ShortStack

Alignment of small RNA-seq data and annotation of small RNA-producing genes

Author

Michael J. Axtell, Penn State University, mja18@psu.edu

Table of Contents

• Citations
• Installation
• Usage
• Resources
• Testing and Examples
• Outputs
• Visualizing Results
• Overview of Methods
• How to go FAST
• ShortStack Version 4 Major Changes
• Issues
• FAQ

Citations

If you use ShortStack in support of your work, please cite one or more of the following:

• Johnson NR, Yeoh JM, Coruh C, Axtell MJ. (2016). G3 6:2103-2111. doi:10.1534/g3.116.030452
• Shahid S., Axtell MJ. (2013) Identification and annotation of small RNA genes using ShortStack. Methods doi:10.1016/j.ymeth.2013.10.004
• Axtell MJ. (2013) ShortStack: Comprehensive annotation and quantification of small RNA genes. RNA 19:740-751. doi:10.1261/rna.035279.112

Installation

You can either use the conda package manager to install from the bioconda channel, or manually set up an environment. Use of conda/bioconda is highly recommended!

Install using conda (recommended)

First, install conda, and then set it up to use the bioconda channel following the instructions at https://bioconda.github.io

Then, follow instructions below based on your system to install the dependencies to a new environment and activate the environment.

Linux or Intel-Mac

conda create --name ShortStack4 shortstack 
conda activate ShortStack4

Silicon-Mac

Some dependencies have not been compiled for the newer Silicon-based Macs on bioconda, so we need to force conda to install the osx-64 (Intel) versions instead. Silicon Macs can run Intel code using built-in Rosetta translation.

conda create --name ShortStack4
conda activate ShortStack4
conda config --env --set subdir osx-64
conda install shortstack

Manual installation

Create an environment that contains the following packages / tools compiled and installed:

• python >= 3.10.8 https://www.python.org
• samtools >= 1.16 https://www.htslib.org
• bowtie >= 1.3.1 https://bowtie-bio.sourceforge.net/index.shtml
• viennarna 2.* https://www.tbi.univie.ac.at/RNA/documentation.html
• tqdm https://tqdm.github.io
• numpy https://numpy.org
• biopython https://biopython.org
• strucVis https://github.com/MikeAxtell/strucVis
• ShortTracks https://github.com/MikeAxtell/ShortTracks
• bedtools https://bedtools.readthedocs.io/en/latest/
• cutadapt https://cutadapt.readthedocs.io/en/stable/

Then, download the ShortStack script from this github repo. Make it executable chmod +x ShortStack and then copy it into your environment's PATH.

Usage

ShortStack [-h] [--version] --genomefile GENOMEFILE [--known_miRNAs KNOWN_MIRNAS]
    (--readfile [READFILE ...] | --bamfile [BAMFILE ...]) [--outdir OUTDIR] [--adapter ADAPTER | --autotrim]
    [--autotrim_key AUTOTRIM_KEY] [--threads THREADS] [--mmap {u,f,r}] [--align_only] [--show_secondaries]
    [--dicermin DICERMIN] [--dicermax DICERMAX] [--locifile LOCIFILE | --locus LOCUS] [--nohp] [--dn_mirna]
    [--strand_cutoff STRAND_CUTOFF] [--mincov MINCOV] [--pad PAD] [--no_bigwigs]

Required

• --genomefile GENOMEFILE : Path to the reference genome in FASTA format. Must be indexable by both samtools faidx and bowtie-build, or already indexed.
• (--readfile [READFILE ...] | --bamfile [BAMFILE ...]) : Either --readfile or --bamfile is required.
  • --readfile [READFILE ...] : Path(s) to one or more files of reads in fastq or fasta format. May be gzip compressed. Multiple files are separated by spaces. Inputting reads triggers alignments to be performed.
  • --bamfile [BAMFILE ...] : Path(s) to one or more files of aligned sRNA-seq data in BAM format. Multiple files are separated by spaces. BAM files must match the reference genome given in --genomefile.

