Code is available here: https://bitbucket.org/mirnylab/hiclib
Here we present a collection of tools to map, filter and analyze Hi-C data. The library is written in Python, an easy-to-learn human-friendly programming language.
This page contains the general information about the library. Detailed documentation for each module can be found in API documentation below.
“Update from 2014-05-14” I’ve tried to streamline the pipeline which does read mapping of .sra files from GEO. It is in examples/pipeline2014. Mapping performance was significantly increased.
This comment mostly applies human/mouse Hi-C data, as well as other multi-chromosomal organisms with long genomes. It indicates what we have changed recently and how we deal with billion-read Hi-C datasets.
This library consists of three parts: mapping pipeline (mapping.py), fragment-level filtering pipeline (FragmentHiC.py), and a binned data analysis toolset (BinnedData.py).
Originally, all three modules were developed for analysis of the (Lieberman 2009) Hi-C data, and were used mostly for analysis of inter-chromosomal data at low resolution (>= 200 kb, see Imakaev 2012).
As times passes, Hi-C data gets more and more reads, and requires analyses at higher and higher resolution. FragmentHiC and mapping modules were written with that in mind, and were able to keep up with the current increase of the Hi-C sequencing depth. As a proof of principle, they were used to map, filter and combine all published IMR90 and HES datasets from (Jin, 2013) and (Dixon, 2012), totalling to over a billion mapped double-sided reads in one of the combined datasets. fragnentHiC was then used to build a by-chromosome whole-genome heatmap at 40kb resolution (saveByChromosomeHeatmap), as well as a by-chromosome within-chromosome-only heatmap at 10kb resolution (saveHiResHeatmapWithOverlaps). All of these tasks were performed using a mere 16GB of RAM, though 32GB would probably be recommended. All mapping and filtering of (Naumova 2013), and (Le 2013) were performed with these two pipelines.
The increase in the Hi-C data resolution also changed the type and scale of analyses preformed on Hi-C data. Previous low-resolution analyses often focused on a genome-wide characterization of chromosomal interactions, or on 1Mb-resolution analyses of within-chromosomal data, and genome- or chromosome-wide compartments (Lieberman 2009, Yaffe 2011, Imakaev 2012). Now, the focus of analyses is shifting towards locus specific intra-chromosomal features, such as TADs (Dixon 2012) and enhancer-promoter interactions (Jin, 2013).
The BinnedData module was originally built, and is still fully equipped to perform comparative analysis of multiple Hi-C datasets at low resolution (200kb+, maybe 100kb). To analyze whole-genome data at low resolution (200k+), we use a whole-genome iterative correction in the BinnedData module (see Imakaev 2012). However, this module is limited to a set of tools needed to analyze the Hi-C data at low resolution, and is not suitable for higher resolution analyses. Additionally, the memory footprint of this class is quadratic with the number of bins, and therefore it cannot be used to analyze the Hi-C data at bin sizes much less than 100kb. Therefore, a different methodology and different set of tools are required to analyse high-resolution features of the Hi-C data.
In our current projects (see for example Naumova 2013), we mostly analyze between-chromosomal data at low resolutions (200kb to 1MB) using a genome-wide ICE, and within-chromosomal data at higher resolution (40kb). The rationale for the low-resolution genome-wide analysis is that at higher resolution, between-chromosomal data becomes too sparse, with fewer than 1 read per bin pair on average.
Within-chromosomal Hi-C maps contain a higher-resolution features of the Hi-C data. To analyze them, we remove biases from the within-chromosomal Hi-C data with per-chromosome iterative correction. We perform per-chromosome iterative correction using mirnylib.ultracorrect set of methods, after removing all bins which have fewer than some cutoff number of reads (e.g. 50 or 100). We used within-chromosome corrected data to obtain within-chromosome A- and B-compartment profile using eigenvector expansion, as described in (Naumova, 2013). We followed this strategy for (Naumova, 2013), as well as many of our ongoing projects.
We also developed a set of tools to perform whole-genome iterative correction at high resolution (up to 20kb, or fragment-level). In late 2012 we developed a tool to perform whole-genome iterative correcton of Hi-C datasets at a very high resolution (up to 20kb) using HiResBinnedData module. This module was written as a sketch and proof-of-concept, and is not actively used or maintained. It was however successfully used to iteratively correct genome-wide Hi-C data sets at 20kb resolution in a few hours (see examples in testHighResHiC). Additionally, we developed a fragment-based iterative correction (FragmentHiC.IterativeCorrectionFromMax) which takes into accout variable fragment length. We often use this method, especially for shorter genomes.
To summarize, our current Hi-C analysis pipeline builds around iterative mapping and fragment-level Hi-C filtering. A separate project-specific set of scripts is used to perform per-chromosome ICE and downstream analysis. The BinnedData module is currently used for a specific subset of low-resolution (100k-1M) analyses of the Hi-C data.
Tiny bibliography... (Lieberman, 2009): Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome; (Kalhor, 2011):Genome architectures revealed by tethered chromosome conformation capture and population-based modeling; (Yaffee, 2011): Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture; (Dixon, 2012): Topological domains in mammalian genomes identified by analysis of chromatin interactions; (Imakaev, 2012): Iterative correction of Hi-C data reveals hallmarks of chromosome organization; (Naumova, 2013): Organization of the Mitotic Chromosome; (Le, 2013): High-Resolution Mapping of the Spatial Organization of a Bacterial Chromosome; (Jin, 2013): A high-resolution map of the three-dimensional chromatin interactome in human cells.
Written by Maxim Imakaev. Please contact me (email@example.com) with any questions. (2013-11-25)
The hiclib was designed and tested only under Ubuntu environment, but may theoretically be ported to the other Linux distributions and OS.
