ArticleBioinformatics (Oxford, England)2019
SquiggleKit: a toolkit for manipulating nanopore signal data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
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
14 citing papers in PubMed.
- Transcriptomics in the era of long-read sequencing.Nature reviews. Genetics · 2025Review
- Nanopore- and AI-empowered microbial viability inference.GigaScience · 2025Article
- LongReadSum: A fast and flexible quality control and signal summarization tool for long-read sequencing data.Computational and structural biotechnology journal · 2025Article
- Direct RNA sequencing in plants: Practical applications and future perspectives.Plant communications · 2024Review
- Nanopore Current Events Magnifier (nanoCEM): a novel tool for visualizing current events at modification sites of nanopore sequencing.NAR genomics and bioinformatics · 2024Article
- The Development of Plant Genome Sequencing Technology and Its Conservation and Application in Endangered Gymnosperms.Plants (Basel, Switzerland) · 2023Review
- Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Review
- Nanopore sequencing technology, bioinformatics and applications.Nature biotechnology · 2021Review
- The mRNA (New York, N.Y.) · 2021Article
- Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets.BMC genomics · 2021Article
- Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection.Cell reports · 2021Article
- Molecular barcoding of native RNAs using nanopore sequencing and deep learning.Genome research · 2020Article
- Inverted duplicate DNA sequences increase translocation rates through sequencing nanopores resulting in reduced base calling accuracy.Nucleic acids research · 2020Article
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
summaryThe management of raw nanopore sequencing data poses a challenge that must be overcome to facilitate the creation of new bioinformatics algorithms predicated on signal analysis. SquiggleKit is a toolkit for manipulating and interrogating nanopore data that simplifies file handling, data extraction, visualization and signal processing. AVAILABILITY AND IMPLEMENTATION: SquiggleKit is cross platform and freely available from GitHub at (https://github.com/Psy-Fer/SquiggleKit). Detailed documentation can be found at (https://psy-fer.github.io/SquiggleKitDocs/). All tools have been designed to operate in python 2.7+, with minimal additional libraries. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.