Sep

21

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Seq

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This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. The authors found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.

  • Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. (2012) Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics 13(1), 484. [article]

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Sep

21

The bench scientist’s guide to statistical analysis of RNA-Seq data

Filed Under Analysis Pipelines, Data Analysis, Statistical Analysis | Leave a Comment

Bench ScientistThe authors provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.

When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.

Because of the high variability between methods for determining differential expression of RNA-Seq data, the authors suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. They also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.

  • Yendrek CR, Ainsworth EA, Thimmapuram J. (2012) The bench scientist’s guide to statistical analysis of RNA-Seq data. BMC Res Notes 5(1), 506. [article]

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Jan

26

Another review of methods for detecting differentially expressed genes from RNA-Seq data

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Here, researchers from Iowa State University compare four recently proposed statistical methods, edgeR, DESeq, baySeq, and a method with a two-stage Poisson model (TSPM), through a variety of simulations that were based on different distribution models or real data. They compared the ability of these methods to detect DE genes in terms of the significance ranking of genes and false discovery rate control. All methods compared are implemented in freely available software.

  • Kvam VM, Liu P, Si Y. (2012) A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot [Epub ahead of print]. [article]

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Jun

25

DESeq – Digital gene expresion analysis based on the negative binomial distribution

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Motivation: High throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq), cell counting. Statistical inference of differential signal in these data needs to take into account their natural variability throughout the dynamic range. When the number of replicates is small, error modeling is needed to achieve statistical power. Read more

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  • RSS SEQanswers – RNA Sequencing

    • How to increase rowsize in heatmap? May 16, 2013
      Hi, I am a complete newbie to all things cummeRbund and am currently fighting with generating readable heatmaps. When I use ... […]
      Mags
    • novoalign mapping May 15, 2013
      Hi, I want to use novoalign to map reads - allowing up to 15 mismatches for 100 bp paired-end reads I am new to novoalign(went through the... […]
      abh
    • Design of expt across multiple lanes May 15, 2013
      Hi, I am performing an RNA-seq experiment to look at differential expression. The design is as follows: 2 populations x 3 biological... […]
      jbono
    • RNA kinds expected in RNA-seq results May 15, 2013
      Hi, We use RNA isolation and library preparation protocols which capture polyadenylated RNA. My question is what kinds of RNA can we expect to... […]
      Kocur
    • Discrepancy between genotype and expressed alleles May 15, 2013
      Hi all, I am working on the analysis of allele-specific expression using both genotype information and RNA-seq data from the same individuals. I... […]
      RedMary
    • Does Cufflinks Give Me Trascriptomes? May 14, 2013
      Hi Everyone, I'm a beginner in this area, please forget any silly question. My situation is that I have a raw scaffold whole genome... […]
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  • RSS Biostar – RNA-Seq

    • 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 tag, which is useful for subsequent analyses, such as annotation. However, does this information influence the "mappability" of reads, or is this unaffected? My guess is that the information will be considered for mapping […]
    • 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 […]
    • Cell Type composition in a tissue based on gene marker expression
      I am not sure if the following would even make sense.... Tissues are composed of composite cell types, and often there are studies such as microarray/NGS where we perform a collective sampling of cells from these tissues. Information about the composition (say percentage of cell type) is not taken into consideration. In some case (such as brain/cancer), ther […]
    • Which SNP caller / method to use after aligning RNA-seq with TopHat
      Which SNP caller / method can / should I use after aligning RNA-seq data with TopHat? For genomic data I use GATK, but supposedly it is not just as easy as running GATK on the TopHat RNA-seq data. The team from Broad has no information / documentation on how to use GATK for RNA-seq data. I don't have any variants yet from DNA re-sequencing. […]