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1  Streptococcus pneumoniae genomes for shared suffixes.
2 omic distribution of disease extent, but the suffixes A or B for symptoms will only be included for H
3 nt an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently
4 es, low frequency trigrams, indicator terms, suffixes and part-of-speech information.
5             The algorithm leverages a sparse suffix array (SA), a text index that stores every K-th p
6 ich uses a learned data model to augment the suffix array and enable faster queries.
7 r each oligonucleotide using the constructed suffix array and local alignment followed by thermodynam
8 computational efficiency is achieved using a suffix array data structure that allows for fast queryin
9 dentify alignment seeds, due to its use of a suffix array data structure.
10    Here a theory of haplotype matching using suffix array ideas is developed, which should scale too
11                                          The suffix array is a widely used data structure to accelera
12             We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chime
13 s an efficient implementation of the partial suffix array to detect read overlaps with different seed
14 than a factor of two while adding <1% to the suffix array's memory footprint.
15                     Here, we show how to use suffix array-based methods that have formed the basis of
16              In this work we present GeDi, a suffix array-based somatic single nucleotide variant (SN
17 utation of minimal absent words based on the suffix array.
18 e desirable features of the lookup table and suffix array.
19                           By designing novel suffix-array based SNV calling methods, we have develope
20  a replacement and improvement over previous suffix-array based SNV calling methods.
21 maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching
22 ng for faster read overlap detection, sparse suffix arrays for creating smaller indexes, and Bloom fi
23                    Due to their flexibility, suffix arrays have been the data structure of choice for
24    Over the last few years, methods based on suffix arrays using the Burrows-Wheeler Transform have b
25 ent transcriptome indexing based on modified suffix arrays, EMSAR minimizes the use of CPU time and m
26 he same problem based on the construction of suffix arrays.
27  length n on a fixed-sized alphabet based on suffix arrays.
28 ntegrated seed-and-extend framework based on suffix arrays.
29 al absent words based on the construction of suffix arrays; and second, we provide the respective imp
30 -sized alphabet based on the construction of suffix automata.
31 he last two bases of each window were read ('suffix' codons).
32 lly complex words (words made up of base and suffix, e.g., agree+able) typically reflects frequency o
33 nic ancestor, Modern English uses the dental suffix, '-ed', to signify past tense.
34  found overlaps between reads using a prefix/suffix hash table.
35 bitors (inhibitors are denoted herein by the suffix "i") when combined with MSLN-TTC.
36                         A supplemental "(S)" suffix indicates use of stenting.
37 thm as long as the space between the indexed suffixes is not greater than a minimum length of a MEM.
38 eptide allows one to separate the prefix and suffix ladders, to greatly reduce the number of noise pe
39 rigin from the larger gastric bezoar and the suffix 'lets' conveys they are smaller in size.
40 e introduce suffix skips to traverse several suffix links simultaneously and use them to efficiently
41 eads, in order to find the longest prefix or suffix of the read that has a match in the target sequen
42 max)(x, y), is the longest string which is a suffix of x and a prefix of y.
43                                          The suffix -pontin means bridge and denotes the role of OPN
44  in the UStags (i.e., the UStags' prefix and suffix sequences and the UStags themselves) were used to
45                                 We introduce suffix skips to traverse several suffix links simultaneo
46 orphemes: a verb stem ('saddaqa'), a subject suffix ('-t-'), and a direct object pronoun ('-hu').
47                           We then search the suffix tree against the spectrum graph for candidate pep
48 parison with other techniques, probabilistic suffix tree and correlation mining techniques produce th
49 hat models haplotype sharing with a modified suffix tree data structure and computes expression group
50  speaking, we employed an approach combining suffix tree data structure and spectrum graph.
51                    We describe an algorithm, Suffix Tree EM for Motif Elicitation (STEME), that appro
52                                            A suffix tree is a data structure that can efficiently ind
53                                          The suffix tree is used to preprocess the protein sequence d
54 T or FASTA, while requiring less memory than suffix tree methods.
55 lengths of the reads through a well-designed suffix tree structure.
56 ts efficiency, Slicembler uses a generalized suffix tree to identify these frequent contigs (or fract
57    In this version, we replaced the original suffix tree with Burrows-Wheeler Transform and introduce
58   Using an efficient data structure called a suffix tree, the system is able to rapidly align sequenc
59                                We describe a suffix-tree algorithm that can align the entire genome s
60 plore deep topological relationships between suffix trees and compressed de Bruijn graphs and introdu
61  4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters b
62  knowledge, this is the first application of suffix trees to EM.
63 citation (STEME), that approximates EM using suffix trees.
64  for de novo transcriptome assembly based on suffix trees.