Recommended

• --known_miRNAs KNOWN_MIRNAS : Path to FASTA-formatted file of known mature miRNAs. FASTA must be formatted such that a single RNA sequence is on one line only. ATCGUatcgu characters are acceptable. These RNAs are typically the sequences of known microRNAs; for instance, a FASTA file of mature miRNAs pulled from https://www.mirbase.org. These known miRNA sequences are aligned to the genome and used to nucleate searches for loci that meet all expression-based and secondary structure-based requirements for MIRNA locus identification. See also option --dn_mirna.
• --outdir OUTDIR : Specify the name of the directory that will be created for the results.
  • default: ShortStack_[time], where [time] is the Unix time stamp according to the system when the run began.
• --autotrim : This is strongly recommended when supplying untrimmed reads via --readfile. The autotrim method automatically infers the 3' adapter sequence of the untrimmed reads, and the uses that to coordinate read trimming. However, do not use --autotrim if your input reads have already been trimmed!
  • Note: autotrim currently assumes your library strategy generated reads where nucleotide 1 of the read is the first biological / sRNA-derived nucleotide, and the 3' adapter starts immediately after the last sRNA nucleotide. It further assumes there are no random nucleotides (Ns) in the 3' adapter sequence. If your data do not meet these assumptions you cannot use --autotrim. Instead, remove your adapters by other appropriate methods and input the trimmed reads using --readfile without option --autotrim.
  • Note: mutually exclusive with --adapter.
• --threads THREADS : Set the number of threads to use. More threads = faster completion.
  • default: 1

Other options

• -h : Print a help message and then quit.
• --version : Print the version and then quit.
• --adapter ADAPTER : Manually specify a 3' adapter sequence to use during read trimming. Mutually exclusive with --autotrim. The --adapter option will apply the same adapter sequence to trim all given readfiles.
  • Note: Use of --adapter is discouraged. In nearly all cases, --autotrim is a better bet for read trimming.
• --autotrim_key AUTOTRIM_KEY : A DNA sequence to use as a known suffix during the --autotrim procedure. ShortStack's autotrim discovers the 3' adapter by scanning for reads that begin with the sequence given by AUTOTRIM_KEY. This should be the sequence of a small RNA that is known to be highly abundant in all of the libraries. The default sequence is for miR166, a microRNA that is present in nearly all plants at high levels. For non-plant experiments, or if the default is not working well, consider providing an alternative to the default.
  • default: TCGGACCAGGCTTCATTCCCC (miR166)
• --mmap {u,f,r} : Sets the mode by which multi-mapped reads are handled. These modes are described in Johnson et al. (2016). The default u mode has the best performance.
  • u : (Default) Only uniquely-aligned reads are used as weights for placement of multi-mapped reads.
  • f : Fractional weighting scheme for placement of multi-mapped reads.
  • r : Multi-mapped read placement is random.
• --align_only : This switch will cause ShortStack to terminate after the alignment phase; no analysis occurs.
• --show_secondaries : If this switch is set, ShortStack will retain secondary alignments for multimapped reads. This will increase bam file size, possibly by a lot.
• --dicermin DICERMIN : An integer setting the minimum size (in nucleotides) of a valid small RNA. Together with --dicermax, this option sets the bounds to discriminate Dicer-derived small RNA loci from other loci. >= 80% of the reads in a given cluster must be in the range indicated by --dicermin and --dicermax.
  • default: 21
• --dicermax DICERMAX : An integer setting the minimum size (in nucleotides) of a valid small RNA. Together with --dicermin, this option sets the bounds to discriminate Dicer-derived small RNA loci from other loci. >= 80% of the reads in a given cluster must be in the range indicated by --dicermin and --dicermax.
  • default: 24
• --locifile LOCIFILE : Path to a file of pre-determined loci to analyze. This will prevent de novo discovery of small RNA loci. The file may be in gff3, bed, or simple tab-delimited format (Chr:Start-Stop[tab]Name). Mutually exclusive with --locus.
• --locus LOCUS : A single locus to analyze, given as a string in the format Chr:Start-Stop (using one-based, inclusive numbering). This will prevent de novo discovery of small RNA loci. Mutually exclusive with --locifile.
• --nohp : Switch that prevents search for microRNAs. This saves computational time, but MIRNA loci will not be differentiated from other types of small RNA clusters.
• --dn_mirna : Switch that activates a de novo search for MIRNA loci. By default ShortStack will confine MIRNA analysis to loci where one or more queries from the --known_miRNAs file are aligned to the genome. Activating de novo searching with --dn_mirna does a more comprehensive genome-wide scan for MIRNA loci. Loci discovered with --dn_mirna that do not overlap already known microRNAs should be treated with caution.
• --strand_cutoff STRAND_CUTOFF : Floating point number that sets the cutoff for standedness. Must be > 0.5 and < 1.
  • default: 0.8. Loci with >80% reads on the top genomic strand are '+' stranded, loci with <20% reads on the top genomic strand are '-' stranded, and all others are unstranded '.'
• --mincov MINCOV : Minimum alignment depth, in units of reads per million, required to nucleate a small RNA cluster during de novo cluster search. Must be an floating point number > 0.
  • default: 1
• --pad PAD : Initial peaks (continuous regions with depth exceeding argument --mincov) are merged if they are this distance or less from each other. Must be an integer >= 1.
  • default: 200
• --no_bigwigs : Disable creation of bigwig files from sRNA-seq alignments. Applies only when performing alignments via input to --readfile.

