Jan

18

Bioinformatics – Next Gen Sequencing – Virtual Issue

Filed Under Analysis Pipelines, Data Analysis, Expression and Quantification, Other Tools, Splicing and Junction Mapping, Transcriptome Assembly Tools, Unspliced Mapping Tools | 1 Comment

Bioinformatics has published a Next-Gen Sequencing “Virtual Issue” covering all the sequencing tools that appeared in the journal.  We have listed those described as applicable to RNA-Seq.

Statistical Inferences for Isoform Expression in RNA-Seq.
Hui Jiang and Wing Wong
Bioinformatics (2009) 25: 1026–1032 Full Text

A toolkit for analysing large-scale plant small RNA datasets
Simon Moxon et al.
Bioinformatics (2008) 24: 2252-2253 Full Text

TopHat: discovering splice junctions with RNA-Seq
Cole Trapnell et al.
Bioinformatics (2009) 25: 1105–1111 Full Text

Read more

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Sep

21

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

Filed Under Expression and Quantification | Leave a Comment

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

Filed Under Expression and Quantification | Leave a Comment

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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  • RSS 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

  • RSS 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 […]