Yaron Orenstein

Generating minimum-density minimizers

This paper introduces an algorithm to compute minimum-density minimizers for high-throughput sequencing, achieving faster results than brute-force methods.

Inferring binding specificities of human transcription factors with the wisdom of crowds

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

A conserved regulatory atlas reveals enhancers critical for mouse and human gonadal development

The paper constructs a comparative regulatory atlas of gonad development using mouse and human data, identifies a conserved enhancer critical for gonadal development, and prioritizes non-coding variants in DSD patients.

CROP: A feature-independent context-aware method for CRISPR-Cas9 frameshift prediction

CROP predicts CRISPR-Cas9 frameshift rates using a context-aware, feature-independent method, outperforming existing approaches across multiple datasets.

10-minimizers: a promising class of constant-space minimizers

This paper introduces 10-minimizers, a class of constant-space minimizers with lower density than random minimizers and efficient k-mer key retrieval.

Context-dependent determinants of CRISPR-Cas9 editing efficiency revealed through cross-species endogenous editing analysis

The paper identifies context-dependent factors affecting CRISPR-Cas9 editing efficiency across species using endogenous editing data and shows that repair outcomes are conserved.

ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning

ExoShorkie improves RNA-seq coverage prediction for exogenous genomes in yeast using transfer learning, outperforming existing methods and revealing new regulatory insights.

GreedyMini: Generating low-density DNA minimizers

GreedyMini generates DNA minimizers with low expected density, improving HTS analysis performance and approaching theoretical lower bounds.

Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

The paper generates a comprehensive GUIDE-seq dataset with bulges, trains and evaluates machine-learning models to predict off-target sites with bulges, and visualizes model features.

Efficient minimizer orders for large values of k using minimum decycling sets

The paper introduces new minimizer orders for large k-values using decycling sets, improving efficiency in DNA sequencing analysis.

A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction

The paper evaluates CRISPR off-target site prediction methods using large-scale in vitro and in vivo datasets, highlighting the importance of data preprocessing, inactive site inclusion, and mismatch features.

Improving the efficiency of de Bruijn graph construction using compact universal hitting sets

The paper demonstrates that using universal hitting sets improves de Bruijn graph construction efficiency by reducing runtime and memory usage in genome assembly.

Overcoming the design, build, test (DBT) bottleneck for synthesis of nonrepetitive protein-RNA binding cassettes for RNA applications

The paper uses high-throughput oligo libraries and machine learning to design non-repetitive RNA binding cassettes for gene editing and RNA tracking, demonstrating their functionality in mammalian cells.

DeCoDe: degenerate codon design for complete protein-coding DNA libraries

DeCoDe optimizes degenerate codon libraries for protein-coding DNA, outperforming existing methods and supporting mixed-length libraries.

A randomized parallel algorithm for efficiently finding near-optimal universal hitting sets

This paper introduces PASHA, a randomized parallel algorithm for generating near-optimal universal hitting sets, which is significantly faster and more memory-efficient than existing methods.

A Deep Learning Approach for Learning Intrinsic Protein-RNA Binding Preferences

This paper introduces DLPRB, a deep learning model for predicting protein-RNA interactions by analyzing RNA sequence and structure, showing improved accuracy over existing methods.

Improving the performance of minimizers andwinnowing schemes

The paper improves minimizers and winnowing schemes by analyzing k-mer ordering and introducing universal hitting sets to enhance performance.