• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,836 feed subscribers
• 
  

• Featured Events

  
  

• Media Partners

  
  

• 

• Polls

  What is the greatest immediate need facing the RNA Sequencing community? (Choose Only One)

  • Better (more uniform, less bias, simpler, faster, easier, etc) library preparation protocols
  • Skilled bioinformatics specialists
  • A standardized data analysis pipeline
  • Further advancement in sequencing technology capabilities (throughput, depth, accuracy, etc)
  • More sequencing capacity
  • Continued reduction in cost of sequencing reagents/services

  View Results

   Loading ...
• Search for:

• Data Analysis Tools

  Transcriptome Assembly Tools

  Expression & Quantification

  Unpliced Mapping Tools

  Splicing and Junction Mapping

  Analysis Pipelines

  Clouding Platforms

  Databases

  Other Tools

• Tag Cloud

  allele-specific expression alternative splicing bioinformatics breast cancer chip-seq cloud computing cufflinks Data Analysis data analysis pipeline deep sequencing de novo assembly differential expression encode ensembl expression analysis galaxy gene expression high-throughput sequencing illumina ion torrent junction mapping library preparation life technologies lncrna microarray microarrays microrna mirna next-generation sequencing next-gen sequencing nih pacific biosciences poll results RNA-Seq rnaseq rna sequencing roche small rna tophat transcriptome transcriptome analysis transcriptome assembly transcriptomes transcriptome sequencing transcriptomics

  WP Cumulus Flash tag cloud by Roy Tanck requires Flash Player 9 or better.

• Categories

  Select Category Advertisers Analysis Pipelines Annotation Clouding Platforms Controls Data Analysis Data Analysis Data Sets Data Visualization Databases Events Expression and Quantification Grants / Funding Industry News Information Jobs Library Preparation Methods News Other Tools Patents Pathway Analysis Poll Results Presentations Press Release Publications Reader Conributions Request for RNA-Seq Services Review Sequencing Protocols SNP Detection Splicing and Junction Mapping Statistical Analysis Transcriptome Assembly Tools Transcriptome Sequenced Uncategorized Unspliced Mapping Tools Web Tools Workflow
• 
  
  

• Recent Comments

  • Anthony on A Review of Computational Tools in microRNA Discovery
  • Nicolas on Computational approaches to the analysis of RNA-seq data
  • Ernesto on Novel amplification based RNA-seq methodology generates highly reproducible sequencing libraries from as low as 25 picograms of mRNA
  • Arrayers on RNA-Seq Data Analysis Tools
  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute

• Popular Search Terms

  • grape
  • tirthadipa pradhan
  • rna seq analysis
  • next generation sequencing ppt
  • rna-seq analysis
  • rna seq data analysis
  • rna seq vs microarray
  • RSEM
  • wine grapes
  • mangrove

• Recent Search Terms

  • gene set enrichment analysis rna seq
  • visualizing rnaseq data
  • CaptureSeq
  • rna-seq analysis pipeline
  • rna seq vs microarray
  • cufflinks software
  • database of gene fusions
  • yhs-lavasoft
  • illumina onechannelGUI
  • miRFANs

• Archives

  • June 2013
  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • February 2010
  • January 2010
  • January 2009

Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as RNA-Seq significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, the authors aim to summarize several commonly used tools in metazoan miRNA discovery.

Incoming search terms:

• Sequence Comparative Analysis Using Networks source code
• tcga rna-seq depth
• best methods to rna-seq mirna
• mirna for non model genomes rnaseq
• mirnaseq normalization

MicroRNAs (miRNAs) are a class of non-coding RNAs approximately 21 nt in length which play important roles in regulating gene expression in plants. Although many miRNA studies have focused on a few model plants, miRNAs and their target genes remain largely unknown in hot pepper (Capsicum annuum), one of the most important crops cultivated worldwide.

Here, researchers at the Seoul National University, Korea employed high-throughput sequencing technology to identify miRNAs in pepper extensively from 10 different libraries, including leaf, stem, root, flower, and six developmental stage fruits. Based on a bioinformatics pipeline, they successfully identified 29 and 35 families of conserved and novel miRNAs, respectively. Northern blot analysis was used to validate further the expression of representative miRNAs and to analyze their tissue-specific or developmental stage-specific expression patterns. Moreover, they computationally predicted miRNA targets, many of which were experimentally confirmed using 5′ rapid amplification of cDNA ends analysis. One of the validated novel targets of miR-396 was a domain rearranged methyltransferase, the major de novo methylation enzyme, involved in RNA-directed DNA methylation in plants. This work provides the first reliable draft of the pepper miRNA transcriptome. It offers an expanded picture of pepper miRNAs in relation to other plants, providing a basis for understanding the functional roles of miRNAs in pepper.

• Hwang DG, Park JH, Lim JY, Kim D, Choi Y, Kim S, Reeves G, Yeom SI, Lee JS, Park M, Kim S, Choi IY, Choi D, Shin C. (2013) The Hot Pepper (Capsicum annuum) MicroRNA Transcriptome Reveals Novel and Conserved Targets: A Foundation for Understanding MicroRNA Functional Roles in Hot Pepper. PLoS One 8(5)e64238. [article]

Incoming search terms:

• eco PCR check the RNAseq library quantity
• transcriptomic analysis ppt

Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression.

In this study, a team led by researchers at the Centre for Genomic Regulation (CRG), Barcelona, Spain have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. They used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, they delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, they derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, they analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs.

The researchers propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes.

• Llorens F, Hummel M, Pantano L, Pastor X, Vivancos A, Castillo E, Matllin H, Ferrer A, Ingham M, Noguera M, Kofler R, Dohm JC, Pluvinet R, Bayés M, Himmelbauer H, Del Rio JA, Martí E, Sumoy L. (2013) Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor. BMC Genomics 14(1), 371. [Epub ahead of print]. [abstract]

Incoming search terms:

• work of dario on makabuhay plant
• work of dario in makabuhay plant
• sample preparation for rna-seq with microrna
• novel mirna prediction software
• mirna seq和rnaseq
• microarray mirna
• meta analysis comparing expression data rnaseq
• flowchart import wine
• epigeentics market
• dario on makabuhay integrated scientist gutierrez

New sequencing technologies allow unprecedented views into changes occurring in virus-infected cells, including comprehensive and largely unbiased measurements of different types of RNA. In this study, researchers from the University of Washington used RNA-Seq to profile dynamic changes in cellular microRNAs occurring in HIV-infected cells. The sensitivity afforded by sequencing allowed them to detect changes in microRNA expression early in infection, before the onset of viral replication. A phased pattern of expression was evident among these microRNAs, and many that were initially suppressed were later overexpressed at the height of infection, providing unique signatures of infection. By integrating additional mRNA data with the microRNA data, they identified a role for microRNAs in transcriptional regulation during infection and specifically a network of microRNAs involved in the expression of a known HIV cofactor. Finally, as a distinct benefit of sequencing, they identified candidate nonannotated microRNAs, including one whose downregulation may allow HIV-1 replication to proceed fully.

• Chang ST, Thomas MJ, Sova P, Green RR, Palermo RE, Katze MG. (2013) Next-generation sequencing of small RNAs from HIV-infected cells identifies phased microrna expression patterns and candidate novel microRNAs differentially expressed upon infection. MBio 4(1), e00549-12. [article]

Incoming search terms:

• memek
• can rna-seq data be used for mirna analaysia
• HIV next-generation-sequencing
• micro rna genetics
• Next generation sequencing of small RNA ppt
• rnaseq mirna analysis
• seqanswers mirna

MicroRNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5′-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs.

Researchers at Seoul National University validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species.

Availability – miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm .

