Sayed Mohammad Ebrahim Sahraeian

A near-complete human pancreatic cancer genome

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.

Small variant benchmark from a complete assembly of X and Y chromosomes

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.

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

The paper develops a benchmark for detecting low-frequency mosaic variants in the HG002 reference material using high-coverage sequencing data and validates its utility across multiple technologies.

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The paper evaluates variant calling in difficult genomic regions using short- and long-read sequencing, showing improved performance with new methods and multi-technology approaches.

Towards a Comprehensive Variation Benchmark for Challenging Medically-Relevant Autosomal Genes

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.

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

A robust benchmark for germline structural variant detection

Genome in a Bottle benchmark of 12745 sequence-resolved insertions and deletions of 50 bp or more, integrating 19 calling methods, scores false negatives and false positives alike.

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

This study develops and validates deep learning strategies for robust cancer mutation detection using tumor-normal sequencing data across multiple technologies and scenarios.

Deep convolutional neural networks for accurate somatic mutation detection

NeuSomatic is a convolutional neural network for somatic mutation detection that outperforms existing methods across various sequencing platforms and tumor purities.