ArticleScientific reports2019
Improving in-silico normalization using read weights.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Eupentacta fraudatrix: the first chromosome-level holothurian genome with extraordinarily high heterozygosity.Scientific data · 2026Article
- Morphogenesis, starvation, and light responses in a mushroom-forming fungus revealed by long-read sequencing and extensive expression profiling.Cell genomics · 2025Article
- VenomCap: An exon-capture probe set for the targeted sequencing of snake venom genes.Molecular ecology resources · 2024Article
- De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide.Frontiers in zoology · 2024Review
- The long and short of it: benchmarking viromics using Illumina, Nanopore and PacBio sequencing technologies.Microbial genomics · 2024Article
- Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2.Nature communications · 2023Article
- Antimicrobial Resistance and Genetic Diversity ofPathogens (Basel, Switzerland) · 2022Article
- In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes.BMC genomics · 2022Article
- A simple guide to de novo transcriptome assembly and annotation.Briefings in bioinformatics · 2022Review
- Bacteriophages Roam the Wheat Phyllosphere.Viruses · 2022Article
- Design of Hydrogel Silk-Based Microarrays and Molecular Beacons for Reagentless Point-of-Care Diagnostics.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Specialized de novo assemblers for diverse datatypes have been developed and are in widespread use for the analyses of single-cell genomics, metagenomics and RNA-seq data. However, assembly of large sequencing datasets produced by modern technologies is challenging and computationally intensive. In-silico read normalization has been suggested as a computational strategy to reduce redundancy in read datasets, which leads to significant speedups and memory savings of assembly pipelines. Previously, we presented a set multi-cover optimization based approach, ORNA, where reads are reduced without losing important k-mer connectivity information, as used in assembly graphs. Here we propose extensions to ORNA, named ORNA-Q and ORNA-K, which consider a weighted set multi-cover optimization formulation for the in-silico read normalization problem. These novel formulations make use of the base quality scores obtained from sequencers (ORNA-Q) or k-mer abundances of reads (ORNA-K) to improve normalization further. We devise efficient heuristic algorithms for solving both formulations. In applications to human RNA-seq data, ORNA-Q and ORNA-K are shown to assemble more or equally many full length transcripts compared to other normalization methods at similar or higher read reduction values. The algorithm is implemented under the latest version of ORNA (v2.0, https://github.com/SchulzLab/ORNA ).
Identifiers
What OpenQuestion holds
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.