Alvis Brāzma

scDenorm reverts normalized single-cell omics data to raw counts to improve integration and reduce biases in downstream analysis.

This study challenges the ortholog conjecture by analyzing scRNA-seq data across species, revealing gene-specific patterns of expression conservation and divergence.

This paper evaluates 31 cell-type deconvolution methods across human and mouse tissues, identifies regression-based methods as effective, and introduces the CATD pipeline for reproducible analysis.

The paper compares gene expression in melanocyte regeneration and melanoma to identify signaling pathways that promote regeneration and suppress cancer traits.

This study benchmarks cross-species integration methods for single-cell RNA-seq data, finding that scANVI, scVI, and SeuratV4 balance species mixing and biology conservation.

The paper introduces a simulation pipeline to generate structurally variable synthetic NPCs for benchmarking image analysis methods and understanding NPC variability in SMLM data.

The BioImage Archive provides a centralized repository for biological imaging data to enhance reproducibility, reuse, and development of image analysis tools.

The paper performs a meta-analysis of single-cell RNA data from 9 studies to identify consistent immune response patterns in COVID-19 patients, finding consistent upregulation of type I interferon and downregulation of mitochondrial genes.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

This study compiles a proteomics resource for cancer cell lines and tumour samples, finding that protein abundance in cell lines often reflects tumours but fails to capture subtype-specific variations.

The paper introduces an interactive scRNA-Seq analysis environment based on Galaxy, integrating ~70 functions for scalable and accessible single-cell data analysis.

This paper develops a gene expression signature, Cam_121, to predict long-term outcomes in primary melanoma patients, showing improved accuracy compared to clinical and existing prognostic models.

The paper analyzes transcriptomics data availability and reusability between microarray and RNA-seq experiments published between 2009 and 2013, and provides recommendations to improve RNA-seq data sharing.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

This study identifies alternative promoters as key regulators in the cancer transcriptome using RNA-Seq data from 1,188 cancer samples.

This study identifies two molecular subgroups of anaplastic meningioma with distinct survival outcomes and links them to specific genetic mutations.

The paper compares genome assemblies of Mus caroli and Mus pahari with Mus musculus and Rattus norvegicus to reveal lineage-specific evolutionary mechanisms, particularly repeat-associated processes.

The paper analyzes RNA alterations across 27 cancer types using whole-genome sequencing data, identifying gene expression associations with DNA variants and structural fusions.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.

The paper systematically characterizes gene fusions in cancer using RNA and whole genome DNA sequencing data from 1188 donors across 27 cancer types, identifying 3297 high-confidence fusions and revealing novel mechanisms.

The paper introduces IDR, a platform for integrating and disseminating biological image data, enabling analysis of gene networks and functional interactions through linked data and computational resources.