Alignment-free sequence comparison-a review

By Susana Vinga, Jonas S. Almeida. Bioinform., 2003

851 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3ada434d8006a3a8e3d1ec6a4c5a35509fe817b4

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Citations in the corpus

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study (2020, background): “similarities, their application can be challenging in many cases [45, 46].”
Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutions (2009, methodology): “The first usage of l -mer counts for biological sequence comparison was implemented by Blaisdell (6) and more recent developments in alignment-free comparison have been reviewed by Vinga and Almeida (7).”
Fast model-based protein homology detection without alignment (2007, methodology): “Rather it extracts new similarity measures automatically and may cope with genetic recombination and genetic shuffling (Vinga and Almeida, 2003).”
MUSCLE: a multiple sequence alignment method with reduced time and space complexity (2004, neutral): “Many sequence comparison methods based on k-mer counting have been proposed in the literature; for a review, see [19].”
Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences (2011, methodology): “Markers with strong segregation distortion or that appeared un-linked at LOD , 5.0 were excluded.”
CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers (2015, background): “The k -spectrum is a succinct (lossy) representation of x , which allows sequence comparison (see e.g., [18]).”
Mash: fast genome and metagenome distance estimation using MinHash (2015, background): “Similar ‘alignment-free’ methods have a long history in bioinformatics [12, 13].”
The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes (2014, methodology): “Exact alignment methods, often formulated as k-mer matching, can produce high precision results in a fraction of the time required for gapped alignment methods [60-62].”
Alignment-free sequence comparison: benefits, applications, and tools (2017, methodology): “Additionally, because word-based methods operate on vectors, their mathematical elegance allows the employment of more than 40 functions other than the Euclidean distance, such as the Pearson correlation coefficient [38], Manhattan distance, and Google distance [59].”
Fractal MapReduce decomposition of sequence alignment (2012, methodology): “However, better implementations of dynamic programming do not by themselves remove its limited scalability, which has motivated research into a variety of alignment-free methods in the last decade [5-9].”