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Practical Guide to ChIP-seq Data Analysis - Focus Computational Biology Series (Hardback)
  • Practical Guide to ChIP-seq Data Analysis - Focus Computational Biology Series (Hardback)
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Practical Guide to ChIP-seq Data Analysis - Focus Computational Biology Series (Hardback)

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£54.00
Hardback 100 Pages / Published: 05/11/2018
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Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through the steps of ChIP-seq analysis: from quality control, through peak calling, to downstream analyses. It will help experimental biologists to design their ChIP-seq experiments with the analysis in mind, and to perform the basic analysis steps themselves. It also aims to support bioinformaticians to understand how the data is generated, what the sources of biases are, and which methods are appropriate for different analyses.

Publisher: Taylor & Francis Ltd
ISBN: 9781138596528
Number of pages: 100
Weight: 249 g
Dimensions: 216 x 140 mm


MEDIA REVIEWS

I found the book to be very well structured; the topic is presented following a logical progression, guiding the reader through a ChIP-seq experiment, illustrating each step of the analytical workflow. The workflow is nicely divided in smaller blocks, each including the relevant theory and practical exercises (consisting of code) that enable the reader to put the theory intro practice, plus suggestions for additional reading.

I particularly appreciated the reference to biases and important issues that need to be considered when planning an experiment, as well as the discussion of data quality issues, extremely relevant to users dealing with publicly available data.

This will be a great resource for teaching, particularly given the use of public data as well as open source software. All that is presented in the book is reproducible, making it an extremely useful resource for any learner. I also appreciated the chapter dedicated to downstream analysis and interpretation - great to see as extremely useful and not often covered to the extent required; similar observation for the integration with other data types, a very timely subject of great interest to many researchers working with different data types and in need of combining the results of different experiment types. This book will be useful to different audiences (experimentalists as well as bioinformaticians) and it is written in a way which is easy to understand for non-technical audiences.

-Gabriella Rustici, University of Cambridge

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