These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

167 related articles for article (PubMed ID: 31073610)

  • 1. The barcode, UMI, set format and BUStools.
    Melsted P; Ntranos V; Pachter L
    Bioinformatics; 2019 Nov; 35(21):4472-4473. PubMed ID: 31073610
    [TBL] [Abstract][Full Text] [Related]  

  • 2. BUSZ: compressed BUS files.
    Einarsson PH; Melsted P
    Bioinformatics; 2023 May; 39(5):. PubMed ID: 37129540
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.
    You Y; Tian L; Su S; Dong X; Jabbari JS; Hickey PF; Ritchie ME
    Genome Biol; 2021 Dec; 22(1):339. PubMed ID: 34906205
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.
    Edwards N; Dillard C; Prashant NM; Hongyu L; Yang M; Ulianova E; Horvath A
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36448703
    [TBL] [Abstract][Full Text] [Related]  

  • 5. snakePipes: facilitating flexible, scalable and integrative epigenomic analysis.
    Bhardwaj V; Heyne S; Sikora K; Rabbani L; Rauer M; Kilpert F; Richter AS; Ryan DP; Manke T
    Bioinformatics; 2019 Nov; 35(22):4757-4759. PubMed ID: 31134269
    [TBL] [Abstract][Full Text] [Related]  

  • 6. kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.
    Sullivan DK; Min KHJ; Hjörleifsson KE; Luebbert L; Holley G; Moses L; Gustafsson J; Bray NL; Pimentel H; Booeshaghi AS; Melsted P; Pachter L
    bioRxiv; 2024 Jan; ():. PubMed ID: 38045414
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data.
    Srivastava A; Malik L; Smith T; Sudbery I; Patro R
    Genome Biol; 2019 Mar; 20(1):65. PubMed ID: 30917859
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Alignment-free clustering of UMI tagged DNA molecules.
    Orabi B; Erhan E; McConeghy B; Volik SV; Le Bihan S; Bell R; Collins CC; Chauve C; Hach F
    Bioinformatics; 2019 Jun; 35(11):1829-1836. PubMed ID: 30351359
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions.
    Zhu K; Anastassiou D
    Bioinformatics; 2020 Jun; 36(11):3588-3589. PubMed ID: 32108864
    [TBL] [Abstract][Full Text] [Related]  

  • 10. scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions.
    Liu J; Liu X; Ren X; Li G
    Bioinformatics; 2019 Nov; 35(21):4264-4271. PubMed ID: 30951147
    [TBL] [Abstract][Full Text] [Related]  

  • 11. RSEQtools: a modular framework to analyze RNA-Seq data using compact, anonymized data summaries.
    Habegger L; Sboner A; Gianoulis TA; Rozowsky J; Agarwal A; Snyder M; Gerstein M
    Bioinformatics; 2011 Jan; 27(2):281-3. PubMed ID: 21134889
    [TBL] [Abstract][Full Text] [Related]  

  • 12. BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method.
    Sun S; Xu L; Zou Q; Wang G
    Bioinformatics; 2021 Jun; 37(9):1319-1321. PubMed ID: 32976573
    [TBL] [Abstract][Full Text] [Related]  

  • 13. BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq.
    Gustafsson J; Robinson J; Nielsen J; Pachter L
    Genome Biol; 2021 Jun; 22(1):174. PubMed ID: 34103073
    [TBL] [Abstract][Full Text] [Related]  

  • 14. schex avoids overplotting for large single-cell RNA-sequencing datasets.
    Freytag S; Lister R
    Bioinformatics; 2020 Apr; 36(7):2291-2292. PubMed ID: 31794001
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
    Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
    Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Modular, efficient and constant-memory single-cell RNA-seq preprocessing.
    Melsted P; Booeshaghi AS; Liu L; Gao F; Lu L; Min KHJ; da Veiga Beltrame E; Hjörleifsson KE; Gehring J; Pachter L
    Nat Biotechnol; 2021 Jul; 39(7):813-818. PubMed ID: 33795888
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Interpretable factor models of single-cell RNA-seq via variational autoencoders.
    Svensson V; Gayoso A; Yosef N; Pachter L
    Bioinformatics; 2020 Jun; 36(11):3418-3421. PubMed ID: 32176273
    [TBL] [Abstract][Full Text] [Related]  

  • 18. smallWig: parallel compression of RNA-seq WIG files.
    Wang Z; Weissman T; Milenkovic O
    Bioinformatics; 2016 Jan; 32(2):173-80. PubMed ID: 26424856
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning.
    Zhang XF; Ou-Yang L; Yang S; Zhao XM; Hu X; Yan H
    Bioinformatics; 2019 Nov; 35(22):4827-4829. PubMed ID: 31125056
    [TBL] [Abstract][Full Text] [Related]  

  • 20. scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data.
    Wang Z; Hu J; Johnson WE; Campbell JD
    BMC Bioinformatics; 2019 May; 20(1):222. PubMed ID: 31046658
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.