2 Search Results for "Shao, Mingfu"


Document
Locality-Sensitive Bucketing Functions for the Edit Distance

Authors: Ke Chen and Mingfu Shao

Published in: LIPIcs, Volume 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)


Abstract
Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing k-mer-based bucketing methods have been efficient in processing sequencing data with low error rate, but encounter much reduced sensitivity on data with high error rate. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps. Here we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be (d₁, d₂)-sensitive if any two sequences within an edit distance of d₁ are mapped into at least one shared bucket, and any two sequences with distance at least d₂ are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of (d₁,d₂) and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal. These results provide theoretical foundations for their practical use in analyzing sequences with high error rate while also providing insights for the hardness of designing ungapped LSH functions.

Cite as

Ke Chen and Mingfu Shao. Locality-Sensitive Bucketing Functions for the Edit Distance. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 22:1-22:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{chen_et_al:LIPIcs.WABI.2022.22,
  author =	{Chen, Ke and Shao, Mingfu},
  title =	{{Locality-Sensitive Bucketing Functions for the Edit Distance}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{22:1--22:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-243-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{242},
  editor =	{Boucher, Christina and Rahmann, Sven},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2022.22},
  URN =		{urn:nbn:de:0030-drops-170563},
  doi =		{10.4230/LIPIcs.WABI.2022.22},
  annote =	{Keywords: Locality-sensitive hashing, locality-sensitive bucketing, long reads, embedding}
}
Document
Context-Aware Seeds for Read Mapping

Authors: Hongyi Xin, Mingfu Shao, and Carl Kingsford

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)


Abstract
Motivation: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. Results: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mapping but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 20.3% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver. Availability: https://github.com/Kingsford-Group/CAS_code

Cite as

Hongyi Xin, Mingfu Shao, and Carl Kingsford. Context-Aware Seeds for Read Mapping. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Copy BibTex To Clipboard

@InProceedings{xin_et_al:LIPIcs.WABI.2019.15,
  author =	{Xin, Hongyi and Shao, Mingfu and Kingsford, Carl},
  title =	{{Context-Aware Seeds for Read Mapping}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{15:1--15:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.15},
  URN =		{urn:nbn:de:0030-drops-110452},
  doi =		{10.4230/LIPIcs.WABI.2019.15},
  annote =	{Keywords: Read Mapping, Seed and Extend, Edit Distance, Suffix Trie}
}
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