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HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes

Proceedings of the National Academy of Sciences(2020)10.1073/pnas.2010738117Source: DataRank Database

HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes is a research paper published in Proceedings of the National Academy of Sciences (2020). On theSindex it has a DataRank of 1.6. It has been cited 44 times, with 35 citing works in its 1-hop citation network.

N/A

1.6DataRank · unranked

Scored on demand from live citation data

Open Access44 citations · 35 citing works in its network

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Linked data & code

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (10 grants)Impact (FWCI 1.94)OA: bronzeTopics (71)Linked Datasets (4)DataCite Reuse (4)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 36%Citation Network 64%

Base Score Contribution

0.571

From this paper's citation signal

Citation Network Contribution

1.0

From 32 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 35 citers.

Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering
iScience202116 citations23 referencesContributes 0.123
scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling
Bioinformatics202131 citations47 referencesContributes 0.074
Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements
Nature Communications202442 citations57 referencesContributes 0.066
Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing
Nature Genetics202524 citations52 referencesContributes 0.062
Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM
Nature Computational Science202228 citations59 referencesContributes 0.057

Authors (18)

Benjamin R. Doughty ORCID,Vidya Subramanian,Philine Guckelberger ORCID,Qingbo S. Wang ORCID,Linlin M. Chen ORCID

Live enrichment

PubMed:33376219 PMC:PMC7776864 OpenAlex:https://openalex.org/W3113630028

HHS | National Institutes of Health

Grant: 1DP5OD024583

Broad Institute

Grant: -

HHS | NIH | National Human Genome Research Institute

Grant: 1K99HG009917

HHS | NIH | National Human Genome Research Institute

Grant: R00HG009917

Lucile Packard Children's Hospital

Grant: BASE Research Initiative

National Science Foundation

Grant: 1122374

Graduate Research Fellowship Program

NIH HHS

Grant: DP5 OD024583

NHGRI NIH HHS

Grant: K99 HG009917

National Institutes of Health

Grant: 1DP5OD024583-01

Characterizing glioma heterogeneity with novel multiplexed nanoscale imaging technologies

National Institutes of Health

Grant: 5R00HG009917-04

Systematic mapping and prediction of gene-enhancer connections

FWCI

1.94

Citation Percentile

0.9%

Citation Trend

2020

2021

2022

2023

2024

2025

2026

bronzeLicense: https://www.pnas.org/site/aboutpnas/licenses.xhtml

journalhttps://www.pnas.org/doi/pdf/10.1073/pnas.2010738117

publisherhttps://www.pnas.org/doi/pdf/10.1073/pnas.2010738117

publisherhttp://www.pnas.org/syndication/doi/10.1073/pnas.2010738117

publisherhttps://pnas.org/doi/pdf/10.1073/pnas.2010738117

journalhttps://doi.org/10.1073/pnas.2010738117

Fields of Study

Single-cell and spatial transcriptomicsCRISPR and Genetic EngineeringAdvanced biosensing and bioanalysis techniques0206 medical engineering02 engineering and technologyAnimalsCRISPR-Cas SystemsDNA ProbesGene ExpressionHigh-Throughput Nucleotide SequencingHumansIntronsK562 CellsKidneyMiceNucleic Acid HybridizationPolyadenylationRNARNA, MessengerSingle-Cell AnalysisTHP-1 CellsTime Factors

MeSH Terms

THP-1 CellsAnimalsHumansIntronsKidneyNucleic Acid HybridizationRNARNA, MessengerTime FactorsDNA ProbesGene ExpressionK562 CellsPolyadenylationMiceSingle-Cell AnalysisHigh-Throughput Nucleotide SequencingCRISPR-Cas Systems

Keywords

Computational biologyDNA sequencingSequencing by hybridizationDNARNA-SeqBiologyComputer scienceGeneticsDNA sequencerGeneGene expressionTranscriptomeGene regulationSingle cellGenomicsEnhancersTime FactorsTHP-1 CellsHigh-Throughput Nucleotide SequencingNucleic Acid HybridizationKidneyPolyadenylationIntronsMiceAnimalsHumansRNARNA, MessengerCRISPR-Cas SystemsSingle-Cell AnalysisDNA ProbesK562 Cells

Additional file 1 of Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

10.6084/m9.figshare.16986768.v1figshare

Additional file 1 of Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

10.6084/m9.figshare.16986768figshare

Additional file 2 of Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

10.6084/m9.figshare.16986771.v1figshare

Additional file 2 of Optimization of library preparation based on SMART for ultralow RNA-seq in mice brain tissues

10.6084/m9.figshare.16986771figshare