• Lee J, Kim DI, Park JH, Choi IY, Shin C. (2013) miRAuto: An automated user-friendly MicroRNA prediction tool utilizing plant small RNA sequencing data. Mol Cells 35(4), 342-7. [abstract]

Incoming search terms:

• mirna sequencing principle
• microrna mrna rna-seq
• miRAuto: An automated user-friendly MicroRNA prediction tool utilizing plant small RNA sequencing data
• tool to align ENCODE bigwig to reference features
• small rna sequencing tool
• small Rna sequence database for plants
• numbers of microrna in plants 2013
• academic library workflow sequence
• mirna sequencing data analysis tool
• mirna analysis tools

MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in regulating post transcriptional gene expression. Gall midges encompass a large group of insects that are of economic importance and also possess fascinating biological traits. The gall midge Mayetiola destructor, commonly known as the Hessian fly, is a destructive pest of wheat and model organism for studying gall midge biology and insect – host plant interactions.

In this study, researchers from the Department of Entomology, Kansas State University systematically analyzed miRNAs from the Hessian fly. Deep-sequencing a Hessian fly larval transcriptome led to the identification of 89 miRNA species that are either identical or very similar to known miRNAs from other insects, and 184 novel miRNAs that have not been reported from other species. A genome-wide search through a draft Hessian fly genome sequence identified a total of 611 putative miRNA-encoding genes based on sequence similarity and the existence of a stem-loop structure for miRNA precursors. Analysis of the 611 putative genes revealed a striking feature: the dramatic expansion of several miRNA gene families. The largest family contained 91 genes that encoded 20 different miRNAs. Microarray analyses revealed the expression of miRNA genes was strictly regulated during Hessian fly larval development and abundance of many miRNA genes were affected by host genotypes.

The identification of a large number of miRNAs for the first time from a gall midge provides a foundation for further studies of miRNA functions in gall midge biology and behavior. The dramatic expansion of identical or similar miRNAs provides a unique system to study functional relations among miRNA iso-genes as well as changes in sequence specificity due to small changes in miRNAs and in their mRNA targets. These results may also facilitate the identification of miRNA genes for potential pest control through transgenic approaches.

• Khajuria C, Williams CE, El Bouhssini M, Whitworth RJ, Richards S, Stuart JJ, Chen MS. (2013) Deep sequencing and genome-wide analysis reveals the expansion of MicroRNA genes in the gall midge Mayetiola destructor. BMC Genomics 14:187. [article]

Incoming search terms:

• mirdeep pair-end small RNA SEQ
• populus euphratica pdf 2010-2013
• transcriptome analysis using rna-seq in insect pests
• how to find transgenic gene expression in rnaseq data
• lowess normalization microrna seq
• methanol utilization pathway
• mirna normalization miRNA-seq
• mirna seq quality report
• rna-seq transgenic plants

In silico generated search for microRNAs (miRNAs) have been driven by methods compiling structural features of the miRNA precursor hairpin as well as to some degree combining this with analysis of RNA-seq profiles for which the miRNA typically leave the drosha/dicer fingerprint of 1-2 ~22nt blocks of reads corresponding to the mature and star miRNA.

In complement to the previous methods, researchers at the University of Copenhagen, Denmark present a study where they systematically exploit these pattern of read profiles. They created databases of 2,540 miRNA read profiles using short RNA-seq data from miRBase and 4,795 read profiles from ENCODE (after preprocessing). Of the 4,795 ENCODE profiles, 1,361 are annotated as noncoding RNAs (ncRNAs) and of which 285 are further annotated as miRNAs. Using \prog{deepBlockAlign} (dba), they align ENCODE ncRNA profiles against the miRBase profiles (cleaned for “self-matches”) and are able to separate ENCODE miRNAs from the other ncRNAs by a Matthews correlation coefficient of 0.8 and then obtain the area under the curve of 0.93. Using the derived separation dba score cut-off, they predict 523 novel miRNA candidates. Further analysis reveal that these are located in genomic regions with (UCSC) MAF block fragmentation and poor sequence conservation, which in part might explain why they have been overlooked in previous efforts.

