SLAM-seq is a novel sequencing protocol that directly uncovers 4-thiouridine incorporation events in RNA by high-throughput sequencing. When combined with metabolic labeling protocols, SLAM-seq allows to study the intracellular RNA dynamics, from transcription, RNA processing to RNA stability.
Original publication: Herzog et al., Nature Methods, 2017; doi:10.1038/nmeth.4435
To analyze a given SLAM-seq dataset with slamdunk without recovering multimappers, you only need to provide the following files and keep everything else to the default parameters.
|Genome||The reference fasta file (Genome assembly).|
|Reference||BED-file containing coordinates for 3' UTRs.|
|Reads||Sample FASTQ(gz) files.|
|Read length||Maximum length of reads (usually 50, 100, 150).|
This will run the entire slamdunk analysis (slamdunk all) with the most relevant output files being:
These files can be input to the Alleyoop tool for visualization and further processing. See the Slamdunk documentation for more information.
To utilize multimapper recovery, modify the following parameters. You must either choose a separate 3' UTR file or activate filtering on the supplied reference file. Will only yield different results than a unique-mapping run by specifying a number > 1 as maximum number of multimapper aligments to consider.
|Maximum number of alignments to report per read||The maximum number of multimapper alignments to consider.|
|Use separate 3' UTR bed to filter multimappers.||3' UTR bed file to filter.|
|Use reference bed file to resolve multimappers.||Use reference as 3' UTR bed file to filter.|
Depending on the use case, more stringent or more lenient measures of T>C conversion and T>C reads are required such as 2 T>C by Muhar et al., Science, 2018; http://doi.org/10.1126/science.aao2793
This can be controlled by the following parameter:
|T>C conversion threshold||Minimum number of T>C conversions to consider a read as T>C read.|