Hiclib requires the following python libraries:
hiclib and mirnylib are not compatible with python 2.5 and python 3.x, but work fine with python 2.6, 2.7.
Besides that, you will need the following binaries and libraries (the names are given for the corresponding Ubuntu packages):
Finally, you will also need the bowtie2 mapping software which can be downloaded manually or installed via the download_bowtie.sh script in the hiclib folder.
To install the library in Linux, do the following procedure:
As of May 2014, both Ubuntu 12.04 and 14.04 provide packages which are recent enough for the library to run successfully.
If you’re upgrading a package (numpy/scipy/matplotlib/cython/etc...) using the pip Python package manager, use “pip install PACKAGENAME”, not “pip install –upgrade PACKAGENAME”!
If you have another version of the same package installed via the standard system package manager, e.g. apt-get or aptitude, the “pip install –upgrade” command will most likely create a second copy of the package. This causes numerous problem that are very hard to detect. If ‘pip install” says that package is already installed, remove it via the system package manager and only then try to install it again. However, if this asks you to remove a ton of dependent packages, you will need to specifically delete files corresponding to the original package. Current version and location of package files can be determined from the python console by executing, e.g., >>>print numpy.__version__. and >>>print numpy.__file__
Also note that PIP compiles packages. Therefore, to do a successfull and complete installation you would need to install all libraries which are required to build a package. Under Ubuntu, you can do this using “aptitude build-dep packagename”, where packagename is an aptitude (not PIP) name of the package (e.g. python-numpy) This is very important for compilation of matplotlib and h5py.
Please refer to troubleshooting guide for a list of known errors!
Fragment-based analysis uses HDD to store all the information about the reads. However, at each point in time a few individual tracks are loaded to RAM to perform certain analysis. Memory requirements for this part can be estimated as 20-30 bytes per read, dependent on the number of filters used. For example, a 500 mln read Hi-C dataset will require no more than 16GB RAM. We’re working on rewriting the core of the library to consume less memory.
Binned data analysis uses RAM to store all the heatmaps. Application of certain filters will create a copy of a heatmap, one at a time, even if multiple datasets are loaded. For example, working with 3 datasets at 200-kb resolution will require: (3 GB/200kb)^2 * (8 bytes per float64) * (3 datasets + 1 extra) = 8 GB RAM
highResBinnedData uses sparse logic to store Hi-C matrix in memory (in a compressed format), or on the HDD. Even in memory storage often allows to fit a 500mln read human Hi-C dataset at 10kb resolution in under 8GB (thanks to HDf5!)
Timewise, fragment-based correction of a dataset is relatively quick, being about an hour for 500 million raw reads. Binned data analysis performance depends on resolution only, usually being of the order of seconds for 1M-resolution analysis, and scaling as (1/resolution^2), reaching few minutes for 200kb resolution, and tens minutes for 100kb. High-resolution binned data takes few hours to complete.
Overall, time-limiting step is mapping, and it’s the only part of the code that is well-paralellized. Multi-threading is only utilized by bowtie when mapping reads, and takes about a few days to map 500-million-read dataset on a high-level desktop, or a day on a high-performance workstation.
mirnylab.h5dict is a default storage used by all parts of the library. H5dict is a persistent python dictionary with all the data stored on the HDD. It uses HDF5 to store and access the data, and can be viewed using external HDF5 viewers or python. It stores data in a compressed format. For small datasets, it can be initialized to hold data in memory only.
H5dict is not equivalent to dict, as it does not provide direct references to arrays on an HDD. For example >>> a = h5dict["myarray"] >>> a = 1 will not change the array on the hdd. You would need to run >>> h5dict["myarray"] = a to commit the change to the hard disk. If you want to edit a part of an array, you can get a direct h5py dataset using >>> h5dict.get_dataset
H5dict file can be converted to txt format or matlab format using a small utilities h5dictToTxt.py and h5dictToMat.py. Both utilities are a part of mirnylib repository.
Two sides of the read are mapped to the genome independently using iterative mapping function. Acceptable read format is fastq (including fastq.gz), but .sra format can be incorporated using a bash_reader parameter of iterative_mapping and a fastq-dump tool from SRA toolkit. Iterative mapping outputs a combination of sam/bam files (autodetect by filename), which can be then parsed and written to an h5dict containing read data using parse_sams function.
Fragment-level analysis inputs an h5dict, or any dictionary-like interface, containing at minimum information about read positions (however, strand information is highly recommended). It stores all the data in an h5dict, and modifies it as the analysis goes.
fragmentHiC module modifies the data file when any filter is applied.
An example script shows how fragment-level analysis can be used to merge multiple datasets together, filter them and save heatmaps to an h5dict.
Heatmaps are then exported by fragment-level analysis to an h5dict, that can be passed to a binnedData class. BinnedData class will load this h5dict, or any other dict-like object, perform multiple types of iterative correction and eigenvector expansion, and compare results with different genomic tracks.
High-resolution binned data class can be used to perform Iterative Correction at high resolutions (was tested at 10kb resolution). It is much less feature-rich, and relies on the user to do all the analysis. It can operate on one dataset only.
Our pipeline also presents a few example scripts that can be used to analyze the data. These scripts can be applied to any Hi-C data with minimal modification.
To perform iterative mapping, use examples/iterative_mapping.py scipt.
To combine data from multiple sequencing lanes, filter all lanes together (including filtering duplicates), and write out heatmaps. You can do it using examples/filteringManyDataests. You can modify genome files location inside the script, and provide mapping output files in the datasets.tsv file. By default, this script saves heatmaps for all replicas of an experiments, then for each experiment combines all replicas together and saves combined heatmaps.
To perform iterative correction and eigenvector expansion, use example scripts (example/iterativeCorrectionEigenvectorExpansion)