jaechang-hits

Facilitates Bayesian modeling using PyMC 5, enabling parameter estimation, model comparison, and predictive checks with uncertainty quantification.

Provides guidance on annotating statistical significance in plots, ensuring clarity and rigor in visual data representation.

Creates effective scientific schematics and diagrams for biological concepts, enhancing clarity and communication in research publications.

Enables Python statistical modeling for regression, time series, and hypothesis testing with detailed diagnostics and inference.

Facilitates seamless integration between Python and ImageJ/Fiji for advanced image processing and analysis.

Enables precise single-particle tracking in video microscopy, extracting trajectories and diffusion coefficients for detailed analysis.

Provides guidance on creating effective scientific visualizations for publications, focusing on chart selection, color theory, and formatting.

Facilitates comprehensive sequence analysis in bioinformatics, enabling parsing, alignment, and phylogenetic tree construction.

Analyzes TF motif enrichment in ChIP-seq/ATAC-seq peaks using HOMER, enabling motif discovery and peak annotation for genomic studies.

MACS3 identifies enriched regions in ChIP-seq/ATAC-seq data, producing peak files for motif analysis and differential binding studies.

Provides a Python library for biological data analysis, enabling sequence manipulation, phylogenetics, and ecological statistics.

Integrates with Benchling's R&D platform for managing biological data and automating workflows using Python SDK.

Enables automation of liquid handling protocols for Opentrons robots using Python, enhancing lab efficiency and accuracy.

Automates plasmid annotation by identifying features through BLAST searches, generating GenBank files, HTML maps, and CSV tables.

Predicts RNA secondary structures and interactions using ViennaRNA Python bindings for various RNA applications.

Parses the Human Metabolome Database XML for detailed metabolite information, aiding in metabolomics research and analysis.

Facilitates mass spectrometry data processing and metabolite identification using spectral matching techniques.

Facilitates proteomics analysis using MaxQuant and Perseus, enabling statistical analysis and visualization of protein data in Python.

Enables users to search and download proteomics datasets from the PRIDE Archive v3 REST API, facilitating data analysis and research.

Processes LC-MS/MS data for proteomics and metabolomics using PyOpenMS, enabling feature detection and peptide identification.