Resources

Memory

Peak memory (RSS) primarily scales with genome size, and especially with the size of the largest chromosomes in the genome assembly. Generally between 4-10GB memory per thread is more than enough, but very large genomes should be monitored for memory overruns and requested memory adjusted accordingly. Because of the multi-threading, it is important to scale available memory with the number of threads being used.

Disk

During the alignment phase ShortStack will potentially write many large, but temporary, files to disk. These can easily exceed 100GB disk space especially when there are a lot of multi-mapping reads. Ensure that the location of --outdir has ample free disk space (plan on 200GB minimum to be safe).

CPU and running time

All compute-intensive parts of ShortStack are now multi-threaded. Providing more threads via --threads generally will decrease run-times in near-linear fashion. Be sure to scale memory with thread use though .. 4-10GB RAM per thread, depending on genome size, seems usually sufficient.

Read alignment is often the most time-consuming portion of the analysis. MIRNA identification (triggered by --known_miRNAs and/or --dn_mirna) is also time-consuming. Larger genomes generally run slower compared to smaller genomes. Highly fragmented genome assemblies (e.g. very high numbers of chromosomes/scaffolds) can be particularly slow because of the index lookup costs associated with thousands of entries. Consider obtaining and using better genome assemblies, or removing very short scaffolds from highly fragmented genome assemblies.

Testing and Examples

Gather Test Data

Reference Genome

For testing we will use the Arabidopsis thaliana TAIR10 genome assembly, including plastid and mitochondrial genomes. The Axtell Lab website is serving a copy of this that is easy to obtain:

curl https://plantsmallrnagenes.science.psu.edu/ath-b10/Arabidopsis_thalianaTAIR10.fa -ko Arabidopsis_thalianaTAIR10.fa

Raw sRNA-seq Reads

We can obtain some example A. thaliana sRNA-seq data from SRA using the SRA toolkit. If you don't already have the sra toolkit installed you can obtain it at https://github.com/ncbi/sra-tools/wiki/01.-Downloading-SRA-Toolkit

After installing the sra toolkit you should configure it .. see https://github.com/ncbi/sra-tools/wiki/03.-Quick-Toolkit-Configuration

tip I like to configure sra tools to prefetch and download to the working directory. That way I can remember to get rid of the pre-fetched directories when I have dumped the FASTQ.

Then you can use the standard prefetch and fasterq-dump method to retrieve the FASTQ files.

prefetch SRR3222443 SRR3222444 SRR3222445
fasterq-dump SRR3222443 SRR3222444 SRR3222445

You will now have 3 .fastq files of raw (untrimmed) sRNA-seq reads. These data are derived from Col-0 Arabidopsis thaliana immature inflorescence tissues (see Wang et al. 2017 https://doi.org/10.1111/tpj.13463)

Alternative using SRA run browser

Sometimes sra-tools is a pain to use. Alternatively, you can use the web interface at https://trace.ncbi.nlm.nih.gov/Traces/?view=run_browser&display=metadata to grab the example data, searching by SRR accession number, and downloading FASTQ.

