Genome Alignment And Contact Matrix Construction

genome-alignment-and-contact-matrix-construction

Summary

Process raw Hi-C FASTQ sequencing data through read alignment, deduplication, and normalization to generate kilobase-resolution contact matrices in .hic format. This skill is essential when starting from FASTQ reads and needing to construct quantitative genome-wide interaction maps for downstream analysis.

When to use

You have raw Hi-C FASTQ files from a kilobase-resolution Hi-C experiment and need to produce a processed Hi-C contact map (.hic file) for visualization, loop calling, or chromatin structure analysis. This is the entry point for any Hi-C dataset that has not yet been aligned and normalized.

When NOT to use

• Input Hi-C data is already in .hic format or pre-processed contact matrix form — use downstream feature annotation tools instead.
• You have single-end Hi-C reads or non-standard pairing; Juicer is optimized for paired-end sequencing.
• Your restriction enzyme or genome is not supported without custom configuration of the pipeline.

Inputs

• Raw Hi-C FASTQ files (paired-end sequencing reads)
• Reference genome FASTA file
• Chromosome sizes file (chrom.sizes)
• Restriction enzyme recognition site file

Outputs

• .hic file (processed Hi-C contact map with normalized interactions)
• Intermediate alignment files (in aligned/ directory)
• Pipeline statistics and logs

How to apply

Clone the Juicer repository (either stable Juicer 1.6 or development Juicer 2 depending on your requirements) and configure the pipeline with your reference genome, restriction enzyme recognition site, and computational resources (threads and memory allocation). Organize raw FASTQ files in a designated fastq/ subdirectory and run juicer.sh with appropriate flags (e.g., -g for genome ID, -y for restriction site file, -z for reference genome). The pipeline automatically handles read alignment via BWA, contact matrix construction, and normalization, producing a .hic output file. Verify success by confirming the .hic file is generated without errors and contains valid Hi-C contact data.

Related tools

• Juicer (Primary pipeline for Hi-C FASTQ-to-.hic processing, including read alignment, contact matrix construction, and normalization) — https://github.com/aidenlab/juicer
• Juicer 1.6 (Stable release version of Juicer for production Hi-C processing) — https://github.com/aidenlab/juicer/releases/tag/1.6
• Juicer 2 (Development version of Juicer with active updates; clone directly from GitHub repository) — https://github.com/aidenlab/juicer
• ENCODE Hi-C uniform processing pipeline (Dockerized, cloud-ready implementation of Juicer-based Hi-C processing for AWS, Google Cloud, and local deployment) — https://github.com/ENCODE-DCC/hic-pipeline
• BWA (Burrows-Wheeler Aligner) (Performs read alignment of Hi-C FASTQ data to reference genome during pipeline execution) — http://bio-bwa.sourceforge.net/
• Juicer Tools (Command-line utilities for feature annotation and post-processing of .hic files after contact matrix construction) — https://github.com/theaidenlab/juicer/wiki/Download

Examples

juicer.sh -g hg19 -d /path/to/work -y /path/to/restriction_sites.txt -z /path/to/hg19.fa

Evaluation signals

• A valid .hic file is produced in the output directory without pipeline errors or crashes.
• The .hic file contains non-zero contact counts and can be opened and visualized in Juicebox or equivalent Hi-C browser.
• Pipeline statistics (merged_nodups file, alignment rates) show reasonable mapping efficiency (>50% expected for Hi-C).
• The contact matrix exhibits expected Hi-C topology: diagonal enrichment, distance-decay relationship, and compartmentalization patterns.
• Intermediate files (merged_sort, merged_nodups) progress through all pipeline stages (chimeric → merge → dedup → final) without truncation or data loss.

Limitations

• Juicer requires significant computational resources (ideally ≥4 cores and ≥64 GB RAM; minimum 1 core and 16 GB RAM) and is optimized for cluster execution; single-CPU runs are supported but slow.
• AWS deployment scripts are deprecated; ENCODE's cloud-based pipeline is recommended for cloud environments.
• The pipeline requires manual configuration of restriction enzyme site files and reference genomes; unsupported genomes or enzymes need custom setup.
• CUDA support for HiCCUPS peak calling requires an NVIDIA GPU; CPU-only HiCCUPS is available but slower.
• Raw FASTQ file organization (must be in [topDir]/fastq/) and naming conventions must follow Juicer conventions or the pipeline will not auto-detect samples.

Evidence

• [readme] Juicer is a platform for analyzing kilobase resolution Hi-C data. In this distribution, we include the pipeline for generating Hi-C maps from fastq raw data files: "pipeline for generating Hi-C maps from fastq raw data files"
• [other] task_id=task_001 workflow step 4: "Execute the Juicer pipeline on the raw FASTQ files to perform read alignment, contact matrix construction, and normalization."
• [other] task_id=task_001 workflow step 5: "Verify that the pipeline completes successfully and produces a .hic output file containing the processed Hi-C contact map."
• [readme] README section Hardware and Software Requirements: "Juicer requires the use of a cluster or the cloud, with ideally >= 4 cores (min 1 core) and >= 64 GB RAM (min 16 GB RAM)"
• [readme] README Quick Start section: "Run the Juicer pipeline on your cluster of choice with "juicer.sh [options]""
• [readme] README intro section: "If you clone the Juicer repo directly from Github, it will clone Juicer 2, which is under active development."
• [other] task_id=task_001 workflow step 3: "Configure the Juicer pipeline with appropriate parameters for the reference genome, restriction enzyme, and computational resources"
• [readme] README Cluster requirements: "Juicer currently works with the following resource management software: OpenLava, LSF, SLURM, GridEngine"