Kishwar Shafin

T2T-HG002v1.1, a complete diploid HG002 assembly lacking detectable errors over 99.4% of its length, enables genome-based benchmarking of assemblies, sequencing reads, and phased variant calls.

PanVariants improves variant calling by integrating k-mer and mapping methods, reducing errors and outperforming existing tools for SNVs, indels, and SVs.

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.

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 updates Giraffe to map both long and short reads to large pangenome graphs, showing improved speed and accuracy compared to existing tools.

This study compares Element Avidity sequencing to Illumina sequencing, finding higher accuracy in mapping and variant calling, especially at lower coverages and in challenging genomic regions.

DeepPolisher improves genome assembly accuracy by reducing errors using a transformer model and phasing method, achieving over 70% indel error reduction.

This paper introduces pangenome-aware DeepVariant, a variant caller that improves accuracy by using a pangenome reference, outperforming linear-reference-based DeepVariant by up to 25.5%.

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 uses long-read sequencing to identify structural variants and their impacts on gene expression and methylation in diverse human brain samples.

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.

This paper introduces a local haplotype approximation method to improve variant calling accuracy across long-read sequencing platforms without increasing computational overhead.

The paper produces gapless assemblies of ape sex chromosomes for five great apes and a lesser ape, revealing divergence in Y chromosomes and evolutionary dynamics.

The paper presents the complete sequence of a human Y chromosome, correcting errors in the GRCh38 reference and adding over 30 million base pairs.

The paper presents a draft human pangenome reference that improves variant discovery and structural variant detection compared to existing references.

This paper develops a scalable protocol for Oxford Nanopore long-read sequencing, achieving high SNP and structural variant detection with haplotype-resolved methylation calls, suitable for large-scale genomics projects.

The paper introduces a tool called best for efficiently quantifying sequencing errors in aligned reads and shows it is 16 times faster than existing methods.

The paper evaluates sequencing and assembly methods to create high-quality diploid human genome assemblies, finding that long reads and haplotype sorting improve accuracy and completeness.

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

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.

The paper improves a telomere-to-telomere human genome assembly by developing a repeat-aware polishing strategy, fixing 51% of errors and increasing assembly quality.

A gapless 3.055 billion bp human genome from HiFi and ultra-long nanopore reads resolves all centromeric arrays and acrocentric p-arms, adding 182 Mbp of new sequence.

The paper presents a comprehensive analysis of fully assembled human centromeres, revealing their architecture, variation, and evolutionary dynamics at high resolution.

The T2T-CHM13 reference genome improves read mapping and variant calling across diverse samples, reducing false positives and enabling novel variant discovery.

Merfin improves variant filtering and polishing by using k-mer validation, enhancing precision and reducing errors in long-read assemblies.

The paper introduces a haplotype-aware variant calling pipeline for nanopore and PacBio HiFi data, achieving high accuracy in variant detection and de novo assembly polishing.

DeepConsensus improves PacBio HiFi read accuracy and assembly quality using a gap-aware transformer model.

DeepTrio improves variant calling in family trios using deep learning, achieving higher accuracy than DeepVariant, especially at lower coverages.

The paper presents an optimized PromethION nanopore sequencing method and new computational tools for efficient human genome assembly, achieving high accuracy and speed.