AdapterRemoval v2: rapid adapter trimming, identification, and read merging
By Mikkel Schubert, Stinus Lindgreen, Ludovic Antoine Alexandre Orlando. BMC Research Notes, 2016
1,905 citations recorded by the source corpus.
As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run. We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support. We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations.
Subfields: Hardware and Architecture, Information Systems and Management, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/58e09ea89672e6b5be8848bc9d442986b985ae97
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Citations in the corpus
- Ancient DNA (2019, background): “Though widely used, UV destruction of DNA mimics that of natural environmental processes, causing photoproducts in DNA that lead primarily to C to T transitions [46].”
- Single‐tube library preparation for degraded DNA (2017, methodology): “3’ adapter removal was performed with the program AdapterRemoval 2 (Schubert et al. 2016), followed by discarding reads shorter than 25 bases.”
- SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data (2017, methodology): “Intermsofaccelerationapproach,multithreading is the sole method adopted by existing tools [14–16, 24– 28 ], For the future works, we will continue adding functions to feature modules.”