Estimating accuracy of RNA-Seq and microarrays with proteomics
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* Corresponding authors: Wei Chen wei@molgen.mpg.de - Philipp Khaitovich khaitovich@eva.mpg.de
- † Equal contributors
BMC Genomics 2009, 10:161 doi:10.1186/1471-2164-10-161
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BioMed Central: 7 citations
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Software
NanoStriDE: normalization and differential expression analysis of NanoString nCounter data Christopher D Brumbaugh, Hyunsung J Kim, Mario Giovacchini, Nader Pourmand BMC Bioinformatics 2011, 12:479 (16 December 2011) |
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Methodology article
Construction and evaluation of a whole genome microarray of Chlamydomonas reinhardtii Jörg Toepel, Stefan P Albaum, Samuel Arvidsson, Alexander Goesmann, Marco la Russa, Kristin Rogge, Olaf Kruse BMC Genomics 2011, 12:579 (25 November 2011) |
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Research article
Exploring the gonad transcriptome of two extreme male pigs with RNA-seq Anna Esteve-Codina, Robert Kofler, Nicola Palmieri, Giovanni Bussotti, Cedric Notredame, Miguel Pérez-Enciso BMC Genomics 2011, 12:552 (8 November 2011) |
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Research article
Diana V Dugas, Marcela K Monaco, Andrew Olson, Robert R Klein, Sunita Kumari, Doreen Ware, Patricia E Klein BMC Genomics 2011, 12:514 (18 October 2011) |
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Review
Microarrays, deep sequencing and the true measure of the transcriptome John H Malone, Brian Oliver BMC Biology 2011, 9:34 (31 May 2011) Global measures of gene expression can now be extracted either from microarrays or from RNA-seq, which do not always seem to give the same answer. Malone and Oliver review the advantages and limitations of each and conclude that, with some important exceptions, they tell the same story.
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Research article
Integrated network analysis of transcriptomic and proteomic data in psoriasis Eleonora Piruzian, Sergey Bruskin, Alex Ishkin, Rustam Abdeev, Sergey Moshkovskii, Stanislav Melnik, Yuri Nikolsky, Tatiana Nikolskaya BMC Systems Biology 2010, 4:41 (8 April 2010) |
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Method
Gene ontology analysis for RNA-seq: accounting for selection bias Matthew D Young, Matthew J Wakefield, Gordon K Smyth, Alicia Oshlack Genome Biology 2010, 11:R14 (4 February 2010) GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-seq |