General purpose stuff to generate and handle Hi-C data in its simplest form.
Project description
hicstuff
A lightweight library that generates and handles Hi-C contact maps in either cooler-compatible 2Dbedgraph or instaGRAAL format. It is essentially a merge of the yahcp pipeline, the hicstuff library and extra features illustrated in the 3C tutorial and the DADE pipeline, all packaged together for extra convenience.
The goal is to make generation and manipulation of Hi-C matrices as simple as possible and work for any organism.
Table of contents
Installation
To install a stable version:
pip3 install -U hicstuff
or, for the latest development version:
pip3 install -e git+https://github.com/koszullab/hicstuff.git@master#egg=hicstuff
Note for OSX and BSD users: hicstuff pipeline
relies on the GNU coreutils. If you want to use it, you should use these as your default. Here is a tutorial to set the gnu coreutils as default commands on OSX.
Usage
Full pipeline
All components of the pipelines can be run at once using the hicstuff pipeline
command. This allows to generate a contact matrix from reads in a single command. By default, the output sparse matrix is in GRAAL format, but it can be a 2D bedgraph file if required.
usage:
pipeline [--quality_min=INT] [--duplicates] [--size=INT] [--no-cleanup]
[--threads=INT] [--minimap2] [--bedgraph] [--prefix=PREFIX]
[--tmpdir=DIR] [--iterative] [--outdir=DIR] [--filter]
[--enzyme=ENZ] [--plot] --fasta=FILE
(<fq1> <fq2> | --sam <sam1> <sam2> | --pairs <bed2D>)
arguments:
fq1: Forward fastq file. Required by default.
fq2: Reverse fastq file. Required by default.
sam1: Forward SAM file. Required if using --sam to skip
mapping.
sam2: Reverse SAM file. Required if using --sam to skip
mapping.
bed2D: Sorted 2D BED file of pairs. Required if using
"--pairs" to only build matrix.
options:
-b, --bedgraph If enabled, generates a sparse matrix in
2D Bedgraph format (cooler-compatible)
instead of GRAAL-compatible format.
-C, --circular Enable if the genome is circular.
-d, --duplicates: If enabled, trims (10bp) adapters and
remove PCR duplicates prior to mapping.
Only works if reads start with a 10bp
sequence. Not enabled by default.
-e ENZ, --enzyme=ENZ Restriction enzyme if a string, or chunk
size (i.e. resolution) if a number. Can
also be multiple comma-separated enzymes.
[default: 5000]
-f FILE, --fasta=FILE Reference genome to map against in FASTA
format
-F, --filter Filter out spurious 3C events (loops and
uncuts) using hicstuff filter. Requires
"-e" to be a restriction enzyme, not a
chunk size.
-S, --sam Skip the mapping and start pipeline from
fragment attribution using SAM files.
-i, --iterative Map reads iteratively using hicstuff
iteralign, by truncating reads to 20bp
and then repeatedly extending and
aligning them.
-m, --minimap2 Use the minimap2 aligner instead of
bowtie2. Not enabled by default.
-A, --pairs Start from the matrix building step using
a sorted list of pairs in 2D BED format.
-n, --no-cleanup If enabled, intermediary BED files will
be kept after generating the contact map.
Disabled by defaut.
-o DIR, --outdir=DIR Output directory. Defaults to the current
directory.
-p, --plot Generates plots in the output directory
at different steps of the pipeline.
-P PREFIX, --prefix=PREFIX Overrides default GRAAL-compatible
filenames and use a prefix with
extensions instead.
-q INT, --quality_min=INT Minimum mapping quality for selecting
contacts. [default: 30].
-s INT, --size=INT Minimum size threshold to consider
contigs. Keep all contigs by default.
[default: 0]
-t INT, --threads=INT Number of threads to allocate.
[default: 1].
