Giuseppe Narzisi

The paper assembles and characterizes 2,110 human centromeres, revealing novel haplotypes, mobile element insertions, and mutation rate variation across chromosomes.

This paper presents near-complete, haplotype-resolved assemblies of a pancreatic cancer genome and normal tissue, identifying extensive somatic variants and epigenetic changes in previously excluded regions.

Lancet2 improves somatic variant calling with faster runtime and better performance, especially for InDels, using enhanced methods and machine learning.

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

The paper benchmarks somatic variant and indel detection at low allele fractions using short- and long-read data, finding sensitivity and precision around 80% at 300x coverage.

The paper describes a comprehensive genomic dataset from a tumor-normal pair with broad consent, generated using multiple sequencing technologies for benchmarking somatic variant detection.

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

The paper creates a variant benchmark for X and Y chromosomes using a complete assembly of HG002, highlighting challenges in variant detection in complex regions.

The paper introduces tools to efficiently index and query annotations in pangenome graphs using GAF format, demonstrating applications on human pangenome data.

The paper introduces NAHRwhals, a method to detect serial structural variations in long-read genomes, identifies 37 sSV loci in humans, and shows their relevance to disease.

The paper presents a high coverage whole genome sequencing resource of 3,202 samples, including 602 trios, generated from the expanded 1000 Genomes Project.

This paper expands genome benchmarking to include difficult-to-map regions using linked and long reads, identifying more false negatives in variant calling.

This paper creates a comprehensive benchmark for challenging medically-relevant autosomal genes, improving variant recall from 8% to 100% by addressing reference-specific false duplications.

Whole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

The paper introduces an updated version of the Lancet somatic variant caller that supports linked-reads sequencing data analysis by integrating barcodes and haplotype read assignments.

The paper assesses DNA sequencing performance across multiple platforms using human and bacterial reference genomes to evaluate quality and reproducibility.

The paper introduces SVCurator, a crowdsourcing app for evaluating structural variants in the human genome, finding high concordance among curators and with existing benchmarks.

ExpansionHunter Denovo detects known and novel repeat expansions in short-read sequencing data without requiring predefined repeat catalogs.

ExpansionHunter is a tool for accurately genotyping short tandem repeats, particularly in complex regions with adjacent variants or imperfect repeats.

Lancet improves somatic variant calling accuracy, especially for indels, using colored DeBruijn graphs and local assembly across synthetic and real whole-genome sequencing data.

The paper introduces ExpansionHunter, a tool for detecting long repeat expansions from PCR-free WGS data, achieving high sensitivity and specificity in identifying pathogenic expansions.

This paper introduces Scalpel, an open-source tool for accurate indel detection from short-read sequencing data, and demonstrates its application in various analysis modes.

This study identifies sources of INDEL calling errors in sequencing data and shows that whole-genome sequencing (WGS) outperforms whole-exome sequencing (WES) in accuracy and sensitivity for detecting INDELs.

Scalpel detects de novo and transmitted INDELs in exome data with high specificity and sensitivity, outperforming existing tools in challenging repeat regions and identifying new autism-related genes.