Carl Kingsford

This paper introduces a corrected estimator for mutual information that accounts for measurement error, showing improved accuracy in simulated and real gene expression data.

This study quantifies structural variability of TADs across 137 Hi-C samples, showing significant variation between samples and protocol-specific changes but robustness to lab differences.

The paper uses topological data analysis to study chromosome structure changes during cellular differentiation across three cell lines, identifying global and local topological differences.

The paper introduces a method to create practical universal k-mer sets for minimizer schemes that can handle larger k-mer lengths, reducing false positives in sequence matching.

The paper introduces an LSH method for edit distance that considers both k-mer content and order, improving alignment detection efficiency.

The paper develops a method to automatically choose optimal parameters for transcript assembly, improving accuracy over default settings on multiple RNA-Seq datasets.

This study identifies microbiota-dependent gene expression changes in Drosophila melanogaster heads using RNA-seq and nanoString nCounter, revealing genes involved in immune response, metabolism, and stress resistance.

The paper analyzes minimizers schemes and shows that asymptotically optimal schemes with lower density than previously thought exist, leading to more efficient bioinformatics tools.

SQUID detects transcriptomic structural variations from RNA-seq by improving accuracy on simulation data and identifying novel non-fusion-gene TSVs in TCGA samples.

The paper introduces an improved indexing method, Split Sequence Bloom Tree (SSBT), for efficiently searching large RNA-seq databases and demonstrates its performance on breast, blood, and brain tissue data.

This paper introduces a graph-guided assembly method for HLA typing that enables accurate identification of both known and novel alleles from whole genome sequencing data.

Scallop improves transcript assembly by preserving phasing paths and reducing coverage deviation, outperforming existing assemblers in multi-exon and lowly expressed transcript reconstruction.

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

Salmon provides accurate and fast transcript expression estimates by modeling and correcting GC content bias using a dual-phase inference algorithm.

The paper uses a Gaussian Network Model (GNM) to predict chromosomal dynamics from Hi-C data, showing correlations with accessibility and gene coexpression.

The paper introduces an efficient heuristic for decomposing a flow into the minimum number of paths, showing significant improvement over previous methods in both simulated and transcript assembly scenarios.

The paper introduces a method to efficiently search large RNA-seq databases for expressed sequences using Sequence Bloom Trees, achieving faster query times than existing tools.

This paper introduces a new sequence compression method for RNA-seq data that achieves better compression ratios than existing approaches, even with poor reference matches.