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Here, I want to do a literature list classification and disscuss the results of the evaluation.
1. general review for RNA-sequencing
Garber, M., et al. (2011). "Computational methods for transcriptome annotation and quantification using RNA-seq." Nat Methods 8(6): 469-477.
Alamancos, G. P., et al. (2013). "Methods to study splicing from high-throughput RNA Sequencing data." arXiv preprint arXiv:1304.5952.
Smith, D. R. (2013). "RNA-Seq data: a goldmine for organelle research." Brief Funct Genomics.
Rung, J. and A. Brazma (2013). "Reuse of public genome-wide gene expression data." Nat Rev Genet 14(2): 89-99.
Chen, G., et al. (2011). "Overview of available methods for diverse RNA-Seq data analyses." Sci China Life Sci 54(12): 1121-1128.
Oshlack, A., et al. (2010). "From RNA-seq reads to differential expression results." Genome Biol 11(12): 220.
2. RNAseq assembly
RNAseq error correction:
Le, H. S., et al. (2013). "Probabilistic error correction for RNA sequencing." Nucleic Acids Res.
Assembly evaluation:
Clarke, K., et al. (2013). "Comparative analysis of de novo transcriptome assembly." Sci China Life Sci 56(2): 156-162.
Ren, X., et al. (2012). "Evaluating de Bruijn graph assemblers on 454 transcriptomic data." PLoS One 7(12): e51188.
Mundry, M., et al. (2012). "Evaluating characteristics of de novo assembly software on 454 transcriptome data: a simulation approach." PLoS One 7(2): e31410.
4. RNAseq mapping evaluation
Grant, G. R., et al. (2011). "Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)." Bioinformatics 27(18): 2518-2528.
Lindner, R. and C. C. Friedel (2012). "A Comprehensive Evaluation of Alignment Algorithms in the Context of RNA-Seq." PLoS One 7(12): e52403.
5. Differential expression
For the normalization, edgeR's TMM method and DESeq's method are recommended.
Cuffdiff was not involved in the following evaluations.
Soneson, C. and M. Delorenzi (2013). "A comparison of methods for differential expression analysis of RNA-seq data." BMC Bioinformatics 14(1): 91.
Rapaport, F., et al. (2013). "Comprehensive evaluation of differential expression analysis methods for RNA-seq data." arXiv preprint arXiv:1301.5277.
Dillies, M. A., et al. (2012). "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis." Brief Bioinform.
Kvam, V. M., et al. (2012). "A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data." Am J Bot 99(2): 248-256.
Bullard, J. H., et al. (2010). "Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments." BMC Bioinformatics 11: 94.
6. Alternative splicing
So many softwares for this topic, but no evaluation has been published
7. System biology
This section includ the RNAseq specific analysis after the differentially expressed genes have been detected.
1. general review for RNA-sequencing
Garber, M., et al. (2011). "Computational methods for transcriptome annotation and quantification using RNA-seq." Nat Methods 8(6): 469-477.
Alamancos, G. P., et al. (2013). "Methods to study splicing from high-throughput RNA Sequencing data." arXiv preprint arXiv:1304.5952.
Smith, D. R. (2013). "RNA-Seq data: a goldmine for organelle research." Brief Funct Genomics.
Rung, J. and A. Brazma (2013). "Reuse of public genome-wide gene expression data." Nat Rev Genet 14(2): 89-99.
Chen, G., et al. (2011). "Overview of available methods for diverse RNA-Seq data analyses." Sci China Life Sci 54(12): 1121-1128.
Oshlack, A., et al. (2010). "From RNA-seq reads to differential expression results." Genome Biol 11(12): 220.
2. RNAseq assembly
RNAseq error correction:
Le, H. S., et al. (2013). "Probabilistic error correction for RNA sequencing." Nucleic Acids Res.
Assembly evaluation:
Clarke, K., et al. (2013). "Comparative analysis of de novo transcriptome assembly." Sci China Life Sci 56(2): 156-162.
Ren, X., et al. (2012). "Evaluating de Bruijn graph assemblers on 454 transcriptomic data." PLoS One 7(12): e51188.
Mundry, M., et al. (2012). "Evaluating characteristics of de novo assembly software on 454 transcriptome data: a simulation approach." PLoS One 7(2): e31410.
4. RNAseq mapping evaluation
Grant, G. R., et al. (2011). "Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)." Bioinformatics 27(18): 2518-2528.
Lindner, R. and C. C. Friedel (2012). "A Comprehensive Evaluation of Alignment Algorithms in the Context of RNA-Seq." PLoS One 7(12): e52403.
5. Differential expression
For the normalization, edgeR's TMM method and DESeq's method are recommended.
Cuffdiff was not involved in the following evaluations.
Soneson, C. and M. Delorenzi (2013). "A comparison of methods for differential expression analysis of RNA-seq data." BMC Bioinformatics 14(1): 91.
Rapaport, F., et al. (2013). "Comprehensive evaluation of differential expression analysis methods for RNA-seq data." arXiv preprint arXiv:1301.5277.
Dillies, M. A., et al. (2012). "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis." Brief Bioinform.
Kvam, V. M., et al. (2012). "A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data." Am J Bot 99(2): 248-256.
Bullard, J. H., et al. (2010). "Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments." BMC Bioinformatics 11: 94.
6. Alternative splicing
So many softwares for this topic, but no evaluation has been published
7. System biology
This section includ the RNAseq specific analysis after the differentially expressed genes have been detected.