Serghei Mangul

The paper benchmarks discrepancies across microbial genome reference databases, finding high consistency in viral genomes but significant variability in fungal genomes.

This paper introduces a hybrid training model to enhance bioinformatics capacity globally, demonstrating its effectiveness through an Eastern European workshop series.

This study shows that better software development practices for RNA-seq tools lead to more citations, as evidenced by a strong statistical association between development quality and citation counts.

This study evaluates the reproducibility of eight read alignment tools using synthetic replicates, finding significant variability in their performance and impact on downstream variant calling.

This study analyzes genetic variation in Saudi Arabians, identifying sub-clusters and patterns influenced by demographic and cultural factors, and finds enrichment of deleterious alleles in more inbred sub-populations.

The paper uses unmapped reads from whole-exome sequencing data in the UK BioBank to construct features and identify 21 novel genetic loci associated with immune-related traits.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

VISTA improves structural variant detection by integrating multiple callers with a novel filtering algorithm, achieving high accuracy across various SV types and genomic contexts.

This study benchmarks large language models in bioinformatics tasks, finding GPT-4 leads in most areas while Gemini excels in math and LLaMA struggles with code generation.

This study benchmarks 12 HLA callers on RNA-seq data across 682 samples to evaluate accuracy, computational cost, and performance differences by ancestry.

CAMP provides a modular metagenomics analysis system that enables scalable, extensible workflows with integrated data visualization and reporting.

The paper develops a computational framework to detect emerging viral variants with altered phenotypes by analyzing epistatic networks, achieving early detection of known SARS-CoV-2 strains.

This paper meta-analyzes single-cell method benchmarks to identify challenges in extensibility, interoperability, and reproducibility.

pyTCR is a computational notebook platform for TCR-Seq analysis that provides flexibility, transparency, and user-friendliness for researchers with limited computational skills.

TAMPA provides an interpretable visualization tool for metagenomics taxonomic profiles, enabling better biological insights and tool selection.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

This study assesses metadata completeness in public omics studies, finding that most important phenotypes are often omitted, with only 11.5% of studies fully sharing all phenotypes.

This study evaluates ten deconvolution tools on 4,000 clinical and synthetic samples, finding EPIC performs best on Framingham data and HPCA references are generally accurate.

This paper introduces CliqueSNV, a method to accurately identify minority viral haplotypes from NGS data by reducing sequencing noise, achieving detection at frequencies as low as 0.1%.

This paper introduces SBT, a software platform that extracts omics features from clinical tumor sequencing data, identifying prognostic biomarkers in lung, breast, and colorectal cancers.

The paper investigates the association of rare loss-of-function variants in type I IFN immunity genes with severe COVID-19 and finds no significant association.

GEDIT estimates cell type abundances from gene expression data, outperforming existing tools across multiple platforms and tissue types.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

Metalign improves metagenomic profiling by using containment min hash for efficient alignment-based analysis, achieving better results than existing methods on benchmark datasets.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

This study benchmarks WGS-based structural variant callers using a PCR-confirmed gold standard, finding wide performance variation and identifying tools best suited for low and ultra-low pass data.

The paper evaluates existing microbial databases and proposes guidelines for creating a unified reference database to enhance the quality and comprehensiveness of metagenomics research.