Genome Sequencing Technology and Algorithms examines the computational work behind DNA sequencing, from interpreting raw signals to assembling and comparing genomes. Edited by Sun Kim, Haixu Tang, and Elaine R. Mardis, this 2008 volume addresses graduate students and researchers in genomics, bioinformatics, and computational biology.
Digital ebook. No physical book.
This edited volume connects sequencing technologies with the computational decisions involved in analyzing their output. Topics include base calling, quality scoring, error handling, sequence assembly, alignment, and genome annotation. Its emphasis on algorithmic reasoning places individual analysis stages within the larger task of reconstructing and interpreting genomic sequences.
Assembly challenges such as repeated regions and the use of mate-pair information receive attention alongside comparative genomics and data management. The chapters bring together biological questions and computational strategies for readers who already have some molecular biology and programming experience. Published in 2008, the book supplies a historical technical foundation for graduate study and research in sequence analysis.
This edited volume follows sequencing from raw signal to interpreted genome. Base calling, quality scoring, assembly, alignment and annotation are treated as linked computational problems, written for graduate students and researchers who already have some molecular biology and programming experience.
The book pairs sequencing technologies with the computational decisions their output demands. Early stages receive attention first: turning signals into bases, scoring quality and handling error. Assembly follows, including the difficulties posed by repeated regions and the use of mate-pair information to resolve them. Alignment, genome annotation and comparative genomics place those stages within the larger job of reconstructing and interpreting a genome, while data management runs alongside as a concern. Throughout, the emphasis is on algorithmic reasoning rather than any single tool. Published in 2008, the volume captures the methods and assumptions of its moment, giving graduate readers and researchers a foundation for sequence analysis and a way to see how the field's problems were framed at the time.
Sequencing begins with raw output that must be turned into usable sequence data. Base calling is presented as the first computational decision, linking instrument behavior to interpreted letters. The chapter sets the tone for treating each stage as an algorithmic problem.
Quality scores accompany sequence data and shape how much trust later stages place in it. Error handling is treated as a continuous concern rather than a one-time correction. These ideas recur wherever sequence is assembled or aligned.
Genome assembly is the problem of reconstructing a whole from fragments. Repeated regions make this difficult because they create ambiguity about where pieces belong. The text presents assembly as a reasoning task shaped by the nature of the data.
Mate-pair information is introduced as a way to constrain assembly across distances. It helps resolve arrangements that fragment overlap alone cannot settle. The technique shows how additional data types support algorithmic reasoning.
Alignment places sequence against references, while annotation assigns meaning to regions. Comparative genomics uses alignment across genomes to reveal similarities and differences. This cluster moves from assembling a sequence to interpreting it.
The book's unifying thread is algorithmic reasoning rather than any single software tool. Data management appears as a practical companion to analysis. Published in 2008, it also records how the field framed these problems at that time.
Graduate students and researchers in genomics, bioinformatics and computational biology who already have some molecular biology and programming background.
Edition, audience, and study-guide details at a glance.
Genome Sequencing Technology and Algorithms examines the computational work behind DNA sequencing, from interpreting raw signals to assembling and comparing genomes. Edited by Sun Kim, Haixu Tang, and Elaine R. Mardis, this 2008 volume addresses graduate students and researchers in genomics, bioinformatics, and computational biology.
Graduate students and researchers in genomics, bioinformatics and computational biology who already have some molecular biology and programming background.
Yes. This page includes an original study guide with six thematic sections, key takeaways, and a topic map for structured reading.
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