Notes on some bioinformatics books from two perspectives. Here’s what I’m looking for: 1) advanced discussion of computational and statistical methods for genomics, especially microarray analysis, and 2) suitability for an intro and survey bioinformatics course I teach.<\/p>\n
THE ANALYSIS OF GENE EXPRESSION DATA: METHODS AND SOFTWARE<\/b> Bioinformatics for Geneticists<\/b><\/a> Advances in Systems Biology<\/strong> Microarray Gene Expression Data Analysis: A Beginners Guide<\/strong> Bioinformatics: Databases and Systems<\/strong><\/a> Statistical Methods in Bioinformatics An Introduction<\/strong><\/a> <\/p>\n ‘In Silico’ Simulation of Biological Processes<\/strong><\/a> Micr Microarray Bioinformatics<\/strong><\/a> Bioinformatics: Methods and Protocols<\/strong><\/a> Computational Molecular Biology: an Algorithmic Approach<\/strong><\/a> DNA Microarrays: A Practical Approach<\/strong><\/a> Mathematics of Genome Analysis<\/strong><\/a> Post-genome Informatics<\/strong><\/a> Guide to Analysis of DNA Microarray Data<\/strong><\/a> Methods of Microarray Data Analysis II<\/strong><\/a>
\nedited by Giovanni Parmigiani, Elizabeth S Garrett, Rafael A Irizarry, Scott L Zeger
\ncompanion website<\/a>
\nGreat medium depth howto. Covers R, dChip, SAM, and packages that take more sophisticated approaches to analysis, clustering, and visualization.<\/p>\n
\nEdited by Michael R. Barnes and Ian C. Gray
\nPublished Online: 22 May 2003
\nGenomic orientation, but too light and introductory for my needs, too genomic focused for my course. Good starting point book for a geneticist.<\/p>\n
\nSeries: Advances in Experimental Medicine and Biology, Vol. 547
\nOpresko, Lee K.; Gephart, Julie M.; Mann, Michaela B. (Eds.)
\n2004
\nPulled this book for A Systems Approach to Discovering Signaling and Regulatory Pathways—or how to digest large interaction networks into relevant pieces<\/em> by Trey Ideker.<\/p>\n
\nby Helen C. Causton , John Quackenbush , Alvis Brazma
\n2003
\nWhat it says it is: design, image processing, normalization, basic stats, clustering.<\/p>\n
\nEdited by Stan Letovsky
\nKluwer Academic Press, Boston, 1999
\nA chapter a database. Covers the usual gang, plus chapters on WIT\/WIT2, KEGG, BioWidgets (Java), and AceDB (overview).<\/p>\n
\nSeries: Statistics for Biology and Health
\nEwens, Warren J. and Grant, Gregory
\n2nd ed. , 2004, 588p
\nerrata<\/a>
\nHeavy statistical treatment of sequence analysis. Starts with stats, then stat properties of one seq, multiple seq alignments, BLAST, Marlov chains, HMMs.<\/p>\n
\nNo. 247 Novartis Foundation Symposium
\n270 pages January 2003
\nPicked this up for the GO chapter. Also has a chapter on KEGG.<\/p>\n
\noarrays Methods and Applications: Nuts and Bolts<\/strong><\/a>
\nEdited by Gary Hardiman
\n2003
\nCovers topics from basic MA howtos to more specialized applications. A mixed bag. One chapter on microarray scanner evaluation, another on C. elegans<\/i> 50-mer oligos made by the Hardiman lab at UCSD. Chapter text and figures are online.<\/p>\n
\nMicroarray Quality Control<\/strong><\/a>
\nby Wei Zhang, Ilya Shmulevich and Jaakko Astola
\n136 pages February 2004
\nWhat the title says. Microarrays from a core facility perspective. Ends with normatization. Covers long oligo design.<\/p>\n
\nby Dov Stekel
\nCambridge University Press. 2003
\nThorough and clear coverage of microarray design, construction, image proc, and normalization. What caught my eye was the chapter on oilgo design, but its all good. Best single book on microarrays I’ve seen.<\/p>\n
\nEdited by Stephen Misener, Stephen A. Krawetz
\nHumana Press; 1st edition 2000
\nProgram based by chapter. Too program based for a course. Has good chpaters on Clustal and Phylip. A chapter on Trascription control regions using MatInspector\/GenomeInspector, weight matrix based sequence scanning for single or paired binding sites.<\/p>\n
\nby Pavel A. Pevzner
\nMIT Press, 2000
\nTakes a unique approach. Looks at interesting computational aspects of seqeunce-based biology, and sometimes takes an odd sideways view of problems, getting deep into the underlying mathematics. A great complement to a more straightforward survey book. The topic coverage isn’t broad enough for my course, and the topics get covered in more depth than I need. Runs thorugh algorithmics of restircition digests to microarrays including, of course, sequencing by hybridization. Covers algorithmics of genomic comparisons. Great source book for a serious comp bio student.<\/p>\n
\nEdited by Mark Schena
\nOxford University Press, Oxford, UK, 1999
\nA book on microarray technology. From DNA spotted on glass to enzymatic oligo arrays to ink jets, to microelectronic arrays. Written early on when many different approaches were being explored. Great source for technical information.<\/p>\n
\nby Jerome K. Percus
\nCambridge University Press; 1st edition 2002
\nNot a genomics book, but a mathmatician’ s look at DNA sequence, from clone libraries through seq alignments.<\/p>\n
\nby Minoru Kanehisa
\nOxford University Press, Oxford, 2000
\nCool cover! Written by the guy behind KEGG. An eclectic choice of topics, first databases, then sequence analysis basics, then network analysis. Had a few pages on comparing networks that I found useful.<\/p>\n
\nby Steen Knudsen
\nJohn Wiley and Sons, New York, 2002
\nA what-to-do book on microarray data analysis. Very short, too short to explain how to do things, but describes what to do, issues to consider, and what results will look like. Useful as a place to start, will show you what you don’t know. The linked site has example R and BioConductor code.<\/p>\n
\nEdited by Simon M. Lin and Kimberly F. Johnson
\nSpringer, 2002
\nPapers from CAMDA ‘01 Various array analysis, especially advanced clsutering methods. Particularly interesting are:<\/p>\n\n
\nGO-based clustering.<\/li>\n