ArticleGenome biology2024
Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Article
- CircZBTB46, a promising therapeutic target in crizotinib resistant ALK-positive T lymphomas.Leukemia · 2026Article
- CircRNAs derived from the tyrosine phosphatase PTPN22 impact chemosensitivity in ALK-positive T-cell lymphomas.Scientific reports · 2026Article
- Acute myeloid leukemia risk stratification in younger and older patients through transcriptomic machine learning models.Scientific reports · 2025Article
- RNALoc-LM: RNA subcellular localization prediction using pre-trained RNA language model.Bioinformatics (Oxford, England) · 2025Article
- Effective requesting method to detect fusion transcripts in chronic myelomonocytic leukemia RNA-seq.NAR genomics and bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.
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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.