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Cazaux, B.

Publications and source records attributed to Cazaux, B..

2 recordsLinked to original sources

A survey of mapping algorithms in the long-reads era

It has been ten years since the first publication of a method dedicated entirely to mapping third-generation sequencing long-reads. The unprecedented characteristics of this new type of sequencing data created a shift, and methods moved on from the seed-and-extend framework previously used for short reads to a seed-and-chain framework due to the abundance of seeds in each read. As a result, the main novelties in proposed long-read mapping algorithms are typically based on alternative seed constructs or chaining formulations. Dozens of tools now exist, whose heuristics have considerably evolved with time. The rapid progress of the field, synchronized with the frequent improvements of data, does not make the literature and implementations easy to keep up with. Therefore, in this survey article, we provide an overview of existing mapping methods for long reads with accessible insights into methods. Since mapping is also very driven by the implementations themselves, we join an original visualization tool to understand the parameter settings (http://bcazaux.polytech-lille.net/Minimap2/) for the chaining part.

bioinformatics↗

Toward optimal fingerprint indexing for large scale genomics

MotivationTo keep up with the scale of genomic databases, several methods rely on local sensitive hashing methods to efficiently find potential matches within large genome collections. Existing solutions rely on Minhash or Hyperloglog fingerprints and require reading the whole index to perform a query. Such solutions can not be considered scalable with the growing amount of documents to index. ResultsWe present NIQKI, a novel structure with well-designed fingerprints that lead to theoretical and practical query time improvements, outperforming state-of-the-art by orders of magnitude. Our contribution is threefold. First, we generalize the concept of Hyperminhash fingerprints in (h,m)-HMH fingerprints that can be tuned to present the lowest false positive rate given the expected sub-sampling applied. Second, we provide a structure able to index any kind of fingerprints based on inverted indexes that provide optimal queries, namely linear with the size of the output. Third, we implemented these approaches in a tool dubbed NIQKI that can index and calculate pairwise distances for over one million bacterial genomes from GenBank in a few days on a small cluster. We show that our approach can be orders of magnitude faster than state-of-the-art with comparable precision. We believe this approach can lead to tremendous improvements, allowing fast queries and scaling on extensive genomic databases. Availability and implementationWe wrote the NIQKI index as an open-source C++ library under the AGPL3 license available at https://github.com/Malfoy/NIQKI. It is designed as a user-friendly tool and comes along with usage samples. 2012 ACM Subject ClassificationApplied computing [->] Bioinformatics Digital Object Identifier10.4230/LIPIcs.WABI.2022.25

bioinformatics↗