High Throughput Annotation of Modified Ribonucleotides

HAMR detects modified ribonucleotides based upon their ability to intefere with Watson-Crick base pairing. This leads to reverse transcriptase base misincorporations, which can be detected as mismatchea from the reference genome in cDNA-based RNA-seq libraries. HAMR tabulates these mismatches and tests for patterns of mismatches that cannot be explained by:

1. Sequencing errors
2. Single nucleotide polymorphisms (SNPs)
3. RNA editing

HAMR's assumptions only hold true for a haploid or diploid genome.

HAMR input consists of coordinate-sorted mapped reads in BAM format, and a reference genome in FASTA format. Reads should be mapped with at least 1 mismatch, and we suggest at most floor(0.06 * mean read length) mismatches. HAMR outputs predicted modification sites, and predicted modification identity.

Please cite the following papers when using HAMR:

1. HAMR: high-throughput annotation of modified ribonucleotides
2. Chemical Modifications Mark Alternatively Spliced and Uncapped Messenger RNAs in Arabidopsis

Installing HAMR

HAMR includes pre-compiled binary programs. If neccessary, to re-compile use

make clean

make

Preparing Mapped Reads for HAMR

Spliced alignments (with N in CIGAR string) in BAM files will be ignored. Alignments with insertion/deletions will be ignored. Only continuous, un-interrupted read alignments will be used. Any spliced alignments should be resolved (e.g., by splitting) before running HAMR, e.g., using GATK

java -jar GenomeAnalysisTK.jar -T SplitNCigarReads -R <genome.fa> -I <input.bam> -o <input.splitN.bam> -U ALLOW_N_CIGAR_READS

Operating instructions

Python with version v2.7.x is preferred. Python 3+ is not yet supported. Rscript and samtools are required for running HAMR (make sure they are in searchable path).

Usage

python hamr.py [align.bam] [genome.fa] [prediction_model_file] [output_dir] [output_prefix] [min_read_qual] [min_read_coverage] [seq_error_rate] [hypothesis] [max_p] [max_fdr] [min_ref_percent] [OPTIONS]

Examples

download reference genome (e.g., hg19)

wget http://tesla.pcbi.upenn.edu/hamr/genomes/hg19_all_chr.fas

run HAMR in genome-wide mode

python hamr.py trial.human.bam  genomes/hg19_all_chr.fas models/euk_trna_mods.Rdata HAMRtest human 30 10 0.05 H4 0.01 0.05 0.05

run HAMR in target mode (BED-restricted)

python hamr.py trial.human.bam  genomes/hg19_all_chr.fas models/euk_trna_mods.Rdata HAMRtest human_region 30 10 0.05 H4 0.01 0.05 0.05 --target_bed region.human.bed

REQUIRED arguments

<align.bam>
	input BAM file
<genome.fa>
	reference genome FASTA file (should be the same as the genome file used in mapping (input BAM). NOTE: remember to index the reference genome using samtools faidx before running HAMR.
<min_read_qual>
	the minimum base calling quality score. All low-quality bases will removed from analysis
<min_read_cov>
	the minimum read coverage of a genomic position for it to be analyzed
<seq_error_rate>
	The expected percentage of mismatches based solely on sequencing error
<hypothesis>
	The hypothesis to be tested, either "H1" or "H4" (see HAMR paper. H4 is recommended).
<max_p>
	the maximum p-value cutoff. All sites with P-value><max_p> will be filtered out.
<max_fdr>
	the maximum FDR cutoff
<refpercent>
	the minimum percentage of reads that must match the reference nucleotide. All sites with reference read nucleotide proportion < <refpercent> will be filtered out
<output_dir>
	directory for the HAMR output
    <output_prefix>
	prefix for HAMR output files

OPTIONS

--target_bed <targets.bed>
	BED file with regions of interest for HAMR analysis (this replaces the default genome-wide HAMR mode and restricts HAMR analysis to specific genomic regions listed in BED)
--paired_ends, -pe
	indicates paired-end sequencing was used 
--filter_ends,-fe
	excludes the first and last positions in the read from the analysis

Copyright

Copyright (c) 2013-2018 University of Pennsylvania

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