Getting Started

git clone https://github.com/haowenz/chromap.git
cd chromap && make
# create an index first and then map
./chromap -i -r test/ref.fa -o ref.index
./chromap -x ref.index -r test/ref.fa -1 test/read1.fq -2 test/read2.fq -o test.bed
# use presets (no test data)
./chromap --preset atac -x index -r ref.fa -1 read1.fq -2 read2.fq -o aln.bed       # ATAC-seq reads
./chromap --preset atac -x index -r ref.fa -1 read1.fq -2 read2.fq -o aln.bed \
 -b barcode.fq.gz --barcode-whitelist whitelist.txt                                       # scATAC-seq reads
./chromap --preset chip -x index -r ref.fa -1 read1.fq -2 read2.fq -o aln.bed       # ChIP-seq reads
./chromap --preset hic -x index -r ref.fa -1 read1.fq -2 read2.fq -o aln.pairs      # Hi-C reads and pairs output
./chromap --preset hic -x index -r ref.fa -1 read1.fq -2 read2.fq --SAM -o aln.sam  # Hi-C reads and SAM output

Table of Contents

• Getting Started
• User Guide
  • Installation
  • General usage
  • Use cases
    • Map ChIP-seq short reads
    • Map ATAC-seq/scATAC-seq short reads
    • Map Hi-C short reads
  • Getting help
  • Citing Chromap

User Guide

Chromap is an ultrafast method for aligning and preprocessing high throughput chromatin profiles. Typical use cases include: (1) trimming sequencing adapters, mapping bulk ATAC-seq or ChIP-seq genomic reads to the human genome and removing duplicates; (2) trimming sequencing adapters, mapping single cell ATAC-seq genomic reads to the human genome, correcting barcodes, removing duplicates and performing Tn5 shift; (3) split alignment of Hi-C reads against a reference genome. In all these three cases, Chromap is 10-20 times faster while being accurate.

Installation

To compile from the source, you need to have the GCC compiler with version>=7.3.0, GNU make and zlib development files installed. Then type make in the source code directory to compile.

Chromap is also available on bioconda. Thus you can easily install Chromap with Conda

conda install -c bioconda -c conda-forge chromap

General usage

Before mapping, an index of the reference needs to be created and saved on the disk:

chromap -i -r ref.fa -o index

The users can input the min fragment length expected in their sequencing experiments, e.g. read length, by --min-frag-length. Then Chromap will choose proper k-mer length and window size to build the index. For human genome, it only takes a few minutes to build the index. Without any preset parameters, Chromap takes a reference database and a query sequence file as input and produce approximate mapping, without base-level alignment in the BED format:

chromap -x index -r ref.fa -1 query.fq -o approx-mapping.bed

You may ask Chromap to output alignments in the SAM format:

chromap -x index -r ref.fa -1 query.fq --SAM -o alignment.sam

But note that the the processing of SAM files is not fully optimized and can be slow. Thus generating the output in SAM format is not preferred and should be avoided when possible. Chromap can take multiple input read files:

chromap -x index -r ref.fa -1 query1.fq,query2.fq,query3.fq --SAM -o alignment.sam

Chromap also supports wildcards in the read file names and will find all matched read files. To use this function, the read file names must be put in quotation marks:

chromap -x index -r ref.fa -1 "query*.fq" --SAM -o alignment.sam

Chromap works with gzip'd FASTA and FASTQ formats as input. You don't need to convert between FASTA and FASTQ or decompress gzip'd files first.

Importantly, it should be noted that once you build the index, indexing parameters such as -k, -w and --min-frag-length can't be changed during mapping. If you are running Chromap for different data types, you will probably need to keep multiple indexes generated with different parameters. This makes Chromap different from BWA which always uses the same index regardless of query data types. Chromap can build the human genome index file in a few minutes.

Detailed explanations for the options can be found at the manpage.

Use cases

To support different data types (e.g. ChIP-seq, Hi-C, ATAC-seq), Chromap needs to be tuned for optimal performance and accuracy. It is usually recommended to choose a preset with option --preset, which sets multiple parameters at the same time.

Map ChIP-seq short reads

chromap --preset chip -x index -r ref.fa -1 read1.fq.gz -2 read2.fq.gz -o aln.bed      # ChIP-seq reads

This set of parameters is tuned for mapping ChIP-seq reads. Chromap will map the paired-end reads with max insert size up to 2000 (-l 2000) and then remove duplicates (--remove-pcr-duplicates) using the low memory mode (--low-mem). The output is in BED format (--BED). In the output BED file, each row is a mapping of a fragment (i.e., a read pair) and the columns are

chrom chrom_start chrom_end N mapq strand

The strand here is the strand of the first read in a read pair (specified by -1). If the mapping start and end locations of each read in a read pair are desired, --TagAlign should be used to overide --BED in the preset parameters as following

chromap --preset chip -x index -r ref.fa -1 read1.fq.gz -2 read2.fq.gz --TagAlign -o aln.tagAlign      # ChIP-seq reads

For each read pair, there will be two rows in the output file, one for each read in the pair respectively. The meaning of the columns remains the same.

