s IPA

The SeqSolve analysis software, exclusively designed for Next Generation Sequencing (NGS), performs tertiary level analysis of RNA-seq data at the gene and transcript level for biologically relevant results. Integromics has already integrated three solutions with Ingenuity’s IPA: Integromics Biomarker Discovery(R) for microarray data analysis, RealTime StatMiner(R) for the analysis of qPCR data as well as OmicsHub(R) Proteomics for the analysis and storage of proteomics data.

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Next-generation sequencing is providing researchers with a relatively fast and affordable option for developing genomic resources for organisms that are not among the traditional genetic models. Here we present a de novo assembly of the guppy (Poecilia reticulata) transcriptome using 454 sequence reads, and we evaluate potential uses of this transcriptome, including detection of sex-specific transcripts and deployment as a reference for gene expression analysis in guppies and a related species. Guppies have been model organisms in ecology, evolutionary biology, and animal behaviour for over 100 years. An annotated transcriptome and other genomic tools will facilitate understanding the genetic and molecular bases of adaptation and variation in a vertebrate species with a uniquely well known natural history. Read more

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RNA-seq is a method for studying the transcriptome of cells or tissues by massively parallel sequencing of tens of millions of short DNA fragments. However, the broad dynamic range of gene expression levels, which span more than five orders of magnitude, necessitates considerable over-sequencing to characterize low-abundance RNAs at sufficient depth. Here, the authors describe a method that enables efficient sequencing of low-abundance RNAs by normalizing or reducing the range spanned by the most abundant RNA species to the least abundant RNA species. This normalization is achieved using an approach that was developed for generating expressed sequence tag (EST) libraries that uses the crab duplex-specific nuclease and exploits the kinetics of DNA annealing. That is, double-stranded cDNA is denatured, then allowed to partially re-anneal, and the most abundant species, which re-anneal most rapidly, are digested with crab duplex-specific nuclease. This procedure substantially decreases the proportion of sequence reads from highly expressed RNAs, facilitating assessment of the full spectrum of the sequence and structure of transcriptomes.

Christodoulou DC, Gorham JM, Herman DS, Seidman JG. (2011) Construction of Normalized RNA-seq Libraries for Next-Generation Sequencing Using the Crab Duplex-Specific Nuclease. Curr Protoc Mol Biol. Chapter 4:Unit4.12. [abstract]

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Alfalfa, [Medicago sativa (L.) sativa], a widely-grown perennial forage has potential for development as a cellulosic ethanol feedstock. However, the genomics of alfalfa, a non-model species, is still in its infancy. The recent advent of RNA-Seq, a massively parallel sequencing method for transcriptome analysis, provides an opportunity to expand the identification of alfalfa genes and polymorphisms, and conduct in-depth transcript profiling. Read more

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from MedWire News – Human papillomavirus does not actively promote cutaneous SCC growth
By Helen Albert

J Invest Dermatol 2011: Advance online publication

Although β-human papillomavirus has been found in some cutaneous squamous cell carcinomas (SCCs), there is no evidence that it acts to promote carcinogenesis in such patients, show study findings.

Around 12% of all human cancers are known to be caused by viruses, but the presence of viral DNA in a cancer biopsy does not necessarily indicate causality.

β-human papillomavirus DNA has been found in some cutaneous SCCs. To assess whether the virus actively promotes carcinogenesis in these patients, Joseph DeRisi (University of California, San Francisco, USA) and colleagues carried out ultra-high-throughput sequencing of the cancer transcriptome of 67 cutaneous SCC samples to assess whether papillomavirus transcripts were also present.

Of the samples examined, 30% were positive for β-human papillomavirus DNA. People with β-human papillomavirus-positive tumors tended to be older and were more likely to be immunosuppressed than those with β-human papillomavirus-negative tumors.

However, no difference in viral load was observed between tumor and normal tissue in these individuals. In addition, transcriptome sequencing was unable to identify papillomavirus expression in any of the skin tumors, suggesting that the virus does not play an active carcinogenic role.

In contrast, mRNA transcripts of human papillomavirus 16 and 17 were identified in a significant number of primary cervical and periungual cancers, which were tested for comparison purposes.

