Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists

Chappell, Timothy; Geva, Shlomo; Zuccon, Guido

doi:10.1007/978-3-319-16354-3_16

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…Inverted Signature Slice Lists (ISSL) can be used to rapidly perform approximate nearest neighbourhood searches in collections of locality-sensitive signatures [5]. By using fixed-length signatures as search-keys, items in a neighbourhood can be found in constant time.…”

Section: Off-target Scoring Using Inverted Signature Slice Listsmentioning

confidence: 99%

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Faster and better CRISPR guide RNA design with the Crackling method

Bradford

Chappell

Perrin

2020

Preprint

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The design of CRISPR-Cas9 guide RNAs is not trivial, and is a computationally demanding task. Design tools need to identify target sequences that will maximise the likelihood of obtaining the desired cut, whilst minimising off-target risk. There is a need for a tool that can meet both objectives while remaining practical to use on large genomes.Here, we present Crackling, a new method that is more suitable for meeting these objectives. We test its performance on 12 genomes and on data from validation studies. Crackling maximises guide efficiency by combining multiple scoring approaches. On experimental data, the guides it selects are better than those selected by others. It also incorporates Inverted Signature Slice Lists (ISSL) for faster off-target scoring. ISSL provides a gain of an order of magnitude in speed, while preserving the same level of accuracy. Overall, this makes Crackling a faster and better method to design guide RNAs at scale.Crackling is available at https://github.com/bmds-lab/Crackling under the Berkeley Software Distribution (BSD) 3-Clause license.

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Section: Off-target Scoring Using Inverted Signature Slice Listsmentioning

confidence: 99%

“…Filtering based on features such as: GC content, presence of TTTT, gRNA secondary structure [18] sgRNA Scorer 2.0 Scoring using machine learning model, trained on [6] [7] CHOPCHOP Flag indicating position 20 containing guanine [17] A B (3,5,12,20) s (1,9,16,20) s (1,2,8,9) s (1,2,3,4) s (1,2,19,20)…”

Section: Toolmentioning

confidence: 99%

Faster and better CRISPR guide RNA design with the Crackling method

Bradford

Chappell

Perrin

2020

Preprint

Self Cite

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“…Chappell et al proposed a system for approximate nearest-neighbour search of bit strings [7] aimed at locating such nearest neighbour hashes by creating inverted lists of smaller bit string "slices", similar to the divisions done in MIH. However, for larger collections of longer bit strings, such as binary descriptors, the method does not scale due to each slice list increasing in size linearly.…”

Section: Indexing Bit Stringsmentioning

confidence: 99%

“…This applies to both the sequential and the indexed query implementations. Chappell et al [7] performed all searches in memory, which complicates direct comparison of the two implementations.…”

Section: Hamming Tree Search Accelerationmentioning

confidence: 99%

An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying

van Blokland,

Theoharis

2020

Preprint

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A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.

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“…The patents in the k-nearest neighbourhood of the (query) target patent are considered to determine the target's classification code; this is obtained by weighting the classification codes of the patents in the neighbourhood. This approach guarantees extreme efficiency, specifically because of its capacity to scale to very large collections given that indexing is linear with the size of the collection (like inverted file search engines) and searching time increases linearly at a lower rate than the increase in collection size [1].…”

Section: Introductionmentioning

confidence: 99%

A Signature Approach to Patent Classification

Seneviratne

Geva

Zuccon

et al. 2015

Information Retrieval Technology

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Abstract. We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that, intuitively, document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.

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Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists

Cited by 11 publications

References 15 publications

Faster and better CRISPR guide RNA design with the Crackling method

Faster and better CRISPR guide RNA design with the Crackling method

An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying

A Signature Approach to Patent Classification

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