Serafim Batzoglou

This study evaluates how chromatographic sampling affects quantification and biological discovery in DIA proteomics, finding that statistical power and LOQ are more critical than identifications.

Radiant DIA is a fast and cost-effective search engine for proteomics that maintains accuracy and sensitivity, enabling efficient analysis of large-scale datasets.

The paper presents a scalable MBR algorithm for proteomics cohort analysis, enabling faster processing of large datasets with results comparable to existing methods.

This study quantitatively analyzes plasma proteome changes in vaccinated individuals using a nanoparticle-based proteomic workflow, identifying proteins associated with vaccine response and COVID-19 infection.

This study performs a genome-wide association study using mass spectrometry proteomics to identify pQTLs, finding that epitope effects may affect some affinity proteomics results while confirming others.

This study uses mass spectrometry proteomics to identify protein altering variants and map pQTLs, confirming target specificity and identifying novel variants not accessible by affinity proteomics.

This study identifies novel plasma biomarkers for Alzheimer's disease and related dementias using unbiased proteomics in a large cohort.

This paper demonstrates that nanoparticle-based proteomics workflows enable accurate and scalable deep plasma proteomics with high precision and statistical power for biomarker discovery.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper assembles the pig-tailed macaque genome using linked-read sequencing and HiC scaffolding, achieving chromosome-level resolution and identifying large-scale chromosomal rearrangements with humans.

This study uses read cloud sequencing to identify mobile elements in Bacteroides caccae that contribute to antibiotic resistance in a patient's microbiome over time.

This paper introduces a read cloud metagenomic sequencing approach combined with a new assembler, Athena, to generate high-quality microbial genome drafts from human and marine microbiomes without culturing.

Network Enhancement improves the signal-to-noise ratio of weighted biological networks, enhancing downstream analysis performance across multiple applications.

HAPDeNovo filters and phases de novo mutations in linked read sequencing data to reduce false positives while maintaining sensitivity.

GATTACA bins metagenomic contigs into species-specific clusters using compact k-mer indexing and MinHash, achieving accuracy comparable to existing methods but with significant speed improvements.

SIMLR is a tool for single-cell RNA-seq analysis that improves clustering and visualization through multi-kernel learning.

Ref-Reveel improves genotype calling accuracy and efficiency from low-coverage sequencing data by jointly analyzing large cohorts and using generated genotypes as references for new datasets.

This paper introduces GROC-SVs, a method for detecting and assembling structural variants using read cloud data, showing improved specificity and enabling complex breakpoint reconstruction in cancer samples.

This paper introduces SIMLR, a similarity-learning framework for single-cell RNA-seq data analysis, showing improved clustering and visualization.

Reveel improves low-coverage whole-genome variant calling and genotyping by leveraging linkage disequilibrium, achieving higher accuracy and lower computation cost than existing methods.