| shenayuwzv | Date: Monday, 24 Jun 2013, 21:07:11 | Message # 1 |
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锘縜 bioinformatics primer for the analysis of genetic data
Box 9600, 2300 RC Leiden, Netherlands. The availability of 100K to 1M SNP chips and massive parallel sequencing technology has radically changed the setup of genetic linkage and association studies. With the increased throughput in these studies, new algorithms and software for highthroughput data analysis became available. Therefore, a new edition of Bioinformatics for Geneticists seems to be adequately timed.
Given the rapid developments in the field, such a book is per definition outdated. It is also noted that the development of new data analysis routines will never be able to keep up with the requirements from the newest technologies. For the analysis of massive parallel sequencing data, analysis tools are hardly available, partly explaining why there is, also in the current edition, hardly any attention paid to this type of analysis. To program yourself is the best option, and is also advocated by the editor. In Chapter 2, a short appetizer for programming with Perl is provided, although probably still too farfetched for a person with a biological or medical background. A more likely option in currentday genetic research is a tight collaboration between geneticists and informaticians. For both groups, the book provides useful material. Given the amount of background in classical and current methods in genetics, the editor might as well have chosen Genetics for Bioinformaticians as a subtitle. This book may really help to get geneticists and bioinformaticians on 'speakingterms'.
What is clear after reading the book is that there are many possibilities to improve the design and the interpretation of wetlab experiments by in silico tools. This is not only true for expensive highthroughput experiments but also for more targeted and everyday experiments. Therefore, this book contains some essential reading for almost any person working in the field of molecular genetics
The book gives the geneticist a valuable survey of the enormously rich data resources and numerous analysis tools available. It focuses on human genetics, and covers gene annotation, comparative genomics, disease gene mapping, prediction of the effects of mutations, noncoding RNAs, microarrays, and the use of gene expression data as quantitative traits. Some chapters merely contain lists of available tools and regrettably refrain from a discussion of their advantages and limitations and fail to give insight into which tool to be used in which situation. Given the expected frequent changes in web addresses and the availability of these tools, a web page with the updated links to the resources listed would be extremely helpful, but is currently not available. On the positive side, the book provides a considerable amount of case studies that nicely illustrate the reasoning and research path one can follow in currentday genetics. A point of criticism would be that some crossreferences between chapters are lacking and that there is some redundancy here and there (eg, programs for finding exonsplice enhancer sites are dealt with in chapters 11 and 12).
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