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Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills.

Here, researchers from the University of Padova, Italy present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases.

Availability – Graphite Web is freely available at http://graphiteweb.bio.unipd.it/.

Sales G, Calura E, Martini P, Romualdi C. (2013) Graphite Web: web tool for gene set analysis exploiting pathway topology. Nucleic Acids Res [Epub ahead of print]. [article]

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• www rna-seqblog com graphite-web-web-tool-for-gene-set-analysis-exploiting-pathway-topology

Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. A team led by scientists at University of Wisconsin previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs. To enhance the coverage and resolution of the maize gene atlas, they have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson’s correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues.

RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.

• Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, et al. (2013) Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays. PLoS ONE 8(4), e61005. [article]

Incoming search terms:

• maize rna seq gene atlas data
• maize RNA-seq
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Cytokinins are N6-substituted adenine derivatives that play diverse roles in plant growth and development. Researchers at the University of North Carolina sought to define a robust set of genes regulated by cytokinin, as well as to query the response of genes not represented on microarrays.

To this end, they performed a meta-analysis of microarray data from a variety of cytokinin-treated samples and used RNA-Seq to examine cytokinin-regulated gene expression in Arabidopsis thaliana. Microarray meta-analysis using thirteen microarray experiments combined with empirically-defined filtering criteria identified a set of 226 genes differentially regulated by cytokinin, a subset of which have previously been validated by other methods. RNA-Seq validated about 73% of the up-regulated genes identified by this meta-analysis. In silico promoter analysis indicated an over-representation of type-B ARR binding elements, consistent with the role of type-B ARRs as primary mediators of cytokinin-responsive gene expression. RNA-Seq analysis identified 73 cytokinin-regulated genes that were not represented on the ATH1 microarray. Representative genes were verified using qRT-PCR and NanoString analysis (www.nanostring.com).

Analysis of the genes identified reveals a substantial effect of cytokinin on genes encoding proteins involved in secondary metabolism, particularly those acting in flavonoid and phenylpropanoid biosynthesis, as well as in the regulation of redox state of the cell, particularly a set of glutaredoxin genes. Novel splicing events were found in members of some gene families that are known to play a role in cytokinin signaling or metabolism. The genes identified in this analysis represent a robust set of cytokinin-responsive genes that are useful in the analysis of cytokinin function in plants.

• Bhargava A et al. (2013) Identification of Cytokinin Responsive Genes Using Microarray Meta-analysis and RNA-Seq in Arabidopsis thaliana. Plant Physiology [Epub ahead of print]. [article]

Incoming search terms:

• RNA-seq analysis Clustering Genes
• cytokinin mi rna
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Mature mammalian sperm contain a complex population of RNAs some of which might regulate spermatogenesis while others probably play a role in fertilization and early development. Due to this limited knowledge, the biological functions of sperm RNAs remain enigmatic.

Here, researchers at Texas A&M University report the first characterization of the global transcriptome of the sperm of fertile stallions. The findings improved understanding of the biological significance of sperm RNAs which in turn will allow the discovery of sperm-based biomarkers for stallion fertility. The stallion sperm transcriptome was interrogated by analyzing sperm and testes RNA on a 21,000-element equine whole-genome oligoarray and by RNA-Seq. Microarray analysis revealed 6,761 transcripts in the sperm, of which 165 were sperm-enriched, and 155 were differentially expressed between the sperm and testes. Next, 70 million raw reads were generated by RNA-Seq of which 50% could be aligned with the horse reference genome. A total of 19,257 sequence tags were mapped to all horse chromosomes and the mitochondrial genome.

The data were aligned with selected equine gene models to identify additional exons and splice variants. Gene Ontology annotations showed that sperm transcripts were associated with molecular processes (chemoattractant-activated signal transduction, ion transport) and cellular components (membranes and vesicles) related to known sperm functions at fertilization, while some messenger and microRNAs might be critical for early development. The findings suggest that the rich repertoire of coding and non-coding RNAs in stallion sperm is not a random remnant from spermatogenesis in testes but a selectively retained and functionally coherent collection of RNAs.

• Das PJ, McCarthy F, Vishnoi M, Paria N, Gresham C, Li G, Kachroo P, Sudderth AK, Teague S, Love CC, Varner DD, Chowdhary BP, Raudsepp T. (2013) Stallion Sperm Transcriptome Comprises Functionally Coherent Coding and Regulatory RNAs as Revealed by Microarray Analysis and RNA-Seq. PLoS One 8(2), e56535. [article]

Incoming search terms:

• transcriptome 2013
• Ion RNA Seq microarrays
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• metacore lncRNA

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays, however the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.

A team led by researchers at Institute of Applied Genomics Italy collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. They show how Variance-Stabilizing-Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement.

Different coexpression network centralities are investigated; in particular, they show how betweenness centrality is generally a positive marker for essential genes in Arabidopsis thaliana, regardless of the platform originating the data. In the end, the team focused on a specific gene network case, showing that, although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

• Giorgi FM, Del Fabbro C, Licausi F. (2013) Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana. Bioinformatics [Epub ahead of print]. [abstract]

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from genengnews.com – by Shawn C. Baker, Ph.D, CSO BlueSEQ (www.blueseq.com)- Reprinted with permission from Genetic Engineering & Biotechnology News (GEN)

Is it time to switch?

