Englander Institute for Precision Medicine

Ariadne: synthetic long read deconvolution using assembly graphs.

TitleAriadne: synthetic long read deconvolution using assembly graphs.
Publication TypeJournal Article
Year of Publication2023
AuthorsMak L, Meleshko D, Danko DC, Barakzai WN, Maharjan S, Belchikov N, Hajirasouliha I
JournalGenome Biol
Volume24
Issue1
Pagination197
Date Published2023 Aug 28
ISSN1474-760X
KeywordsAlgorithms, Genomics, Metagenome, Pentaerythritol Tetranitrate
Abstract

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

DOI10.1186/s13059-023-03033-5
Alternate JournalGenome Biol
PubMed ID37641111
PubMed Central IDPMC10463629
Grant ListR35 GM138152 / GM / NIGMS NIH HHS / United States
T32 GM083937 / GM / NIGMS NIH HHS / United States

Weill Cornell Medicine Englander Institute for Precision Medicine 413 E 69th Street
Belfer Research Building
New York, NY 10021