-T DIR, --tmpdir=DIR Directory for storing intermediary BED
files and temporary sort files. Defaults
to the output directory.
output:
abs_fragments_contacts_weighted.txt: the sparse contact map
fragments_list.txt: information about restriction fragments (or chunks)
info_contigs.txt: information about contigs or chromosomes
For example, to run the pipeline with minimap2 using 8 threads and generate a matrix in instagraal format in the directory out
:
hicstuff pipeline -t 8 -m -e DpnII -o out/ -f genome.fa reads_for.fq reads_rev.fq
The pipeline can also be run from python, using the hicstuff.pipeline
submodule. For example, this would run the pipeline with bowtie2 (default) using iterative alignment and keep all intermediate files.
from hicstuff import pipeline as hpi
hpi.full_pipeline(
'genome.fa',
'end1.fq',
'end2.fq',
no_cleanup=True
iterative=True
out_dir='out',
enzyme="DpnII")
Individual components
For more advanced usage, different scripts can be used independently on the command line to perform individual parts of the pipeline. This readme contains quick descriptions and example usages. To obtain detailed instructions on any subcommand, one can use hicstuff <subcommand> --help
.
Iterative alignment
Truncate reads from a fastq file to 20 basepairs and iteratively extend and re-align the unmapped reads to optimize the proportion of uniquely aligned reads in a 3C library.
usage:
hicstuff iteralign [--minimap2] [--threads=1] [--min_len=20] --out_sam=FILE --fasta=FILE <reads.fq>
Digestion of the genome
Digests a fasta file into fragments based on a restriction enzyme or a fixed chunk size. Generates two output files into the target directory named "info_contigs.txt" and "fragments_list.txt"
usage:
digest [--plot] [--figdir=FILE] [--circular] [--size=INT]
[--outdir=DIR] --enzyme=ENZ <fasta>
For example, to digest the yeast genome with MaeII and HinfI and show histogram of fragment lengths:
hicstuff digest --plot --outdir output_dir --enzyme MaeII,HinfI Sc_ref.fa
Filtering of 3C events
Filters spurious 3C events such as loops and uncuts from the library based on a minimum distance threshold automatically estimated from the library by default. Can also plot 3C library statistics.
usage:
filter [--interactive | --thresholds INT-INT] [--plot]
[--figdir FILE] <input> <output>
Viewing the contact map
Visualize a Hi-C matrix file as a heatmap of contact frequencies. Allows to tune visualisation by binning and normalizing the matrix, and to save the output image to disk. If no output is specified, the output is displayed interactively. If two contact maps are provided, the log ratio of the first divided by the second will be shown.
usage:
view [--binning=1] [--despeckle] [--frags FILE] [--trim INT]
[--normalize] [--max=99] [--output=IMG] [--cmap=CMAP]
[--log] [--region=STR] <contact_map> [<contact_map2>]
For example, to view a 1Mb region of chromosome 1 from a full genome Hi-C matrix rebinned at 10kb:
hicstuff view --normalize --binning 10kb --region chr1:10,000,000-11,000,000 --frags fragments_list.txt contact_map.tsv
Library
All components of the hicstuff program can be used as python modules. See the documentation on reathedocs. The expected contact map format for the library is a simple CSV file, and the objects handled by the library are simple numpy
arrays. The various submodules of hicstuff contain various utilities.
import hicstuff.digest # Functions to work with restriction fragments
import hicstuff.iteralign # Functions related to iterative alignment
import hicstuff.hicstuff # Contains utilities to modify and operate on contact maps as numpy arrays
import hicstuff.filter # Functions for filtering 3C events by type (uncut, loop)
import hicstuff.view # Utilities to visualise contact maps
import hicstuff.io # Reading and writing hicstuff files
import hicstuff.pipeline # Generation and processing of files to generate matrices.
Connecting the modules
All the steps described here are handled automatically when running the hicstuff pipeline
. But if you want to connect the different modules manually, the intermediate input and output files must be processed using light python scripting.
Aligning the reads
You can generate SAM files independently using your favorite read mapping software, use the command line utility hicstuff iteralign
, or use the helper function align_reads
in the submodule hicstuff.pipeline
. For example, to perform iterative alignment using minimap2 (instead of bowtie2 by default):
Using the python function:
from hicstuff import pipeline as hpi
hpi.align_reads("end1.fastq", "genome.fasta", "end1.sam", iterative=True, minimap2=True)
Using the command line tool:
hicstuff iteralign --minimap2 --iterative -f genome.fasta -o end1.sam end1.fastq
Extracting contacts from the alignment
The output from hicstuff iteralign
is a SAM file. In order to retrieve Hi-C pairs, you need to run iteralign separately on the two fastq files and process the resulting alignment files as follows using the pipeline
submodules of hicstuff.
from hicstuff import pipeline as hpi
import pysam as ps
# Sort alignments by read names
ps.sort("-n", "-O", "SAM", "-o", "end1.sam.sorted", "end1.sam")
ps.sort("-n", "-O", "SAM", "-o", "end2.sam.sorted", "end2.sam")
# Combine SAM files
hpi.sam2pairs("end1.sorted.sam", "end2.sorted.sam", "output.pairs", "info_contigs.txt", min_qual=30)
This will generate a "pairs" file containing all read pairs where both reads have been aligned with a mapping quality of at least 30.
