A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases

Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long-read mapping algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade de...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computational biology Vol. 25; no. 7; p. 766
Main Authors: Jain, Chirag, Dilthey, Alexander, Koren, Sergey, Aluru, Srinivas, Phillippy, Adam M
Format: Journal Article
Language:English
Published: United States 01.07.2018
Subjects:
Algorithms
Databases, Factual
Genome, Human - genetics
High-Throughput Nucleotide Sequencing - methods
Humans
Sequence Alignment - methods
Sequence Analysis, DNA
Software
Jaccard
minimizers
sketching
winnowing
MinHash
long-read mapping
ISSN:1557-8666, 1557-8666
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Emerging single-molecule sequencing technologies from Pacific Biosciences and Oxford Nanopore have revived interest in long-read mapping algorithms. Alignment-based seed-and-extend methods demonstrate good accuracy, but face limited scalability, while faster alignment-free methods typically trade decreased precision for efficiency. In this article, we combine a fast approximate read mapping algorithm based on minimizers with a novel MinHash identity estimation technique to achieve both scalability and precision. In contrast to prior methods, we develop a mathematical framework that defines the types of mapping targets we uncover, establish probabilistic estimates of p-value and sensitivity, and demonstrate tolerance for alignment error rates up to 20%. With this framework, our algorithm automatically adapts to different minimum length and identity requirements and provides both positional and identity estimates for each mapping reported. For mapping human PacBio reads to the hg38 reference, our method is 290 × faster than Burrows-Wheeler Aligner-MEM with a lower memory footprint and recall rate of 96%. We further demonstrate the scalability of our method by mapping noisy PacBio reads (each ≥5 kbp in length) to the complete NCBI RefSeq database containing 838 Gbp of sequence and >60,000 genomes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1557-8666
1557-8666
DOI:10.1089/cmb.2018.0036