Map ATAC-seq/scATAC-seq short reads

chromap --preset atac -x index -r ref.fa -1 read1.fq.gz -2 read2.fq.gz -o aln.bed      # ATAC-seq reads
chromap --preset atac -x index -r ref.fa -1 read1.fq.gz -2 read2.fq.gz -o aln.bed\
 -b barcode.fq.gz --barcode-whitelist whitelist.txt                                    # scATAC-seq reads

This set of parameters is tuned for mapping ATAC-seq/scATAC-seq reads. Chromap will trim the adapters on 3' end (--trim-adapters), map the paired-end reads with max insert size up to 2000 (-l 2000) and then remove duplicates at cell level (--remove-pcr-duplicates-at-cell-level). Tn5 shift will also be applied to the fragments (--Tn5-shift). The forward mapping start positions are increased by 4bp and the reverse mapping end positions are decreased by 5bp. The processing is run in the low memory mode (--low-mem).

If no barcode whitelist file is given, Chromap will skip barcode correction. When barcodes and a whitelist are given as input, by default Chromap will estimate barcode abundance and use this information to perform barcode correction with up to 1 Hamming distance from a whitelist barcode. By setting --bc-error-threshold to 2, Chromap is able to correct barcodes with up to 2 Hamming distance from a whitelist barcode. User can also increase the probability threshold to make a correction by setting --bc-probability-threshold (set to 0.9 by default) to a large value (e.g., 0.975) to only make reliable corrections. For scATAC-seq data with multiple read and barcode files, you can use "," to concatenate multiple input files as the example above.

Chromap also supports user-defined barcode format, including mixed barcode and genomic data case. User can specify the sequence structure through option --read-format. The value is a comma-separated string, each field in the string is also a semi-comma-splitted string

[r1|r2|bc]:start:end:strand

The start and end are inclusive and -1 means the end of the read. User may use multiple fields to specify non-consecutive segments, e.g. bc:0:15,bc:32:-1. The strand is presented by '+' and '-' symbol, if '-' the barcode will be reverse-complemented after extraction. The strand symbol can be omitted if it is '+' and is ignored on r1 and r2. For example, when the barcode is in the first 16bp of read1, one can use the option -1 read1.fq.gz -2 read2.fq.gz --barcode read1.fq.gz --read-format bc:0:15,r1:16:-1.

The output file formats for bulk and single-cell data are different except for the first three columns. For bulk data, the columns are

chrom chrom_start chrom_end N mapq strand duplicate_count

For single-cell data, the columns are

chrom chrom_start chrom_end barcode duplicate_count

the same as the definition of the fragment file in CellRanger. Note that chrom_end is open-end. This output fragment file can be used as input of downstream analysis tools such as MAESTRO, ArchR, signac and etc.

Besides, Chromap can translate input cell barcodes to another set of barcodes. Users can specify the translation file through the option --barcode-translate. The translation file is a two-column tsv/csv file with the translated barcode on the first column and the original barcode on the second column. This is useful for 10x Multiome data, where scATAC-seq and scRNA-seq data use different sets of barcodes. This option also supports combinatorial barcoding, such as SHARE-seq. Chromap can translate each barcode segment provided in the second column to the ID in the first column and add "-" to concatenate the IDs in the output.

Map Hi-C short reads

chromap --preset hic -x index -r ref.fa -1 read1.fa -2 read2.fa -o aln.pairs           # Hi-C reads and pairs output

Chromap will perform split alignment (--split-alignment) on Hi-C reads and output mappings in pairs format (--pairs), which is used in 4DN Hi-C data processing pipeline. Some Hi-C data analysis pipelines may require the reads are sorted in specific chromosome order other than the one in the index. Therefore, Chromap provides the option --chr-order to specify the alignment order, and --pairs-natural-chr-order for flipping the pair in the pairs format.

Getting help

Detailed description of Chromap command line options and optional tags can be displayed by running Chromap with -h or be found at the manpage. If you encounter bugs or have further questions or requests, you can raise an issue at the issue page.

Citing Chromap

If you use Chromap, please cite:

Zhang, H., Song, L., Wang, X., Cheng, H., Wang, C., Meyer, C. A., ... & Li, H. (2021). Fast alignment and preprocessing of chromatin profiles with Chromap. Nature communications, 12(1), 1-6. https://doi.org/10.1038/s41467-021-26865-w