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Bioinformatics (Oxford, England)
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October 28, 2017
Quark enables semi-reference-based compression of RNA-seq data
Hirak Sarkar, Rob Patro
Bioinformatics (Oxford, England)
|
September 13, 2019
Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level
Hirak Sarkar, Avi Srivastava, Rob Patro
Biorxiv : the Preprint Server for Biology
|
March 3, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
Hongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Genome Research
|
November 17, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
Hongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Bioinformatics (Oxford, England)
|
July 14, 2020
A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification
Avi Srivastava, Laraib Malik, Hirak Sarkar, et al.
Bioinformatics (Oxford, England)
|
June 17, 2016
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes
Avi Srivastava, Hirak Sarkar, Nitish Gupta, et al.
Bioinformatics (Oxford, England)
|
June 29, 2018
A space and time-efficient index for the compacted colored de Bruijn graph
Fatemeh Almodaresi, Hirak Sarkar, Avi Srivastava, et al.
Bioinformatics (Oxford, England)
|
June 28, 2024
A count-based model for delineating cell-cell interactions in spatial transcriptomics data
Hirak Sarkar, Uthsav Chitra, Julian Gold, et al.
Genes & Development
|
November 4, 2024
Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges
Hirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, et al.
Nature Methods
|
March 12, 2022
Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data
Dongze He, Mohsen Zakeri, Hirak Sarkar, et al.
Page
of 3
Search research articles
Search
Showing results (1-10 of 27) with videos related to
Sort By:
Page
of 3
Bioinformatics (Oxford, England)
|
October 28, 2017
Quark enables semi-reference-based compression of RNA-seq data
Hirak Sarkar, Rob Patro
Bioinformatics (Oxford, England)
|
September 13, 2019
Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level
Hirak Sarkar, Avi Srivastava, Rob Patro
Biorxiv : the Preprint Server for Biology
|
March 3, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
Hongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Genome Research
|
November 17, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
Hongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Bioinformatics (Oxford, England)
|
July 14, 2020
A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification
Avi Srivastava, Laraib Malik, Hirak Sarkar, et al.
Bioinformatics (Oxford, England)
|
June 17, 2016
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes
Avi Srivastava, Hirak Sarkar, Nitish Gupta, et al.
Bioinformatics (Oxford, England)
|
June 29, 2018
A space and time-efficient index for the compacted colored de Bruijn graph
Fatemeh Almodaresi, Hirak Sarkar, Avi Srivastava, et al.
Bioinformatics (Oxford, England)
|
June 28, 2024
A count-based model for delineating cell-cell interactions in spatial transcriptomics data
Hirak Sarkar, Uthsav Chitra, Julian Gold, et al.
Genes & Development
|
November 4, 2024
Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges
Hirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, et al.
Nature Methods
|
March 12, 2022
Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data
Dongze He, Mohsen Zakeri, Hirak Sarkar, et al.
Page
of 3