Title | Efficient detection and assembly of non-reference DNA sequences with synthetic long reads. |
Publication Type | Journal Article |
Year of Publication | 2022 |
Authors | Meleshko D, Yang R, Marks P, Williams S, Hajirasouliha I |
Journal | Nucleic Acids Res |
Volume | 50 |
Issue | 18 |
Pagination | e108 |
Date Published | 2022 Oct 14 |
ISSN | 1362-4962 |
Keywords | Algorithms, Base Sequence, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA |
Abstract | Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion's share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact. |
DOI | 10.1093/nar/gkac653 |
Alternate Journal | Nucleic Acids Res |
PubMed ID | 35924489 |
PubMed Central ID | PMC9561269 |
Grant List | R35 GM138152 / GM / NIGMS NIH HHS / United States |