References

RNA-Seq data analysis references

Chen, G., Wang, C., & Shi, T. (2011). Overview of available methods for diverse RNA-Seq data analyses. Science China. Life sciences, 54(12), 1121–8. doi:10.1007/s11427-011-4255-x

DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., et al. (2012). RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics (Oxford, England), 28(11), 1530–2. doi:10.1093/bioinformatics/bts196

Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., et al. (2012). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics, bbs046–. doi:10.1093/bib/bbs046

Feng, H., Qin, Z., & Zhang, X. (2012). Opportunities and Methods for Studying Alternative Splicing in Cancer with RNA-Seq. Cancer letters, null(null). doi:10.1016/j.canlet.2012.11.010

Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011). Computational methods for transcriptome annotation and quantification using RNA-seq. Nature Methods, 8(6), 469–477.

Hu, M., Zhu, Y., Taylor, J. M. G., Liu, J. S., & Qin, Z. S. (2012). Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq. Bioinformatics (Oxford, England), 28(1), 63–8. doi:10.1093/bioinformatics/btr616

Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10(3), R25.

Mezlini, A. M., Smith, E. J., Fiume, M., Buske, O., Savich, G., Shah, S., Aparicion, S., et al. (2012). iReckon: Simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Research, gr.142232.112–. doi:10.1101/gr.142232.112

Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods, 5(7), 621–8. doi:10.1038/nmeth.1226

Rehrauer, H. (n.d.). RNA-seq Quantification RNA-seq isoform quantification problem : How many transcripts ?

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England), 26(1), 139–40. doi:10.1093/bioinformatics/btp616

Schmieder, R., & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics (Oxford, England), 27(6), 863–4. doi:10.1093/bioinformatics/btr026

Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2012). Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature Biotechnology, advance on. doi:10.1038/nbt.2450

Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics (Oxford, England), 25(9), 1105–11.

Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., Pimentel, H., et al. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature protocols, 7(3), 562–78.

Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012). Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments. Molecular Ecology, n/a–n/a. doi:10.1111/mec.12014

Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., He, X., et al. (2010). MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Research, 38(18), e178.

Wang, L., Feng, Z., Wang, X., Wang, X., & Zhang, X. (2010). DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics (Oxford, England), 26(1), 136–8. doi:10.1093/bioinformatics/btp612

Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics, 10(1), 57–63. doi:10.1038/nrg2484

ChIP-Seq data analysis references

 

DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., et al. (2012). RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics (Oxford, England), 28(11), 1530–2. doi:10.1093/bioinformatics/bts196

Feng, J., Liu, T., Qin, B., Zhang, Y., & Liu, X. S. (2012). Identifying ChIP-seq enrichment using MACS. Nature protocols, 7(9), 1728–40. doi:10.1038/nprot.2012.101

Ji, H., Jiang, H., Ma, W., & Wong, W. H. (2011). Using CisGenome to analyze ChIP-chip and ChIP-seq data. Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.], Chapter 2, Unit2.13. doi:10.1002/0471250953.bi0213s33

Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10(3), R25.

Machanick, P., & Bailey, T. L. (2011). MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics (Oxford, England), 27(12), 1696–7. doi:10.1093/bioinformatics/btr189

Narlikar, L., & Jothi, R. (2012). ChIP-Seq data analysis: identification of protein-DNA binding sites with SISSRs peak-finder. Methods in molecular biology (Clifton, N.J.), 802, 305–22. doi:10.1007/978-1-61779-400-1_20

Newkirk, D., Biesinger, J., Chon, A., Yokomori, K., & Xie, X. (2011). AREM: aligning short reads from ChIP-sequencing by expectation maximization. Journal of computational biology : a journal of computational molecular cell biology, 18(11), 1495–505. doi:10.1089/cmb.2011.0185

Rozowsky, J., Euskirchen, G., Auerbach, R. K., Zhang, Z. D., Gibson, T., Bjornson, R., Carriero, N., et al. (2009). PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nature biotechnology, 27(1), 66–75. doi:10.1038/nbt.1518

Schmieder, R., & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics (Oxford, England), 27(6), 863–4. doi:10.1093/bioinformatics/btr026

Wilbanks, E. G., & Facciotti, M. T. (2010). Evaluation of algorithm performance in ChIP-seq peak detection. PloS one, 5(7), e11471. doi:10.1371/journal.pone.0011471

Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., et al. (2008). Model-based analysis of ChIP-Seq (MACS). Genome biology, 9(9), R137. doi:10.1186/gb-2008-9-9-r137