ChromHMM(1)

✨ ChromHMM(1) ✨

Okay bestie, welcome to the ChromHMM(1) page from the General Commands Manual 🌸 Let's get into it 💅

🧚 NAME 🧚

ChromHMM — learning and analyzing chromatin states with a multivariate Hidden Markov Model 💜

🎀 SYNOPSIS 🎀

java -Xmx[GB]g -jar /usr/share/java/chromhmm.jar [options]

The [GB] bit is the placeholder you swap out, and [options] is where your ChromHMM commands go 🫶

🌈 DESCRIPTION 🌈

ChromHMM is software for learning and characterizing chromatin states, and it is lowkey iconic 👑

It can take in multiple chromatin datasets, like ChIP-seq data for different histone modifications, and discover de novo the major recurring combinatorial and spatial patterns of marks. The whole thing runs on a multivariate Hidden Markov Model that explicitly models whether each chromatin mark is present or absent 🔥 The model you end up with can then be used to systematically annotate a genome in one or more cell types. It's giving full genome glow-up ✨

It also computes state enrichments for large-scale functional and annotation datasets, so you can figure out what each state actually means biologically 🧠 And finally, ChromHMM produces files with genome-wide maps of chromatin state annotations that you can load straight into a genome browser 🦋

🌸 OPTIONS 🌸

• LearnModel — This one is the main character 👑 It takes a set of binarized data files and learns chromatin state models. By default it produces a segmentation, generates browser output with default settings, and calls OverlapEnrichment and NeighborhoodEnrichments with default settings for the genome assembly you specify. It also creates a webpage with links to all the files and images it made 💖
• BinarizeBed — Converts a set of bed files of aligned reads into binarized data files for model learning. You can optionally print the intermediate signal files too 🍓
• BinarizeBam — Same energy as BinarizeBed, but it converts a set of bam files of aligned reads into binarized data files for model learning. Again, you can optionally print the intermediate signal files 🌈
• BinarizeSignal — Converts a set of signal files into binarized files. Simple and clean 💅
• MakeSegmentation — Takes a learned model and binarized data, then outputs a segmentation 🦋
• MakeBrowserFiles — Converts segmentation files into a browser-viewable format. Browser-ready, periodt ✨
• OverlapEnrichment — Shows the enrichment of each state of a segmentation for a set of external data 🔥
• NeighborhoodEnrichment — Shows the enrichment of each state relative to a set of anchor positions 🧚
• CompareModels — Can compare models with different numbers of states, based on how correlated their emission parameters are 💜
• Reorder — Lets you reorder the states of the model, reorder the columns of the emission matrix, or add state labels 🎀
• EvalSubset — Evaluates how well a subset of marks can recover a segmentation made with the full set of marks. Main character energy when you only need some of the data 🙈
• StatePruning — Prunes states from a model so you can initialize models when using the non-default two-pass approach 🫶

🦋 SEE ALSO 🦋

http://compbio.mit.edu/ChromHMM/

💖 AUTHOR 💖

ChromHMM was written by Jason Ernst. Legend behavior 👑

Version 1.14, March 2018. That's the manual, bestie, and you're literally ready to go 💅🌸