Evidence map›Paper›PMID 38065078›Full record

ArticleCell2023

SPLASH: A statistical, reference-free genomic algorithm unifies biological discovery.

Kaitlin Chaung, Tavor Z Baharav, George Henderson, Ivan N Zheludev, Peter L Wang, Julia Salzman

Open access · greenAbstract read
In one paragraph

Article in Cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Article
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  7. Pretraining and the lasso.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026
    Article
  8. High-resolution metagenome assembly for modern long reads with myloasm.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Pseudoassembly of k-mers.bioRxiv : the preprint server for biology · 2025
    Article
  11. Article
  12. OASIS: An interpretable, finite-sample valid alternative to Pearson'sProceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  13. Article
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Kaitlin ChaungDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University, Stanford, CA 94305, USA.
Tavor Z BaharavDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
George HendersonDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University, Stanford, CA 94305, USA.
Ivan N ZheludevDepartment of Biochemistry, Stanford University, Stanford, CA 94305, USA.
Peter L WangDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University, Stanford, CA 94305, USA.
Julia SalzmanDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University, Stanford, CA 94305, USA; Department of Statistics (by courtesy), Stanford University, Stanford, CA 94305, USA; Department of Biology (by courtesy), Stanford University, Stanford, CA 94305, USA. Electronic address: julia.salzman@stanford.edu.
Stanford University · US

Funding

Computational- and experimental- driven discovery of splicing regulation and circRNA functionR35GM139517 · NIGMS · STANFORD UNIVERSITY · PI Julia Salzman · 2021 to 2026
$2.9M
NIGMS NIH HHS R35 GM139517
6 · The paper itself

Abstract

Today's genomics workflows typically require alignment to a reference sequence, which limits discovery. We introduce a unifying paradigm, SPLASH (Statistically Primary aLignment Agnostic Sequence Homing), which directly analyzes raw sequencing data, using a statistical test to detect a signature of regulation: sample-specific sequence variation. SPLASH detects many types of variation and can be efficiently run at scale. We show that SPLASH identifies complex mutation patterns in SARS-CoV-2, discovers regulated RNA isoforms at the single-cell level, detects the vast sequence diversity of adaptive immune receptors, and uncovers biology in non-model organisms undocumented in their reference genomes: geographic and seasonal variation and diatom association in eelgrass, an oceanic plant impacted by climate change, and tissue-specific transcripts in octopus. SPLASH is a unifying approach to genomic analysis that enables expansive discovery without metadata or references.

Indexed as

AlgorithmsGenomicsGenomeHLA AntigensHumansSequence Analysis, RNASingle-Cell AnalysisHLA Antigenscomputational biologygeneticsgenomicsreference-freeRNA-seqsingle-cell RNA-seqsplicingstatistics

Identifiers

PMID38065078
PMCPMC10861363
OpenAlexW4389427686

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.