Known miRNAs

To get a list of known miRNAs, we will use all miRBase annotated mature miRNAs from miRBase. First, download the mature.fa file from miRBase at https://mirbase.org/download/. Then filter it to get only the ath ones (e.g. the ones from A. thaliana).

grep -A 1 '>ath' mature.fa | grep -v '\-\-' > ath_known_miRNAs.fasta

Example Run

This example is a full run. It takes 3 raw (untrimmed) readfiles, identifies the adapters, trims the reads, indexes the genome, aligns the reads, discovers small RNA loci, and annotates high-confidence MIRNA loci. The example uses 6 threads; this can be adjusted up or down depending on your system's configuration; more threads decrease execution time but the response is non-linear (diminishing returns with very high thread numbers). The examples uses the test data described above.

ShortStack --genomefile Arabidopsis_thalianaTAIR10.fa --readfile SRR3222443.fastq SRR3222444.fastq SRR3222445.fastq --autotrim --threads 6 --outdir ExampleShortStackRun --known_miRNAs ath_known_miRNAs.fasta

On my laptop this completes in about 18 minutes. All results are in the directory specified by --outdir, "ExampleShortStackRun". The outputs are described in the section below called "Outputs".

Outputs

Results.txt

A tab-delimited text file giving key information for all small RNA clusters. Columns:

1. Locus: Coordinates of the locus in Chrom:Start-Stop format, one-based, inclusive.
2. Name: Name for the Locus. De novo loci are named like Cluster_1, etc. Loci provided by the user with option --locifile preserve the user-given names.
3. Chrom: Name of the chromosome
4. Start: One-based start position (left-most) of the locus, inclusive.
5. End: One-based end position (right-most) of the locus, inclusive.
6. Length: Length of the locus (base-pairs)
7. Reads: Number of aligned sRNA-seq reads that overlap this locus.
8. DistinctSequences: Number of distinct sRNA sequences that overlap this locus. A single DistinctSequence can have one or more reads.
9. FracTop: Fraction of Reads aligned to the top genomic strand.
10. Strand: Inferred strandednes of the locus, inferred from FracTop and the --strand_cutoff setting.
11. MajorRNA: Sequence of the single most abundant RNA sequence at the locus.
12. MajorRNAReads: Number of reads for the MajorRNA aligned to this locus.
13. Short: Number of reads with a size less than --dicermin aligned to the locus. By default these are reads < 21 nucleotides in length.
14. Long: Number of reads with a size greater than --dicermax aligned to the locus. By default these are reads > 24 nucleotides in length.
15. 21: Number of 21 nucleotide reads aligned to the locus.
16. 22: Number of 22 nucleotide reads aligned to the locus.
17. 23: Number of 23 nucleotide reads aligned to the locus.
18. 24: Number of 24 nucleotide reads aligned to the locus.
19. DicerCall: If >= 80% of all aligned reads are within the boundaries of --dicermin and --dicermax, than the DicerCall gives the size of most abundant small RNA size. If < 80% of the aligned reads are in the --dicermin and --dicermax boundaries, DicerCall is set to 'N'. Loci with a DicerCall of 'N' are unlikely to be small RNAs related to the Dicer-Like/Argonaute system of gene regulation.
20. MIRNA: Did the locus pass all criteria to be called a MIRNA locus? If so, 'Y'. If not, 'N'.
21. Known_miRNAs: Semicolon delimited list of user-provided known RNAs that aligned to the locus. If none, 'NA'.

Counts.txt

A tab-delimited text file giving the raw alignment counts for each locus in each separate sample. Only produced if there was more than one sRNA-seq file used to create alignments. This file is useful for downstream analyses, especially differential expression analysis.

alignment_details.tsv

A tab-delimited text file that gives details about small RNA-seq alignments as a function of mapping type, read length, and sample. This can be useful for plotting purposes and subsequent quality control of small RNA-seq data. It is only produced if alignments are performed.

• mapping_type
  • U: Uniquely mapped (not a multimapper).
  • P: Multimapper placed using the method set by option --mmap.
  • R: Multimapper placed at random.
  • H: Very highly multi-mapped read (>=50 hits).
  • N: Unmapped reads.

Results.gff3

Small RNA loci in the gff3 format. Suitable for use on genome browsers. For loci that are annotated as MIRNAs there will be an additional entry for the mature microRNA position. The 'score' column in the gff3 format stores the number of sRNA-seq aligned reads at that locus.

known_miRNAs.gff3

When the user provides known RNA sequences via --known_miRNAs, they are aligned to the reference genome. Every (perfect) alignment to the reference is stored and reported in the known_miRNAs.gff3 file. The score column shows the number of alignments that start and end at the exact coordinates and strand.

Important : known_miRNAs are aligned and shown in the known_miRNAs.gff3 file regardless of whether any empirical small RNA-seq data are found. Thus, expect to find entries with a score of 0; these are cases where no instances of the given knownRNA were aligned to that location in the genome.

strucVis/

The directory strucVis/ contains visualizations of each locus that was annotated as a MIRNA locus. These are made by the script strucVis. For each locus there is a postscript file and a plain-text file. Both show the coverage of aligned small RNA-seq data as a function of position, aligned with the predicted RNA secondary structure of the inferred MIRNA hairpin precursor. These files are meant for manual inspection of MIRNA loci.

mir.fasta

This is a FASTA formatted file containing hairpin, mature miRNA, and miRNA* sequences derived from ShortStack's identification of MIRNA loci. These are genomic sequences, and the genomic coordinates are noted in the FASTA header. ShortStack's determination of mature miRNA vs. miRNA* strands is based on abundance of alignments at that particular locus. These designations may not always be accurate for an entire MIRNA family .. sometimes one paralog can attract most of the true mature miRNA alignments, leaving the other paralogs with mostly true miRNA* alignments. Take care when performing annotations.

.fastq(.gz) file(s)

If raw reads were trimmed by ShortStack, the trimmed fastq files will be found. The names will have a lower-cased 't' appended to the front to signify "trimmed". If the input files were .gz compressed, then the trimmed files will be too.

.bam file(s)

If the ShortStack run was aligning reads, one or more .bam files will be found. The bam format stores large scale alignment data. Corresponding bam index files (.bam.csi) will also be found.

.bw (bigwig) files

If the ShortStack run was aligning reads, multiple .bw files in the bigwig format will be produced. These bigwig files are produced by the program ShortTracks and are useful for visualizing data on genome browsers (in particular JBrowse2). The scales of the bigwig files are normalized to reads per million; thus, multiple tracks can be compared.

Types of bigwig files produced by ShortStack:

• readlength/stranded: A set of 8 .bw files with suffixes in the format _x_y.bw
  • x : A number ('21', '22', '23-24') or the string 'other', indicating the sizes of sRNAs tracked in that file.
  • y : Either 'p' or 'm' for the plus or minus genomic strand.
• readgroup: Only produced when there are multiple samples in the alignment. A set of n .bw files, with one per read-group. Because values are normalized to reads-per-million, these tracks are directly comparable to each other.

The README for ShortTracks has details about how to load these data onto JBrowse2 using "multi-wiggle" tracks for nice visualization of sRNA-seq data.

Visualizing Results

Loci annotated as MIRNA can be visualized from the srucVis/ files. These show the predicted RNA secondary structures with the small RNA-seq read depth coverage.

Genome Browsers

The output of ShortStack is designed to work with genome browsers. Specifically, the files Results.gff3, known_miRNAs.gff3, the .bam files, and the .bw files can be directly visualized on either major genome browser (IGV, JBrowse).

JBrowse2 has the ability to create "multi-wiggle" tracks. These tracks show multiple quantitative data tracks at once, bound to a common quantitative axis. The .bw bigwig files created by ShortStack & ShortTracks are normalized to reads-per-million, allowing direct comparisons in a multi-wiggle track. This allows visualization of size, coverage, and strandedness of the data. See the README for ShortTracks for details. I recommend using the Desktop version of JBrowse2.

Overview of Methods

Read trimming

Read trimming using --autotrim assumes that the input FASTQ data comes from forward strand sequence reads where the first letter of the read corresponds to the first nucleotide of the sense-strand small RNA. Read trimming seeks to first identify the 3' adapter sequence used in the FASTQ file, and then to use the tool cutadapt to remove the adapters. Using the recommended --autotrim setting, the sequence of the 3' adapter is automatically detected. This is accomplished by looking for reads that begin with an --autotrim_key, which by default is the miR166 sequence 'TCGGACCAGGCTTCATTCCCC'. The adapter is inferred by looking at the sequences that follow the --autotrim_key. The cutadapt settings used for read trimming are cutadapt -a [adapter] -o [tInput.fastq] -j [--threads] --discard-untrimmed -m 12 --report minimal [Input.fastq]. These settings discard untrimmed reads (where no adapter was found), and only retained reads that, after trimming, are at least 12 nucleotides long.

Indexing

Two types of genome indices are required. The first is an .fai index, created by samtools faidx. The second is a bowtie index of the genome file, which comprises several files ending with .ebwtl. If either index is missing, ShortStack will attempt to create it. The bowtie index is created using the bowtie-build executable from bowtie using default settings and the number of threads given by the --threads argument. Any index files created by ShortStack are written to the same location as the --genomefile. Therefore, the --genomefile should be located in a writable directory if the required indices are not already built at the start of the run.

Alignments

Alignment of trimmed fastq data uses bowtie. There are usually two phases to alignment. In the first phase reads are mapped to the genome using bowtie settings bowtie -p [--threads] -v 1 -k 50 -S --best --strata -x [--genomefile] [trimmedFASTQ]. This allows zero or one mismatch hits, keeping only the zeroes if both zero and one-hit cases exist. All hits up to a maximum of 50 are stored for multi-mapping reads. In the second phase a single location for each of the multimapping reads is decided upon. This decision is made by one of the three possible methods set by --mmap : u, f, or r. Setting u uses a local weighting scheme set by only the uniquely mapping alignments in an area. Setting f uses a local weighting scheme that includes both the unique and multi-mapped possibilities in an area. Setting r just randomly takes one of the possible location as the reported one. These methods are fully described in Johnson et al., 2016. Each individual FASTQ file is processed and converted to a sorted BAM file. If more than one FASTQ file was input, a single sorted merged file, merged_alignments.bam is created.

Cluster discovery

Genomic intervals where the depth of small RNA coverage, in reads-per-million, is greater than or equal to --mincov (1 by default) are identified. Each of these intervals are then extended in both directions by the length given by setting --pad. Regions that overlap after extension are merged. Note that if one or more MIRNA loci are later found to overlap the intial cluster, the initial cluster is removed from the output (and only the refined, trimmed MIRNA region(s) are reported).

MIRNA annotation

MIRNA annotation has two entry points for initial searches: Locations of aligned user-provided sequences from --known_miRNAs and, if option --dn_mirna is True, any 21 or 22 nt read whose abundance exceeds the depth of --mincov. From these initial starting points, ShortStack first examines the local region to find miR/miR-star-like patterns of read accumulation (essentially, "two-peaks" of read coverage on the same genomic strand that might correspond to the miR/miR-star pair). If such a pattern is found, the RNA secondary structure in the local area is predicted. The sRNA-seq alignments in conjunction with the predicted RNA secondary structure are analyzed with respect to the criteria in Axtell and Meyers, 2018. If the criteria are met, the locus is annotated as a MIRNA.

How to go FAST

• If performing alignments, set mmap to r and use option --no_bigwigs. Setting mmap r means that multi-mapped reads will just be placed using a random-guess instead of the weighted method described by Johnson et al. (2016). The slower, weighted methods are slower but more accurate. Setting --no_bigwigs prevents the use of ShortTracks to create genome browser track files of the sRNA-seq alignments.
• Use more --threads as your system allows. Up to 10 or 20. Be sure to allocate enough total memory (about 8-10GB RAM per thread, maybe higher for large genomes).
• Use a better reference genome: Highly fragmented genome assemblies are much slower than well-assembled genomes.

