HAMRWelcome to HAMR!
HAMR (High-throughput Annotation of Modified Ribonucleotides) is a web application that allows you to detect and classify modified nucleotides in RNA-seq data.
To get started, click on the 'User Input' tab, or click here for a sample input.
User input
RNA-seq data
Please supply read alignments as a web accessible BAM file. The file must consist of uniquely mapped reads, sorted by chromosome and position.
Bam file URL:
Reference genome:
Analyze specific genomic region
If you would like to limit the analysis to a particular region, enter it here: (chr:start-end)
or upload a BED file containing a list of intervals to analyze:
Detect modifications
Minimum coverage (reads):
Minimum base quality score, Q:
Base-call error rate:
Filter out read-ends
Null hypothesis:
Significance threshold:
Max P-Value:
Max FDR:
Classify modifications
Predict modification types
using model:
Polymorphisms
Overlap with human SNPs (dbSNP 135):
The offline version of HAMR is available on GitHub here:
For publication, please cite:
- Ryvkin P, Leung YY, Silverman IM, et al. HAMR: high-throughput annotation of modified ribonucleotides. RNA. 2013;19(12):1684-1692. doi:10.1261/rna.036806.112
- Kuksa P.P., Leung Y.Y., Vandivier L.E., Anderson Z., Gregory B.D., Wang LS. (2017) In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR. In: Lusser A. (eds) RNA Methylation. Methods in Molecular Biology, vol 1562. Humana Press, New York, NY
Help topics
HAMR scans RNA-sequencing data for sites showing potential signatures of nucleotide modification. Simply point it to your RNA-seq data it will scan the entire transcriptome. You can also limit the analysis to particular genomic regions of interest, either by entering one or providing a BED file containing the intervals. The output is a table containing the list of sites with nucleotide patterns that deviate from expectation at a statistically significant rate.
HAMR supports BAM format alignment files and can retrieve them from the web. Specify the URL of your web-accessible file.
You may either enter a region manually in the form "chromosome:start-end" and specify the strand, or you may upload a BED file specifying the intervals.
Options for detecting modifications
Options for detecting modifications Here you specify various options for the detection of modified sites. Minimum coverage: The minimum number of reads covering a site in order for it to be considered Minimum base quality score: Threshold bases by the base-call quality score reported by the sequencer. This is a value from 0 to 40. Base-call error rate: The assumed rate at which base-calling errors occur in the data. Increasing this value will make HAMR more conservative in calling modified sites. Null hypothesis: The null hypothesis to use for the statistical test. H01: Calls significant any site which is inconsistent with a homozygous reference genotype. In this mode HAMR is more sensitive to modified sites but may also pick up SNPs (false positives) and RNA-edits. This mode is recommended only if your data comes from a sample with a very low rate of polymorphisms, e.g. a model organism of the same strain as the reference genome. H02: Calls significant any site which is inconsistent with a biallelic genotype. While more conservative, in this mode HAMR is less likely to call SNPs as modified sites. It will also miss RNA-edits and modifications which produce single nucleotide patterns in cDNA, such as inosine. It will largely detect modifications that induce random RT misincorporation. Significance threshold: p-value: Uses a simple p-value threshold to call sites statistically significant. Sites with p-value less than the specified value will be called significant. Benjamini-Hochberg FDR: Uses multiple testing correction to control the False Discovery Rate at the specified value.
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