The researchers further analyzed known miRNAs from human and mouse and found two distinct classes containing two block or $>2$ block respectively, where the latter class hold profiles having less well defined arrangement of reads. They further compared the read profiles specific for plant and animals respectively, in terms of both length and distribution of reads within the profiles. They observed that some read profiles were specific for the two kingdoms respectively.

Availability: All data as well as a server to search miRBase profiles by uploading a BED file is available at http://rth.dk/resources/dba/mirna.

• Pundhir S, Gorodkin J. (2013) MicroRNA discovery by similarity search to a database of RNA-seq profiles. Frontiers in Bioinform & Comp Biol [Epub ahead of print]. [abstract]

Incoming search terms:

• pundhir gorodkin frontiers
• unstranded
• www rna-seqblog com microrna-discovery-by-similarity-search-to-a-database-of-rna-seq-profiles
• rna-seq blog encode
• Pundhir S Gorodkin J (2013) MicroRNA discovery by similarity search to a database of RNA-seq profiles Frontiers in Bioinform & Comp Biol [Epub ahead of print] [abstract]
• rna seq blog mirna poll
• rna-seq database bam
• encode rna seq guidelines
• rnaseq encode
• database for rna seq results

MicroRNAs (miRNAs) are a class of non-coding RNAs of ∼22 nucleotides in length, and constitute a novel class of gene regulators by imperfect base-pairing to the 3′UTR of protein encoding messenger RNAs. Growing evidence indicates that miRNAs are implicated in several pathological processes in myocardial disease. The past years, we have witnessed several profiling attempts using high-density oligonucleotide array-based approaches to identify the complete miRNA content (miRNOME) in the healthy and diseased mammalian heart. These efforts have demonstrated that the failing heart displays differential expression of several dozens of miRNAs. While the total number of experimentally validated human miRNAs is roughly two thousand, the number of expressed miRNAs in the human myocardium remains elusive.

With the objective of performing an unbiased assay to identify the miRNOME of the human heart, both under physiological and pathophysiological conditions, a team led by researchers at Maastricht University, The Netherlands used deep sequencing and bioinformatics to annotate and quantify microRNA expression in healthy and diseased human heart (heart failure secondary to hypertrophic or dilated cardiomyopathy). Their results indicate that the human heart expresses >800 miRNAs, the majority of which not being annotated nor described so far and some of which being unique to primate species. Furthermore, >250 miRNAs show differential and etiology-dependent expression in human dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM). The human cardiac miRNOME still possesses a large number of miRNAs that remain virtually unexplored. The current study provides a starting point for a more comprehensive understanding of the role of miRNAs in regulating human heart disease.

• Leptidis S, El Azzouzi H, Lok SI, de Weger R, Olieslagers S, Kisters N, Silva GJ, Heymans S, Cuppen E, Berezikov E, De Windt LJ, da Costa Martins P. (2013) A Deep Sequencing Approach to Uncover the miRNOME in the Human Heart. PLoS One 8(2), e57800. [article]

Incoming search terms:

• info ptt-poznan pl
• deep sequencing approach
• deep sequencing ppt
• mirna identification through rna seq
• deep sequencing rna seq
• review housekeeping genes in arabidopsis filetype;pdf
• rice rna
• rna deep sequencing technique
• RNA seq detection of miRNA
• RNA seq vs mirnome Illumina

MicroRNAs (miRNAs) can group together along the human genome to form stable secondary structures made of several hairpins hosting miRNAs in their stems. The few known examples of such structures are all involved in cancer development. A large scale computational analysis of human chromosomes crossing sequence analysis and deep sequencing data revealed the presence of >400 structural clusters of miRNAs in the human genome. An a posteriori analysis validates predictions as bona fide miRNAs. A functional analysis of structural clusters position along the chromosomes co-localizes them with genes involved in several key cellular processes like immune systems, sensory systems, signal transduction and development. Immune systems diseases, infectious diseases and neurodegenerative diseases are characterized by genes that are especially well organized around structural clusters of miRNAs. Target genes functional analysis strongly supports a regulatory role of most predicted miRNAs and, notably, a strong involvement of predicted miRNAs in the regulation of cancer pathways. This analysis provides new fundamental insights on the genomic organization of miRNAs in human chromosomes.

