Author

T. Buturla

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 3

Signature Assays, Organisms & Methods

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Research Focus:variant calling· 3 Research Focus:clustering· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Approach:benchmark study· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1

Frequent Co-Authors

S. Kumar· 3 shared S. Miura· 3 shared T. Vu· 3 shared K. Gomez· 2 shared L. A. Huuki· 2 shared J. Choi· 1 shared J. Deng· 1 shared O. Murillo· 1 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 22 mutual co-author links

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3 shared papersCancer Biology

S. Kumar

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3 papers

Recent·Most cited

Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data

S. Miura, T. Vu, J. Deng, T. Buturla, J. Choi, S. Kumar

This study evaluates seven computational methods for inferring clone phylogenies from bulk sequencing data, finding significant variation in their accuracy for mutation order and clone grouping.

bioRxiv · Jul 19, 2019Cancer Biology

Predicting clone genotypes from tumor bulk sequencing of multiple samples

S. Miura, K. Gomez, O. Murillo, L. A. Huuki, T. Vu, T. Buturla, S. Kumar

The paper evaluates nine computational methods for inferring clone genotypes from bulk tumor sequencing data, finding that most perform poorly on datasets with mixed clonal lineages.

bioRxiv · Jun 7, 2018Cancer Biology

Computational enhancement of single-cell sequences for inferring tumor evolution

S. Miura, L. A. Huuki, T. Buturla, T. Vu, K. Gomez, S. Kumar

This paper introduces BEAM, a Bayesian method for improving single-cell tumor sequencing data by leveraging evolutionary information, showing it outperforms existing methods in imputing missing bases and correcting false positives/negatives.

bioRxiv · Jun 7, 2018Cancer Biology