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Sini Junttila

Showing results (1-10 of 42) with videos related to

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BMC Genomics|November 1, 2012
Characterization of a transcriptome from a non-model organism, Cladonia rangiferina, the grey reindeer lichen, using high-throughput next generation sequencing and EST sequence dataSini Junttila, Stephen Rudd
BMC Research Notes|October 7, 2009
Optimization and comparison of different methods for RNA isolation for cDNA library construction from the reindeer lichen Cladonia rangiferinaSini Junttila, Kean-Jin Lim, Stephen Rudd
Briefings in Bioinformatics|July 26, 2022
Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq dataSini Junttila, Johannes Smolander, Laura L Elo
Bioinformatics (Oxford, England)|August 25, 2023
Cell-connectivity-guided trajectory inference from single-cell dataJohannes Smolander, Sini Junttila, Laura L Elo
BMC Genomics|December 12, 2013
Whole transcriptome characterization of the effects of dehydration and rehydration on Cladonia rangiferina, the grey reindeer lichenSini Junttila, Asta Laiho, Attila Gyenesei, et al.
European Journal of Immunology|June 16, 2011
Alternate pathways for Bcl6-mediated regulation of B cell to plasma cell differentiationJukka Alinikula, Kalle-Pekka Nera, Sini Junttila, et al.
Bioinformatics (Oxford, England)|November 5, 2020
ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq dataJohannes Smolander, Sini Junttila, Mikko S Venäläinen, et al.
Immunology and Cell Biology|June 29, 2025
Sketching T cell atlases in the single-cell era: challenges and recommendationsItana Bojović, António Gg Sousa, Sini Junttila, et al.
Bioinformatics (Oxford, England)|December 10, 2021
scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq dataJohannes Smolander, Sini Junttila, Mikko S Venäläinen, et al.
Nucleic Acids Research|November 16, 2025
Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integrationAntónio G G Sousa, Johannes Smolander, Sini Junttila, et al.
Pageof 5

Showing results (1-10 of 42) with videos related to

Sort By:
Pageof 5
BMC Genomics|November 1, 2012
Characterization of a transcriptome from a non-model organism, Cladonia rangiferina, the grey reindeer lichen, using high-throughput next generation sequencing and EST sequence dataSini Junttila, Stephen Rudd
BMC Research Notes|October 7, 2009
Optimization and comparison of different methods for RNA isolation for cDNA library construction from the reindeer lichen Cladonia rangiferinaSini Junttila, Kean-Jin Lim, Stephen Rudd
Briefings in Bioinformatics|July 26, 2022
Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq dataSini Junttila, Johannes Smolander, Laura L Elo
Bioinformatics (Oxford, England)|August 25, 2023
Cell-connectivity-guided trajectory inference from single-cell dataJohannes Smolander, Sini Junttila, Laura L Elo
BMC Genomics|December 12, 2013
Whole transcriptome characterization of the effects of dehydration and rehydration on Cladonia rangiferina, the grey reindeer lichenSini Junttila, Asta Laiho, Attila Gyenesei, et al.
European Journal of Immunology|June 16, 2011
Alternate pathways for Bcl6-mediated regulation of B cell to plasma cell differentiationJukka Alinikula, Kalle-Pekka Nera, Sini Junttila, et al.
Bioinformatics (Oxford, England)|November 5, 2020
ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq dataJohannes Smolander, Sini Junttila, Mikko S Venäläinen, et al.
Immunology and Cell Biology|June 29, 2025
Sketching T cell atlases in the single-cell era: challenges and recommendationsItana Bojović, António Gg Sousa, Sini Junttila, et al.
Bioinformatics (Oxford, England)|December 10, 2021
scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq dataJohannes Smolander, Sini Junttila, Mikko S Venäläinen, et al.
Nucleic Acids Research|November 16, 2025
Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integrationAntónio G G Sousa, Johannes Smolander, Sini Junttila, et al.
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