FreedomIntelligence

Bead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization

Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms

Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC

Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods

Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods

Read and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration

Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection

Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks

Design guide RNAs for CRISPR-Cas9/Cas12a experiments using CRISPRscan and local scoring algorithms. Score guides for on-target activity using Rule Set 2 and Azimuth models

Design homology-directed repair donor templates for CRISPR knock-ins using primer3-py. Create ssODN, dsDNA, or plasmid templates with optimized homology arms

Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity

Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks

Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices

Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data

Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats

Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices

Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing

Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens

Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design

Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses

Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions

Guidance for SPR and BLI binding characterization experiments

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner

Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters

Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2

Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS

Analyze transcription factor motif accessibility variability using chromVAR

Extract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis

Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools