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Unique read names required The small RNA reads must all have unique names within a given file. If this requirement is not met, alignments will be completely unreliable due to errors in interpreting and handling of multi-mapped reads.

Adapter trimming ShortStack has a primitive 3'-adapter capability. Specify an adapter of at least 8nts in length with option --adapter.

If nothing is given to --adapter, ShortStack assumes your reads are already trimmed. Trimming simply looks for the right-most exact match to the given apdater sequence, and when found, chops it off.

If a read is smaller than 15nts after trimming, it is discarded. For more sophisticated adapter trimming, consider cutadapt or trimmomatic If quality values are present, they are trimmed as well.

Alignment overview ShortStack uses bowtie to align reads. It first aligns, and processes the output on the fly to note how many equally good alignment positions were found for each read.

It then uses this information in a second phase to 'decide' on the most likely 'correct' location for multi-mapped reads.

The final output is a single. If multiple readfiles were input, the final bam or cram file notes the origin of each read with the RG tag see sam format specification.

This helps with sequencing errors and SNPs. If a read has some alignments with 0 mismatches, and some with 1, only those with 0 mismatches are kept.

The option --mismatches controls this threshold, and can be set to 0, 1, or 2. To get around this, when aligning to a 'large' reference, ShortStack forces the number of allowed mismatches to be 0.

The default setting is The choice of method is specified with the option --mmap. The methods are: u: Placement guided by uniquely mapping reads.

During the alignment, the count of uniquely mapped reads is kept in 50nt bins across the reference genome. The bin location is determined by the left-most coordinate of the uniquely mapped read.

After the first phase of alignment for all reads in all files has completed, this genome-wide map of uniquely-mapped read counts is used to guide the decisions of the most likely locations of multi-mapped reads.

Specifically, for a given multi-mapped read, the local count of uniquely mapped reads at each possible location is computed. The local count is that of the specific 50nt bin the alignment lies in again, by left-most positon plus the counts of the 2 bins upstream and 2 bins downstream.

All of the local counts are converted to fractions of the sum of all total counts. These fractions are then used as the probabilities of placement for the multi-mapped read.

For instance, suppose a multi-mapped read had three possible positions. The read counts of uniquely mapped reads were 30, 65, and 5. The actual choice is probabilistic, given the computed weightings, for each read.

Like u, except that multi-mapped reads also contribute to the guidance densities. This is faster than u and f, but performs much more poorly at properly placing multi-mapped reads.

Achieves high sensitivity, but very low precision. Very fast, but ignores large quantities of data. Achieves high precision, but very low sensitivity.

The default setting for --mmap is u ranmax When running mmap method u or f, there are some cases where no guidance can be given, and so the choice on where to put a multi-mapped read is still random.

In those cases, the option ranmax will suppress any alignment where the choice is 'too' random. By default, --ranmax is set at 3, so that if a read can't be placed confidently, no placement is done if there are more than 3 choices.

Alignment output format Final alignments are sorted bam or cram formatted alignments. XX:i tags: Added by ShortStack to each line, this indicates the total number of valid alignments found for the read.

XZ:f tags: Added by ShortStack to each line, this indicates the calculated probability of placement for the read.

Any properly formatted bam or cram file should work with ShortStack, subject to the requirements below. However, for best performance, it is recommended to use ShortStack for alignments as well.

Requirements for input bam or cram files: 1. Header must be present, and contain SQ lines. File most be sorted. Read groups will not be recognized unless they are properly noted in the header.

Paired-end, spliced, clipped, padded, or gapped alignments will be ignored, with a warning to the user. Reads marked as secondary alignments bit set in the FLAG will not be used.

The mincov threshold can also be specified in terms of reads per million by using a value such as 3. Using a rpm threshold allows the sensitivity of cluster discovery to be normalized between libraries of different sizes.

Alternatively, reads per million mapped rpmm can be specified: A --mincov of 1. Only small RNAs with an abundance higher than the limit set by option --mincov are reported.

These small RNAs typically inlcude RNAs that could not be aligned anywhere on the reference, as well as multi-mapped RNAs where ShortStack did not choose a alignment position for see alignment methods.

For just one or a few loci, the option --locus can be used. Un 1 seul prix par personne ou par foyer. Header Image. Contest Description.

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