Algorithms for Big Data Problems in de Novo Genome Assembly Article Swipe
Anand Srivastav
,
Axel Wedemeyer
,
Christian Schielke
,
Jan Schiemann
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-21534-6_13
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-21534-6_13
De novo genome assembly is a fundamental task in life sciences. It is mostly a typical big data problem with sometimes billions of reads, a big puzzle in which the genome is hidden. Memory and time efficient algorithms are sought, preferably to run even on desktops in labs. In this chapter we address some algorithmic problems related to genome assembly. We first present an algorithm which heavily reduces the size of input data, but with no essential compromize on the assembly quality. In such and many other algorithms in bioinformatics the counting of k-mers is a botleneck. We discuss counting in external memory. The construction of large parts of the genome, called contigs, can be modelled as the longest path problem or the Euler tour problem in some graphs build on reads or k-mers. We present a linear time streaming algorithm for constructing long paths in undirected graphs, and a streaming algorithm for the Euler tour problem with optimal one-pass complexity.
Related Topics
Concepts
Computer science
Sequence assembly
Genome
Streaming algorithm
Big data
Time complexity
Task (project management)
Counting problem
Contig
Theoretical computer science
Path (computing)
Algorithm
Upper and lower bounds
Mathematics
Data mining
Biology
Biochemistry
Economics
Gene
Programming language
Transcriptome
Mathematical analysis
Gene expression
Management
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-031-21534-6_13
- https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_13.pdf
- OA Status
- hybrid
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317181459
All OpenAlex metadata
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https://openalex.org/W4317181459Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/978-3-031-21534-6_13Digital Object Identifier
- Title
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Algorithms for Big Data Problems in de Novo Genome AssemblyWork title
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Anand Srivastav, Axel Wedemeyer, Christian Schielke, Jan SchiemannList of authors in order
- Landing page
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https://doi.org/10.1007/978-3-031-21534-6_13Publisher landing page
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https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_13.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/978-3-031-21534-6_13.pdfDirect OA link when available
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Computer science, Sequence assembly, Genome, Streaming algorithm, Big data, Time complexity, Task (project management), Counting problem, Contig, Theoretical computer science, Path (computing), Algorithm, Upper and lower bounds, Mathematics, Data mining, Biology, Biochemistry, Economics, Gene, Programming language, Transcriptome, Mathematical analysis, Gene expression, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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