ArticleGenome biology2023
Ariadne: synthetic long read deconvolution using assembly graphs.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- TELLBASE: a novel tool of TELL-seq barcode-assisted scaffold assembler for bacterial genomes.Briefings in bioinformatics · 2025Article
- Anchorage accurately assembles anchor-flanked synthetic long reads.Algorithms for molecular biology : AMB · 2025Article
- The Bioinformatic Applications of Hi-C and Linked Reads.Genomics, proteomics & bioinformatics · 2024Review
- AsmMix: an efficient haplotype-resolved hybridFrontiers in genetics · 2024Article
- Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.