With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications.

Rising maturity in NGS systems and ancillary protocols such as library preparation and data analysis tools have certainly contributed to the increasing popularity among the research community. Whether it’s a need for more accurate data, better resolution, pressure from granting agencies, or just plain fear of being left behind the technology forefront, it’s clear that the demand for revolutionary sequencing technologies that deliver fast, inexpensive, and accurate genomic information has never been greater. Read more

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The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray has been used as a main platform for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are however subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.

• Zhaonan Sun, Thomas Kuczek, Yu Zhu. (2012) Statistical Calibration of qRT-PCR, Microarray and RNA-Seq Gene Expression Data with Measurement Error Models. Cornell Univ Lib arXiv:1212.6690. [article]

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• microarray pcr

A team led by researchers at Dublin City University, Ireland have studied three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.

• Sîrbu A, Kerr G, Crane M, Ruskin HJ (2012) RNA-Seq vs Dual- and Single-Channel Microarray Data: Sensitivity Analysis for Differential Expression and Clustering. PLoS ONE 7(12): e50986. [article]

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RNA-seq and microarray are the two popular methods employed for genome-wide transcriptome profiling. Current comparison studies have shown that transcriptome quantified by these two methods correlated well. However, none of them have addressed if they complement each other, considering the strengths and the limitations inherent with them. The pivotal requirement to address this question is the knowledge of a well known data set. In this regard, HrpX regulome from pathogenic bacteria serves as an ideal choice as the target genes of HrpX transcription factor are well studied due to their central role in pathogenicity. Read more

Incoming search terms:

• tophat fusion post keyerror score
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• rna-seq and microarray complement each other in transcriptome profiling

Macrophages are dynamic cells integrating signals from their microenvironment to develop specific functional responses. Although, microarray-based transcriptional profiling has established transcriptional reprogramming as an important mechanism for signal integration and cell function of macrophages, current knowledge on transcriptional regulation of human macrophages is far from complete.

Researchers at University of Bonn, Germany performed RNA sequencing (RNA-Seq) of human macrophages to discover novel marker genes, an area of great need particularly in human macrophage biology, and also to generate a much more thorough transcriptome of human M1- and M1-like macrophages, we. Using RNA-Seq, they identified important gene clusters so far not appreciated by standard microarray techniques. In addition, they were able to detect differential promoter usage, alternative transcription start sites, and different coding sequences for 57 gene loci in human macrophages. Moreover, this approach led to the identification of novel M1-associated (CD120b, TLR2, SLAMF7) as well as M2-associated (CD1a, CD1b, CD93, CD226) cell surface markers.

Taken together, these data support that high-resolution transcriptome profiling of human macrophages by RNA-Seq leads to a better understanding of macrophage function and will form the basis for a better characterization of macrophages in human health and disease.

• Beyer M, Mallmann MR, Xue J, Staratschek-Jox A, Vorholt D, Krebs W, Sommer D, Sander J, Mertens C, Nino-Castro A, Schmidt SV, Schultze JL. (2012) High-Resolution Transcriptome of Human Macrophages. PLoS One 7(9), e45466. [article]

Incoming search terms:

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Well, we let this poll go on a little bit longer than normal hoping that one of the choices would pull away from the other.  But alas, the split has been nearly equal since the poll has been up.  There have been many predictions on the Death of Microarrays but it seems they may be here to stay.

The future of microarrays?

Here to stay (i.e. Affy and Agilent affirm continued commitment) (50%, 88 Votes)

Its curtains (i.e. Roche shuts down Nimblegen, Combimatrix trading at $0.85) (50%, 87 Votes)

Total Voters: 175

Check out our latest poll in the left-hand sidebar and cast your vote.

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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

Incoming search terms:

• rna-seq translocation

One of the computational challenges in plant systems biology is to accurately infer the transcriptional regulation relationships based on the correlation analyses of gene expression patterns. Despite the several correlation methods that have been applied in biology to analyze microarray data, concerns regarding the compatibility of these methods to the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution property of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and if necessary, introduction of novel methods into biology have been expected.

In this study, researchers at the University of Arizona compared four existing correlation methods used in microarray analysis and one novel method called Gini correlation coefficient, on previously published microarray-based and sequencing-based gene expression data in Arabidopsis and maize. The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. The analyses pinpointed the strengths and weaknesses of each method, and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation and the Tukey’s biweight correlation.

The Gini correlation method, along with the other four evaluated methods in this study, was implemented as an R package named “rsgcc” that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

The rsgcc package is available at: http://cran.r-project.org/web/packages/rsgcc/index.html

• Ma C, Wang X. (2012) Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. Plant Physiol [Epub ahead of print]. [abstract]

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• SEQanswers – RNA Sequencing

  • 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... […]
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    I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
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  • 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... […]
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  • 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... […]
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  • 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... […]
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  • cannot away with cuffdiff,incredible May 23, 2013
    Hi,all I have 4(A,B,C,D) sample in 4 times(increasing time),I got diff result in 3 different cuffdiff 1.cuffdiff 3(A,B,C) individual... […]
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• 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 […]