Availability: The program, called MIReStruC (standing for ‘miRNA Structural Cluster’), has been implemented in bash, C, Awk and Python. It is available at the address http://www.ihes.fr/∼carbone/data9/.

• Mathelier A, Carbone A. (2013) Large scale chromosomal mapping of human microRNA structural clusters. Nucleic Acids Res [Epub ahead of print]. [article]

Incoming search terms:

• mrna and mirna integration software rna-seq
• Expressed sequence tag
• microrna cufflink
• serum microrna and deep sequencing
• is mirdeep suitable for pairend seq
• rna seq mir bowtie question snorna
• microrna from poly a transcriptome analysis
• mouse mirna rnaseq pipeline
• microrna bolg
• mirna blog

Degradome sequencing for identification of miRNA targets in plants

MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation.

In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as growth, development and responses to biotic and abiotic stresses. Hundreds of miRNAs have been identified in higher plants by direct cloning or more recently by next-gen sequencing. To determine the function of these miRNAs we must first identify their targets.

Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near-perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing.

Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has-been successfully established to screen for miRNA targets in plants. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs have been verified indicating that it is an efficient strategy to identify smRNA targets on a large scale in plants.

Degradome sequencing reveals miRNA targets by globally identifying the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs. Sequencing reads are mapped to mRNAs and the 5′ terminal nucleotide of miRNA-cleaved mRNA fragments corresponds to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript. Confirmed miRNA targets are presented in the form of target plots (t-plots).

Read more

Incoming search terms:

• degradome
• degradome sequencing ppt
• degradome seq
• degradome sequencing review
• degradome software
• degredome seq
• high throughput sequencing degradome
• High-throughput degradomesequencing can be used to gain insights into microR
• illumina degradome sequencing
• pepline of miRNA degradome sequencing

Biogenesis and molecular function are two key subjects in the field of microRNA (miRNA) research. Deep sequencing has become the principal technique in cataloging of miRNA repertoire and generating expression profiles in an unbiased manner.

A team led by researchers at Ewha Womans University, Korea have updated miRGator to version v3.0. miRGator compiles the deep sequencing miRNA data available in public and the team has implemented several novel tools to facilitate exploration of massive data. The miR-seq browser supports users to examine short read alignment with the secondary structure and read count information available in concurrent windows. Features such as sequence editing, sorting, ordering, import and export of user data would be of great utility for studying iso-miRs, miRNA editing and modifications. miRNA-target relation is essential for understanding miRNA function. Coexpression analysis of miRNA and target mRNAs, based on miRNA-Seq and RNA-Seq data from the same sample, is visualized in the heat-map and network views where users can investigate the inverse correlation of gene expression and target relations, compiled from various databases of predicted and validated targets. By keeping datasets and analytic tools up-to-date, miRGator should continue to serve as an integrated resource for biogenesis and functional investigation of miRNAs.

Availability – miRGator v3.0 update is available at: http://mirgator.kobic.re.kr

Cho S, Jang I, Jun Y, Yoon S, Ko M, Kwon Y, Choi I, Jang H, Ryu D, Lee B, Kim VN, Kim W, Lee S. (2012) miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting. Nucleic Acids Res [Epub ahead of print]. [article]

Incoming search terms:

• RNA-Seq and microRNA expression profiling reveal networks of RNA interactions in regenerating dorsal root ganglion neurons
• microRNA blog
• Error running long_spanning_reads:Loading fusions
• mirgator trouble
• splice aware aligner for prokaryotic

from the Houston Chronicle

Hangzhou, China (PRWEB) May 29, 2012 – In a new study published online in Nature Communications, researchers from Sichuan Agricultural University and LC Sciences report the miRNAome in porcine adipose and muscle tissues. The report provides a valuable epigenomic source for obesity prediction and prevention and furthers the development of pig as a model organism for human obesity research.

