Jacob D. Jaffe

The paper identifies a protein-protein interaction network in human excitatory neurons for 13 ASD risk genes, revealing novel interactions and convergent mechanisms contributing to autism spectrum disorders.

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

The paper introduces a cloud-based implementation of the AvG algorithm for DIA data analysis, improving efficiency and accessibility for large datasets.

The paper introduces a computational method to generate causal explanations for proteomic profiles by integrating molecular data with prior biological pathway knowledge, demonstrating its application in various biological contexts.

Avant-garde improves DIA data analysis by refining signals, reducing interference, and controlling false discovery rates through a data-driven approach.

piNET is a web platform for analyzing and visualizing proteomics data, including PTMs, by connecting them to biological knowledge and LINCS signatures.

The paper shows that KDM6A, an H3K27 histone demethylase, is oxygen-sensitive and its inactivation under hypoxia affects chromatin and cell fate.

The paper develops a model to accurately quantify proteoform stoichiometries using mass-spectrometry data without external standards.

The paper investigates mitotic bookmarking through histone modifications and nucleosome deposition patterns in human cells, showing that epigenomic landscapes and modified nucleosomes contribute to chromatin reestablishment after mitosis.

GeNets provides a platform to compare genomic networks using machine learning and allows analysis of genetic and RNA sequencing data.

Specter improves DIA proteomics by directly deconvoluting spectra using linear algebra, enhancing sensitivity for similar peptides and challenging cases.

The paper creates a proteomic resource profiling 90 drugs in six cell lines, revealing cell-specific responses and enabling integration with transcriptional data to identify therapeutic opportunities.