ShortStack version 4 Major Changes

ShortStack version 4 is a major update. The major changes are:

Major Changes

• Completeley re-written in python3.
• Streamlined installation using a conda recipe hosted on bioconda.
• All compute-intensive processes are now multi-threaded, so execution times are faster when the user specifies higher values of --threads.
• Much more reliance on other tools (bedtools, cutadapt for instance) .. less re-inventing of wheels.
• Output of hairpin structure visualizations using strucVis.
• Output of genome-browser-ready quantitative coverage tracks of aligned small RNAs using ShortTracks.
• MIRNA locus identification has been thoroughly changed to increase sensitivity while maintaining specificity.
• MIRNA locus identification can now be guided by user-provided 'known RNAs'. In contrast, truly de novo annotation of MIRNA loci, in the absence of matching the sequence of a 'known RNA' is disabled by default. This change in philosophy acknowledges that, in most well-studied organisms, most high-confidence microRNA families are already known.
• Change the license to MIT from GPL3.

Option changes:

• Drop support for cram format (options --cram, --cramfile eliminated)
• Drop support for colorspace (option --cquals eliminated)
• Replace option --bowtie_cores with --threads
• Eliminate option --bowtie_m. Now -k 50 is always used.
• Eliminate option --ranmax. Now mmappers will always be placed (except mode u)
• Eliminate SAM tags XY:Z:O and XY:Z:M .. no more suppression of mmap reads
• Add SAM tag XY:Z:H .. highly repetitive read (50 or more hits, not all known).
• Add SAM tag YS:Z .. small RNA size information
• Eliminate option --keep_quals. Quality values will always be stored in the bam file if input was fastq.
• Modify option --locus so that it only accepts a single locus query.
• Eliminate option --total_primaries .. instead use a fast hack to rapidly calculate this.
• Option --locifile now understands .bed and .gff3 formats, as well as the original simple tab-delimited format.
• Added options --autotrim and --autotrim_key. This allows automatic detection of 3' adapters by tallying the most common sequence that occurs after a known, highly abundant small RNA (given by autotrim_key).
• Add option --known_miRNAs. Provide a FASTA file of known mature small RNA sequences to search for and to nucleate searches for qualifying MIRNA loci.
• Add option --dn_mirna. The --dn_mirna activates a de novo search for MIRNA loci independent of those that align to the 'known RNAs' provided by the user. By default, --dn_mirna is not active.

Issues

Please post issues, comments, bug reports, questions, etc. to the project github page at https://github.com/MikeAxtell/ShortStack.

FAQ

• I ran an analysis and found no loci annotated as MIRNA loci!
  • By default, ShortStack will not do a de novo search for loci that qualify as MIRNA loci. To search for MIRNA loci the user has to explicitly request it, using either or both of the options --known_miRNAs and --dn_mirna. known_miRNAs provides a list of known mature miRNA sequences. Places where these sequences align to the reference genome are examined to see if the small RNA alignment pattern and predicted RNA secondary structure qualifies as a MIRNA locus. The switch --dn_mirna turns on a de novo MIRNA search. The de novo MIRNA search is turned off by default to reduce false annotations. The idea is that most mature miRNAs are known in most species by now.
• What happened to the phasing scores?
  • I decided to omit phasing scores as of ShortStack version 4.0. This is because I gradually have lost confidence the accuracy of genome-wide scans to provide acceptable sensitivity and specificity for scoring phasing. For a detailed analysis of the challenges of calling phasing of siRNA clusters in genome-wide analyses, see Polydore et al. (2018). I am considering bringing phasing scores back, but just for 21-22 nt siRNA loci, in a future release.
• Installation fails with conda
  • Many problems I've seen with installation using conda stem from incorrectly configured conda installations. It is critical to have conda configured exactly as specified by the bionconda project. Your .condarc file (usually found in the user's home directory) must have the channels configured with conda-forge as the highest priority channel, bioconda as the second-highest priority, and defaults as the lowest priority. Additionally, the 'strict' channel priority option must be enabled. See instructions at bionconda project. A correctly configured conda should have a .condarc file that looks like:

channels:
    - conda-forge
    - bioconda
    - defaults
channel_priority: strict
repodata_fns:
    - repodata.json

• An older version of ShortStack is installed by conda
  • This can occur because of quirks in the way that conda searches for package updates .. sometimes it relies on the local cache of available packages instead of scanning the remotes. To overcome this, explicitly request the desired version. For instance, to get ShortStack version 4.0.1:

conda create --name ShortStack4
conda activate ShortStack4
conda install shortstack=4.0.1