SeqAn An efficient, generic C++ library for sequence analysis

By Andreas Gogol-Döring, David Weese, Tobias Rausch, Knut Reinert. BMC Bioinformatics, 2008

381 citations recorded by the source corpus.

BackgroundThe use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome [1] would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.ResultsTo remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.ConclusionWe anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dfbcb7584fce4b9a4dce998fe09a087d0b1444eb

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Citations in the corpus

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (2009, methodology): “Bowtie is written in C++ and uses the SeqAn library [30].”
GenomeTools: A Comprehensive Software Library for Efficient Processing of Structured Genome Annotations (2013, methodology): “In addition, this section presents the results of several benchmarks for measuring the performance of the GenomeTools annotation processing functionality in comparison to BioPerl and SeqAn.”
Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads (2015, methodology): “Errors were subsequently introduced with Mason [16]; error rates were extracted from alignments of same-length Illumina Human Body Map reads (Additional file 1, Section S1).”
TopHat: discovering splice junctions with RNA-Seq (2009, neutral): “TopHat is implemented in C++ and Python and runs on Linux and Mac OS X. It makes substantial use of previously described tools, including Bowtie (Langmeadet al., 2009), Maq (Liet al., 2008), and the SeqAn library ( Doring et al., 2008 ).”
OpenMS: a flexible open-source software platform for mass spectrometry data analysis (2016, methodology): “The lowest layer, the data and signal processing layer, is built using a set of open-source libraries for numerical computing, XML parsing, sequence analysis [16] and statistics.”
DELLY: structural variant discovery by integrated paired-end and split-read analysis (2012, background): “The sensitivity of DELLY (SR), however, depends on a sufficient read length and coverage, limitations that call for further research.”
FLEXBAR—Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms (2012, methodology): “FLEXBAR has been implemented in C++ using the Seqan library [1].”