The SeqAn C++ template library for efficient sequence analysis: A resource for programmers

Reinert, Knut; Dadi, Temesgen Hailemariam; Ehrhardt, Marcel; Hauswedell, Hannes; Mehringer, Svenja; Rahn, René; Kim, Jong-Kyu; Pockrandt, Christopher; Winkler, Jörg; Siragusa, Enrico; Urgese, Gianvito; Weese, David

doi:10.1016/j.jbiotec.2017.07.017

Cited by 97 publications

(76 citation statements)

References 57 publications

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“…We have presented GenMap, a fast and exact algorithm to compute the mappability of genomes up to e errors, which is based on the C++ sequence analysis library SeqAn [16]. It is significantly faster, often by a magnitude than the algorithm from the widely used GEM suite while refraining from approximations.…”

Section: Discussion and Outlookmentioning

confidence: 99%

GenMap: Fast and Exact Computation of Genome Mappability

Pockrandt

Alzamel

Iliopoulos

et al. 2019

Preprint

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We present a fast and exact algorithm to compute the (k, e)-mappability. Its inverse, the (k, e)frequency counts the number of occurrences of each k-mer with up to e errors in a sequence. The algorithm we present is a magnitude faster than the algorithm in the widely used GEM suite while not relying on heuristics, and can even compute the mappability for short k-mers on highly repetitive plant genomes. We also show that mappability can be computed on multiple sequences to identify marker genes illustrated by the example of E. coli strains. GenMap allows exporting the mappability information into different formats such as raw output, wig and bed files. The application and its C++ source code is available on https://github.com/cpockrandt/genmap.

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Section: Discussion and Outlookmentioning

confidence: 99%

GenMap: Fast and Exact Computation of Genome Mappability

Pockrandt

Alzamel

Iliopoulos

et al. 2019

Preprint

Self Cite

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“…We use the sdsl-lite library (Gog et al, 2014) for succinct and compressed data structures: bit vector with efficient rank and select operations, compressed integer vector using Elias delta coding, and FM index. The wotd-tree we use is provided by the SeqAn2 library (Reinert et al, 2017). All running times are measured on a system with a 3 GHz Intel Xeon E7-8857 processor running Debian 9.4 with Linux kernel 4.9.91.…”

Section: Methodsmentioning

confidence: 99%

“…Even though represented by quite different data structures, sequence graph, path index and chunk index support a common set of abstract traversal operations. In the following, we describe our method in terms of such abstract operations and refer the reader to excellent text books on the details of these data structures (Ohlebusch, 2013;Mäkinen et al, 2015) as well as to mature implementations in libraries such as Seqan (Döring et al, 2008;Reinert et al, 2017) and SDSL (Gog et al, 2014).…”

Section: Traversing Graph Path Index and Chunk Indexmentioning

confidence: 99%

Fully-sensitive Seed Finding in Sequence Graphs Using a Hybrid Index

Ghaffaari

Marschall

2019

Preprint

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Motivation: Sequence graphs are versatile data structures that are, for instance, able to represent the genetic variation found in a population and to facilitate genome assembly. Read mapping to sequence graphs constitutes an important step for many applications and is usually done by first finding exact seed matches, which are then extended by alignment. Existing methods for finding seed hits prune the graph in complex regions, leading to a loss of information especially in highly polymorphic regions of the genome. While such complex graph structures can indeed lead to a combinatorial explosion of possible alleles, the query set of reads from a diploid individual realizes only two alleles per locus-a property that is not exploited by extant methods. Results: We present the Pan-genome Seed Index (PSI), a fully-sensitive hybrid method for seed finding, which takes full advantage of this property by combining an index over selected paths in the graph with an index over the query reads. This enables PSI to find all seeds while eliminating the need to prune the graph. We demonstrate its performance with different parameter settings on both simulated data and on a whole human genome graph constructed from variants in the 1000 Genome Project data set. On this graph, PSI outperforms GCSA2 in terms of index size, query time, and sensitivity. Availability: The C++ implementation is publicly available at: https://github.com/cartoonist/psi.

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“…The classification method, which is based on the k-mer counting lemma and a progressive filtering step, improves sensitivity and precision compared to state-of-the-art tools when using larger sets of references. Ganon was developed in C++ using the SeqAn library (Reinert et al, 2017) and Python. The code is open source and freely available from: https://gitlab.com/rki_bioinformatics/ganon 2 Methods…”

Section: Introductionmentioning

confidence: 99%

ganon: precise metagenomics classification against large and up-to-date sets of reference sequences

Piro

Dadi

Seiler

et al. 2018

Preprint

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11The exponential growth of assembled genome sequences greatly benets metagenomics 12 studies, providing a broader catalog of reference organisms on a variety of environments. 13 However, currently available methods struggle to manage the increasing amount of sequences 14 and their frequent updates. Indexing the current RefSeq is no longer possible on standard 15 infrastructures and it can take days and hundreds of GB of memory on large servers. Few 16 methods address these issues thus far, and even though many can theoretically handle large 17 amounts of references, time/memory requirements are prohibitive in practice. As a result, 18 many studies that require sequence classication use the available tools in conjunction with 19 often outdated and almost never truly up-to-date indices. This also means that the taxonomic 20 composition of the reference database is not being adjusted based on the study performed. 21 These factors can lead to unnecessary performance problems in the sequence classication. 22 Motivated by those limitations we created ganon, a k-mer based read classication tool that 23 uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer count-24 ing/ltering scheme. Ganon provides an ecient method for indexing references, keeping them 25 updated. It requires less than 55 minutes to index the complete RefSeq of bacteria, archaea, 26 fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time 27 necessary to create them, allowing researchers to always work with the most recent references. 28 Ganon makes it possible to query against very large reference sets and therefore it classies 29 signicantly more reads and identies more species than similar methods. When classifying a 30 high-complexity real dataset from the CAMI challenge against complete genomes from RefSeq, 31 ganon shows strongly increased precision while exhibiting equal or better sensitivity compared 32 with state-of-the-art tools. When classifying the same dataset against the complete RefSeq, 33 ganon improved the F1-Score by 65% at the genus level. Ganon supports taxonomy-and 34 assembly-level classication as well as multiple indices and hierarchical classication. The 35 software is open-source and available at: https://gitlab.com/rki_bioinformatics/ganon 36 42 prede(ned nd stti set of referene sequenesF wny of those pprohes re txonomyEsed 43 U nd use this lssi(tion to etter understnd the omposition of smplesF 44 * PiroV@rki.de † RenardB@rki.de I he mount of omplete or drft genomi sequenes in puli repositories is rpidly growing 45 @pigure IA due to dvnes in genome sequeningD improvements in red qulityD length nd ovE 46 erge nd lso etter lgorithms for genome ssemlyF sn dditionD mny prtil nd omplete 47

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The SeqAn C++ template library for efficient sequence analysis: A resource for programmers

Abstract: We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.

Cited by 97 publications

References 57 publications

GenMap: Fast and Exact Computation of Genome Mappability

GenMap: Fast and Exact Computation of Genome Mappability

Fully-sensitive Seed Finding in Sequence Graphs Using a Hybrid Index

ganon: precise metagenomics classification against large and up-to-date sets of reference sequences

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