Attributing each read to a restriction fragment
To build a a contact matrix, we need to attribute each read to a fragment in the genome. This is done under the hood by performing a binary search for each read position against the list of restriction sites in the genome.
from hicstuff import digest as hcd
from Bio import SeqIO
# Build a list of restriction sites for each chromosome
restrict_table = {}
for record in SeqIO.parse("genome.fasta", "fasta"):
# Get chromosome restriction table
restrict_table[record.id] = hcd.get_restriction_table(
record.seq, enzyme, circular=circular
)
# Add fragment index to pairs (readID, chr1, pos1, chr2,
# pos2, strand1, strand2, frag1, frag2)
hcd.attribute_fragments("output.pairs", "output_indexed.pairs", restrict_table)
Filtering pairs
The resulting pairs file can then be filtered, either in the command line using the hicstuff filter
command, or in python using the hicstuff.filter
submodule. Otherwise, the matrix can be built directly from the unfiltered pairs.
Filtering on the command line:
hicstuff filter output_indexed.pairs output_filtered.pairs
Filtering in python:
from hicstuff import filter as hcf
uncut_thr, loop_thr = hcf.get_thresholds("output_indexed.pairs")
hcf.filter_events("output_indexed.pairs", "output_filtered.pairs", uncut_thr, loop_thr)
Note that both the command and the python function have various options to generate figure or tweak the filtering thresholds. These options can be displayed using hicstuff filter -h
Matrix generation
A Hi-C sparse contact matrix can then be generated using the python submodule hicstuff.pipeline
. The matrix can be generated either in GRAAL format, or bedgraph2 (WIP) for cooler compatibility.
from hicstuff import pipeline as hpi
n_frags = sum(1 for line in open(fragments_list, "r")) - 1
hpi.pairs2matrix("output_filtered.pairs", "abs_fragments_contacts_weighted.txt", n_frags, format="GRAAL")
File formats
- pairs files: This format is used for all intermediate files in the pipeline and is also used by
hicstuff filter
. It is a space-separated format holding informations about Hi-C pairs. It has an official specification defined by the 4D Nucleome data coordination and integration center. - 2D bedgraph: This is an optional output format of
hicstuff pipeline
for the sparse matrix. It has two fragment per line, and the number of times they are found together. It has the following fields: chr1, start1, end1, chr2, start2, end2, occurences - GRAAL sparse matrix: This is a simple tab-separated file with 3 columns: frag1, frag2, contacts. The id columns correspond to the absolute id of the restriction fragments (0-indexed). The first row is a header containing the number of rows, number of columns and number of nonzero entries in the matrix. Example:
564 564 6978
0 0 3
1 2 4
1 3 3
- fragments_list.txt: This tab separated file provides information about restriction fragments positions, size and GC content. Note the coordinates are 0 point basepairs, unlike the pairs format, which has 1 point basepairs. Example:
- id: 1 based restriction fragment index within chromosome.
- chrom: Chromosome identifier. Order should be the same as in info_contigs.txt or pairs files.
- start_pos: 0-based start of fragment, in base pairs.
- end_pos: 0-based end of fragment, in base pairs.
- size: Size of fragment, in base pairs.
- gc_content: Proportion of G and C nucleotide in the fragment.
id chrom start_pos end_pos size gc_content
1 seq1 0 21 21 0.5238095238095238
2 seq1 21 80 59 0.576271186440678
3 seq1 80 328 248 0.5201612903225806
- info_contigs.txt: This tab separated file gives information on contigs, such as number of restriction fragments and size. Example:
- contig: Chromosome identified. Order should be the same in pairs files or fragments_list.txt.
- length: Chromosome length, in base pairs.
- n_frags: Number of restriction fragments in chromosome.
- cumul_length: Cumulative length of previous chromosome, in base pairs.
contig length n_frags cumul_length
seq1 60000 409 0
seq2 20000 155 409
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