MPSSR User Support
User Support Options
Click on the links to go to section of interest:
Data Analysis - a discussion of data analysis options for MPSSR users
Background - a list of publications selected to explain massively parallel sequencing and its applications
Data Analysis
The MPSSR is negotiating with a corporate partner to make affordable data analysis available to our users. When the details of the arrangement are finalized, an announcement will appear on this page, along with instructions on accessing the software and links to the portal.
Background
The following lists of articles are provided to direct new users to appropriate literature on the topics of short read sequencing and its various applications, as well as some papers we found interesting. This list is not meant to be exhaustive, but to provide a starting place for investigators to learn about the technology and how it can be used.
Reviews on sequencing technology
Metzker, M. Sequencing technologies - the next generation. 2009. Nature Reviews Genetics 11, 31.
Shendure, J. and Ji, H. 2008. Next-generation DNA sequencing. 2008. Nature Biotechnology 26, 1135.
ten Bosch, J.R. and Grody, W.W. Keeping up with the next generation - massively parallel sequencing in clinical diagnostics. 2008. Journal of Molecular Diagnostics 10, 484.
RNA-seq
Mane, S. P. et al. Transcriptome sequencing of the Microarray Quality Control (MAQC) RNA reference samples using the next generation sequencing. BMC Genomics 10, 264.
Mortazavi, A. et al. Mapping and quantifying mammalian transcriptomes by RNA-seq. 2008. Nature Methods 5, 621.
Wang, Z. et al. RNA-Seq: a revolutionary tool for transcriptomics. 2009. Nature Reviews Genetics 10, 57.
Chip-seq
Euskirchen, G.M. et al. Mapping of transcription factor binding regions in mammalian cells by ChIP: Comparison of array- and sequencing-based technologies. 2007. Genome Research 17, 898.
Park, P.J. ChIP-seq: advantages and challenges of a maturing technology. 2009. Nature Reviews Genetics 10, 669.
Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. 2009. Nature 457, 854.
microRNA analysis
Friedlaender, M.R. et al. Discovering microRNAs from deep sequencing data using miRDeep. 2008. Nature Biotechnology 26, 407.
Jacquier, A. The complex eukaryotic transcriptome: unexpected pervasive transcription and novel small RNAs. 2009. Nature Reviews Genetics 10, 833.
Langenberger, D. et al. Identification and classification of small RNAs in transcriptome sequence data. 2010. Pacific Symposium on Biocomputing 15, 80.
Morin, R.D. et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. 2008. Genome Research 18, 610.
Olson, A.J. et al. Analysis of large-scale sequencing of small RNAs. 2007. Pacific Symposium on Biocomputing 13, 126.
Wang, W.-C. et al. miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. 2009. BMC Bioinformatics 10, 328.
Genome resequencing
Chain, P.S.G. et al. Genome project standards in a new era of sequencing. 2009. Science 326, 236.
Huang, X. et al. High-throughput genotyping by whole -genome resequencing. 2009. Genome Research 19, 1068.
Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. 2010. Nature Methods 7, 111.
Ng, S.B. et al. Exome sequencing identifies the cause of a mendelian disorder. 2009. Nature Genetics 42, 30.
Methylation
Hodges, E. et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. 2009. Genome Research 19, 1593.
Maegawa, S. et al. Widespread and tissue specific age-related DNA methylation changes in mice. 2010. Genome Research epub 10.1101 gr.096826.109
Informatics for sequencing analysis
The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. 2007. Nature 447, 799.
Flicek, P. and Birney, E. Sense from sequence reads: methods for alignment and assembly. 2009. Nature Methods 6, S6.
Li, H. and Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. 2009. Bioinformatics 25, 1754.
Medvedev, P. et al. Computational methods for discovering structural variation with next-generation sequencing. 2009. Nature Methods 6, S13.
Pepke, S. et al. Computation for ChIP-seq and RNA-seq studies. 2009. Nature Methods 6, S22.
Rickter. B.G. and Sexton, D.P. Managing and analyzing next-generation sequence data. 2009. PLoS Computational Biology 5, e1000369.
Articles that we found interesting that aren't specifically related to sequencing
Chandras, C. et al. Models for financial sustainability of biological databases and resources. 2009. Database 2009, bap017; doi:10,1093
Dudley, J.T. and Butte, A.J. A quick guide for developing effective bioinformatics programming skills. 2009. PLoS Computational Biology 5, e1000589.
Lewitter, F. and Reghan, M. Establishing a successful bioinformatics core facility team. PLoS Computational Biology 5, e1000368.
Nelson, Bryn. Empty Archives. 2009. Nature 461, 160.
