24 research outputs found
Assembly, quantification, and downstream analysis for high trhoughput sequencing data
Next Generation Sequencing is a set of relatively recent but already well-established technologies with a wide range of applications in life sciences. Despite the fact that they are constantly being improved, multiple challenging problems still exist in the analysis of high throughput sequencing data. In particular, genome assembly still suffers from inability of technologies to overcome issues related to such structural properties of genomes as single nucleotide polymorphisms and repeats, not even mentioning the drawbacks of technologies themselves like sequencing errors which also hinder the reconstruction of the true reference genomes. Other types of issues arise in transcriptome quantification and differential gene expression analysis. Processing millions of reads requires sophisticated algorithms which are able to compute gene expression with high precision and in reasonable amount of time. Following downstream analysis, the utmost computational task is to infer the activity of biological pathways (e.g., metabolic). With many overlapping pathways challenge is to infer the role of each gene in activity of a given pathway. Assignment products of a gene to a wrong pathway may result in misleading differential activity analysis, and thus, wrong scientific conclusions. In this dissertation I present several algorithmic solutions to some of the enumerated problems above. In particular, I designed scaffolding algorithm for genome assembly and created new tools for differential gene and biological pathways expression analysis
Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.
BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching
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BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.
Single-cell RNA-sequencing (scRNA-seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly generated owing to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-seq integration methods are based on the mutual nearest neighbor paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships
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Rrp6 Moonlights in an RNA Exosome-Independent Manner to Promote Cell Survival and Gene Expression during Stress.
The nuclear RNA exosome is essential for RNA processing and degradation. Here, we show that the exosome nuclear-specific subunit Rrp6p promotes cell survival during heat stress through the cell wall integrity (CWI) pathway, independently of its catalytic activity or association with the core exosome. Rrp6p exhibits negative genetic interactions with the Slt2/Mpk1p or Paf1p elongation factors required for expression of CWI genes during stress. Overexpression of Rrp6p or of its catalytically inactive or exosome-independent mutants can partially rescue the growth defect of the mpk1Δ mutant and stimulates expression of the Mpk1p target gene FKS2. The rrp6Δ and mpk1Δ mutants show similarities in deficient expression of CWI genes during heat shock, and overexpression of the CWI gene HSP150 can rescue the stress-induced lethality of the mpk1Δrp6Δ mutant. These results demonstrate that Rrp6p moonlights independently from the exosome to ensure proper expression of CWI genes and to promote cell survival during stress
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Rrp6 Moonlights in an RNA Exosome-Independent Manner to Promote Cell Survival and Gene Expression during Stress.
The nuclear RNA exosome is essential for RNA processing and degradation. Here, we show that the exosome nuclear-specific subunit Rrp6p promotes cell survival during heat stress through the cell wall integrity (CWI) pathway, independently of its catalytic activity or association with the core exosome. Rrp6p exhibits negative genetic interactions with the Slt2/Mpk1p or Paf1p elongation factors required for expression of CWI genes during stress. Overexpression of Rrp6p or of its catalytically inactive or exosome-independent mutants can partially rescue the growth defect of the mpk1Δ mutant and stimulates expression of the Mpk1p target gene FKS2. The rrp6Δ and mpk1Δ mutants show similarities in deficient expression of CWI genes during heat shock, and overexpression of the CWI gene HSP150 can rescue the stress-induced lethality of the mpk1Δrp6Δ mutant. These results demonstrate that Rrp6p moonlights independently from the exosome to ensure proper expression of CWI genes and to promote cell survival during stress