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Genome-Scale Algorithm Design
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  • Cited by 18
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Kärkkäinen, Juha and Kempa, Dominik 2016. String Processing and Information Retrieval. Vol. 9954, Issue. , p. 204.

    Siren, Jouni 2016. Burrows-Wheeler Transform for Terabases. p. 211.

    Alanko, Jarno and Norri, Tuukka 2017. String Processing and Information Retrieval. Vol. 10508, Issue. , p. 1.

    Alanko, Jarno Cunial, Fabio Belazzougui, Djamal and Mäkinen, Veli 2017. A framework for space-efficient read clustering in metagenomic samples. BMC Bioinformatics, Vol. 18, Issue. S3,

    Badkobeh, Golnaz Kärkkäinen, Juha Puglisi, Simon J. and Zhukova, Bella 2017. String Processing and Information Retrieval. Vol. 10508, Issue. , p. 68.

    Kuosmanen, Anna Norri, Tuukka and Mäkinen, Veli 2017. Evaluating approaches to find exon chains based on long reads. Briefings in Bioinformatics, p. bbw137.

    Louza, Felipe A. Telles, Guilherme P. Hoffmann, Steve and Ciferri, Cristina D. A. 2017. Generalized enhanced suffix array construction in external memory. Algorithms for Molecular Biology, Vol. 12, Issue. 1,

    Sun, Chen Harris, Robert S. Chikhi, Rayan and Medvedev, Paul 2017. Research in Computational Molecular Biology. Vol. 10229, Issue. , p. 272.

    Hujdurović, Ademir Husić, Edin Milanič, Martin Rizzi, Romeo and Tomescu, Alexandru I. 2017. Graph-Theoretic Concepts in Computer Science. Vol. 10520, Issue. , p. 303.

    Sun, Chen Harris, Robert S. Chikhi, Rayan and Medvedev, Paul 2018. AllSome Sequence Bloom Trees. Journal of Computational Biology, Vol. 25, Issue. 5, p. 467.

    Kuosmanen, Anna Paavilainen, Topi Gagie, Travis Chikhi, Rayan Tomescu, Alexandru and Mäkinen, Veli 2018. Research in Computational Molecular Biology. Vol. 10812, Issue. , p. 105.

    Alzamel, Mai Charalampopoulos, Panagiotis Iliopoulos, Costas S. and Pissis, Solon P. 2018. Combinatorial Algorithms. Vol. 10765, Issue. , p. 343.

    FUKUNAGA, Shouhei TAKABATAKE, Yoshimasa I, Tomohiro and SAKAMOTO, Hiroshi 2018. Approximate Frequent Pattern Discovery in Compressed Space. IEICE Transactions on Information and Systems, Vol. E101.D, Issue. 3, p. 593.

    Farruggia, Andrea Gagie, Travis Navarro, Gonzalo Puglisi, Simon J and Sirén, Jouni 2018. Relative Suffix Trees. The Computer Journal, Vol. 61, Issue. 5, p. 773.

    Hujdurovic, Ademir Kacar, Ursa Milanic, Martin Ries, Bernard and Tomescu, Alexandru I. 2018. Complexity and Algorithms for Finding a Perfect Phylogeny from Mixed Tumor Samples. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 15, Issue. 1, p. 96.

    Obscura Acosta, Nidia Mäkinen, Veli and Tomescu, Alexandru I. 2018. A safe and complete algorithm for metagenomic assembly. Algorithms for Molecular Biology, Vol. 13, Issue. 1,

    Giancarlo, Raffaele Manzini, Giovanni Restivo, Antonio Rosone, Giovanna and Sciortino, Marinella 2018. Developments in Language Theory. Vol. 11088, Issue. , p. 1.

    Gog, Simon Kärkkäinen, Juha Kempa, Dominik Petri, Matthias and Puglisi, Simon J. 2018. Fixed Block Compression Boosting in FM-Indexes: Theory and Practice. Algorithmica,

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Book description

High-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows–Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.


‘Genome-Scale Algorithm Design is a well-thought-out … book that fills a gap in the recent literature … [on algorithms] for bioinformatics. It offers a sound, clear, and rich overview of computer science methods for the challenge of today's biological sequence analysis. I [recommend] it to students as well as to researchers in the field.’

Nadia Pisanti - University of Pisa

‘This book will be a new reference text for string data structures and algorithms, as well as their applications in computational molecular biology and genome analysis. The technically precise style, illustrated with a great collection of well-designed examples and many exercises, makes it an ideal resource for researchers, students, and teachers.’

Jens Stoye - Universität Bielefeld

‘I think the book is really great and could envision using it in courses in bioinformatics and data compression. The book’s scope, clarity, and mathematically precise, compelling explanations make the advanced topics in genome-wide bioinformatics accessible to [a] wide audience.’

Christina Boucher - Colorado State University

‘This book is a timely, rigorous and comprehensive systematization of the concepts and tools at the core of post-genome bioinformatics. By choosing to incorporate the principles of algorithms design most pertinent to the topic, the authors have created a rare, self-contained reference that will smoothly introduce the neophyte and assist the seasoned researcher as well as anybody who needs to understand these issues at a fundamental level. The organization of the material, the clarity of exposition and rigor of treatment make the book an ideal textbook for courses directed at a mixed audience coming from diverse, even distant backgrounds.’

Alberto Apostolico - Georgia Institute of Technology

‘A very timely textbook covering many recently established techniques and applications at a level of rigor befitting a computer science audience.’

Paul Medvedev - Pennsylvania State University

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