“These data demonstrate that papillomavirus mRNA expression is not a factor in the maintenance of cutaneous SCCs,” say DeRisi and team.

“The most straightforward interpretation of our data is that the sporadic and low-level presence of β-human papillomavirus genomic DNA in these tumors, unaccompanied by evidence of active viral gene expression, most likely represents colonization rather than an etiologic association,” conclude the authors in the Journal of Investigative Dermatology.

Arron ST, Ruby JG, Dybbro E, Ganem D, Derisi JL. (2011) Transcriptome Sequencing Demonstrates that Human Papillomavirus Is Not Active in Cutaneous Squamous Cell Carcinoma. J Invest Dermatol [Epub ahead of print]. [abstract]

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SAMMate, a Graphical User Interface (GUI) RNA-seq analysis pipeline, allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate automates some of more standard procedures in RNA-seq analysis. Read more

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ExpEdit is a web application for assessing RNA editing in human at known or user specified sites supported by transcript data obtained by RNA-Seq experiments. Mapping data (in SAM/BAM format) or directly sequence reads (in FASTQ/SRA format) can be provided as input to carry out a comparative analysis against a large collection of known editing sites collected in DARNED database as well as other user-provided potentially edited positions. Results are shown as dynamic tables containing UCSC links for a quick examination of the genomic context. Read more

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Stuart Tugendreich Ph.D.
Director, Product Management New Solutions, Ingenuity Systems

At last fall’s Next Generation Sequencing Congress, Keith Batchelder, MD and CEO of Genomic Healthcare Strategies, gave a talk and argued a key point: that the deluge of raw data from NGS technologies has no value unless it is analyzed, annotated, and associated with other data. In other words – the interpretation of the data has more value than the data itself.  A recent special report by Bio-IT World discusses this same problem:

“There is a growing gap between the generation of massively parallel sequencing output and the ability to process and analyze the resulting data,” says Canadian cancer research John McPherson, feeling the pain of NGS neophytes left to negotiate “a bewildering maze of base calling, alignment, assembly, and analysis tools with often incomplete documentation and no idea how to compare and validate their outputs.”

Researchers and laboratories willingly spend thousands of dollars on instrumentation to produce data, but that investment is lost or misguided if the analysis is lacking or haphazard.  But what makes for good analysis of NGS data?  Specifically for RNA-Seq data, there aren’t well-established methods of data quality control, processing, or analysis approaches.  So, how can you overcome this challenge for replicable and reliable results that maximize the value of your experimental investment? And how can you sort through huge amounts of data to quickly make sense of your data?

In the struggle to determine these standards around RNA-Seq data analysis, biological interpretation is emerging as the key way to quickly narrow in on relevant information and examine data within a consistent set of biological references.  Examining the results from an RNA-Seq dataset in the context of established and known molecular relationships, biological processes, and relationships to disease,  provides a faster and more reliable, replicable way to identify key insights from complex data.

Also crucial is the tight integration of a data analysis tool with the relevant content sources, to ensure data integrity and the seamless, accurate transition from data processing to biological interpretation. Tools that will emerge as likely standard setters for RNA-Seq analysis are those like Ingenuity’s IPA, which  1) can accept the upload of relevant identifiers like RNA-Seq, Ensembl, and UCSC  from upstream partners like CLC bio, Geospiza, GenomeQuest, and Partek, and 2) already has a comprehensive biological knowledge base that can put this data in the context known biological relationships and processes.

This sentiment was echoed by Batchelder, who touched on this concept when he suggested that the ability to add value to data, and the ability to aggregate, mine, and curate data in a way that connects it to other information was most crucial for success in the NGS field.

Learn more about how to maximize your investment in RNA-Seq data at the Next-Gen Sequencing Congress in Boston in April.  Dr. Sandeep Sanga, Bioinformatics Product Development Scientist at Ingenuity Systems, will be speaking on Wednesday, April 27, 11:45 am:  Insights into Prostate Cancer Mechanisms via Integrated In Silico RNA-Seq Analysis of NGS Patient Data. You can preview the poster here.

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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 […]