Scientists now know that the genetic code alone isn’t responsible for adult phenotype or even the offspring of these adults. Epigenetics refers to changes in gene expression affecting phenotype that don’t involve changes to the DNA nucleotide sequence itself, and yet are heritable. DNA methylation, histone modification and microRNA (miRNA) expression are examples of epigenetic mechanisms that have recently been identified as important regulators of gene expression in many biological systems.

Obesity is a huge problem worldwide. Recently, the World Health Organization reported that obesity levels doubled in every region of the world between 1980 and 2008, spurring rates of non-communicable diseases such as diabetes and cancer that now account for almost two out of three deaths globally. It has become evident that epigenetic factors, such as DNA methylation and miRNA expression, have essential roles in obesity development. (read more…)

• Li, M. et al. (2012)An atlas of DNA methylomes in porcine adipose and muscle tissues. Nat Commun[Epub ahead of print]. [abstract]

 

Incoming search terms:

• serum microRNA
• mrna seq
• rna seq flow chart
• can u use RNA sequencing for epigeentics
• rna-seq obesity
• RNA-seq for epigenetics
• rna-seq epigenetics
• rna seq epigentic
• geo rna seq
• epigenetics rna-seq

Researchers in the Department of Bioengineering, UCSD evaluated seven commonly used normalization methods:

Global normalization
Lowess normalization
Trimmed Mean Method (TMM)
Quantile normalization
Scaling normalization
Variance stabilization
Invariant method

They assessed these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic.

The results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst.

The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.

• Garmire LX, Subramaniam S. (2012)v Evaluation of normalization methods in mammalian microRNA-Seq data. RNA [Epub ahead of print]. [abstract]

On the subject of microRNA-Seq… check out the latest poll in the left sidebar and cast your vote.

Incoming search terms:

• tmm normalization
• microrna
• normalization microrna rna-seq
• rnaseq quantile normalizatino
• FastQC essay
• invariant microrna should show a Ct of
• loess normalization rna-seq data
• print -tip loess nomalization
• r loess normalization rna-seq

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• SEQanswers – RNA Sequencing

  • RNAseq (SOLiD) from 18 - 200 nt June 18, 2013
    We are interested in small non-coding RNAs. Whomever you ask about the size range of small RNAs, you get a different answer. ;) Lets assume, small... […]
    GenomicIBK
  • Unmapped ratio very high on mouse genome June 17, 2013
    Hi, My problem regards RNA-Seq data. I've downloaded public data (SAGE libs w/ 6 different samples from mouse liver ) to analyse using ArrayStudio.... […]
    le.nono
  • RNASeq: Read length different from expected June 17, 2013
    Hello all, I have received paired-end reads for 40 samples. The reads are supposed to be 100bp per end. Instead, 20 of my samples are 101bp per... […]
    gogodidi
  • How to install xgawk June 16, 2013
    Hi, This is Shrujan, i have a problem while running RNA Sequencing QC. It shows an error that xgawk is not found. So please help me installing... […]
    shrujan
  • RNA Sequencing QC Error while using with Sequence_QC.sh file June 15, 2013
    Hi, This is Shrujan kumar Madadha, I had an error while running QC for Drosophila Yukuba fastq RNA file using Sequence_QC.sh file of FASTX... […]
    shrujan
  • Cuffmerge related query June 12, 2013
    I have a query regarding what samples should be merged using cuffmerge, when you have multiple phenotypes (each with replicates). Lets say my mouse... […]
    ParthavJailwala

• Biostar – RNA-Seq

  • edgeR: very low p-value and very high variance within the group of replicates. What's my problem??
    I'm using edgeR in order to perform differential expression analysis from RNA-seq experiment. I have 6 samples of tumor cell, same tumor and same treatment: 3 patient with good prognosis and 3 patient with bad prognosis. I want to compare the gene expression among the two groups. I ran the edgeR pakage like follow: x […]
  • 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 […]