SRAMM: Short Read Alignment Mapping Metrics

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://github.com/achon/sramm.

Download Full-text

SEAL: a distributed short read mapping and duplicate removal tool

Bioinformatics ◽

10.1093/bioinformatics/btr325 ◽

2011 ◽

Vol 27 (15) ◽

pp. 2159-2160 ◽

Cited By ~ 85

Author(s):

L. Pireddu ◽

S. Leo ◽

G. Zanetti

Keyword(s):

Read Mapping ◽

Short Read ◽

Short Read Mapping

Download Full-text

Short Read Mapping: An Algorithmic Tour

Proceedings of the IEEE ◽

10.1109/jproc.2015.2455551 ◽

2017 ◽

Vol 105 (3) ◽

pp. 436-458 ◽

Cited By ~ 30

Author(s):

Stefan Canzar ◽

Steven L. Salzberg

Keyword(s):

Read Mapping ◽

Short Read ◽

Short Read Mapping

Download Full-text

Faster short-read mapping with strobemer seeds in syncmer space

10.1101/2021.06.18.449070 ◽

2021 ◽

Author(s):

Kristoffer Sahlin

Keyword(s):

High Speed ◽

Mapping Accuracy ◽

Original Sequence ◽

Short Read ◽

Short Read Mapping ◽

Alignment Algorithms ◽

Reverse Complement ◽

Short Read Aligner ◽

Candidate Regions ◽

Burrows Wheeler Transform

Short-read genome alignment is a fundamental computational step used in many bioinformatic analyses. It is therefore desirable to align such data as fast as possible. Most alignment algorithms consider a seed-and-extend approach. Several popular programs perform the seeding step based on the Burrows-Wheeler Transform with a low memory footprint, but they are relatively slow compared to more recent approaches that use a minimizer-based seeding-and-chaining strategy. Recently, syncmers and strobemers were proposed for sequence comparison. Both protocols were designed for improved conservation of matches between sequences under mutations. Syncmers is a thinning protocol proposed as an alternative to minimizers, while strobemers is a linking protocol for gapped sequences and was proposed as an alternative to k-mers. The main contribution in this work is a new seeding approach that combines syncmers and strobemers. We use a strobemer protocol (randstrobes) to link together syncmers (i.e., in syncmer-space) instead of over the original sequence. Our protocol allows us to create longer seeds while preserving mapping accuracy. A longer seed length reduces the number of candidate regions which allows faster mapping and alignment. We also contribute the insight that speed-wise, this protocol is particularly effective when syncmers are canonical. Canonical syncmers can be created for specific parameter combinations and reduce the computational burden of computing the non-canonical randstrobes in reverse complement. We implement our idea in a proof-of-concept short-read aligner strobealign that aligns short reads 3-4x faster than minimap2 and 15-23x faster than BWA and Bowtie2. Many implementation versions of, e.g., BWA, achieve high speed on specific hardware. Our contribution is algorithmic and requires no hardware architecture or system-specific instructions. Strobealign is available at https://github.com/ksahlin/StrobeAlign.

Download Full-text

Fuzzy set intersection based paired-end short-read alignment

10.1101/2021.11.23.469039 ◽

2021 ◽

Author(s):

William J Bolosky ◽

Arun Subramaniyan ◽

Matei Zaharia ◽

Ravi Pandya ◽

Taylor Sittler ◽

...

Keyword(s):

Fuzzy Set ◽

Genetic Material ◽

Genomic Data ◽

Short Read ◽

Read Alignment ◽

Short Read Alignment ◽

Set Intersection

Much genomic data comes in the form of paired-end reads: two reads that represent genetic material with a small gap between. We present a new algorithm for aligning both reads in a pair simultaneously by fuzzily intersecting the sets of candidate alignment locations for each read. This algorithm is often much faster and produces alignments that result in variant calls having roughly the same concordance as the best competing aligners.

Download Full-text