rnateam.srna.yaml

---
id: srna
name: sRNA
description: >-
  In this tutorial, we will examine expression of the piRNA subclass of sRNAs
  and their targets in <i>Drosophila melanogaster</i>.
title_default: srna
tags:
  - "sRNA"

steps:
  - title: Introduction
    content: >-
      Small, noncoding RNA (sRNA) molecules, typically 18-40nt in length, are
      key features of post-transcriptional regulatory mechanisms governing gene
      expression. Through interactions with protein cofactors, these tiny sRNAs
      typically function by perfectly or imperfectly basepairing with substrate
      RNA molecules, and then eliciting downstream effects such as translation
      inhibition or RNA degradation. Different subclasses of sRNAs - e.g.
      microRNAs (miRNAs), Piwi-interaction RNAs (piRNAs), and endogenous short
      interferring RNAs (siRNAs) - exhibit unique characteristics, and their
      relative abundances in biological contexts can indicate whether they are
      active or not.
    backdrop: true
  - title: Introduction
    content: >-
      The data used in this tutorial are from polyphosphatase-treated sRNA
      sequencing (sRNA-seq) experiments in <i>Drosophila</i>. The goal of this
      study was to determine how siRNA expression changes in flies treated with
      RNA interference (RNAi) to knock down Symplekin, which is a component of
      the core cleaveage completx. To that end, in addition to sRNA-seq,
      mRNA-seq experiments were performed to determine whether targets of
      differentially expressed siRNAs were also differentially expressed. The
      original published study can be found <a
      href="https://www.ncbi.nlm.nih.gov/pubmed/28415970">here</a>. Because of
      the long processing time for the large original files - which contained
      7-22 million reads each - we have downsampled the original input data to
      include only a subset of usable reads.
    backdrop: true
  - title: Introduction
    content: >-
      In this tutorial, we will examine expression of the piRNA subclass of
      sRNAs and their targets in <i>Drosophila melanogaster</i>.
    backdrop: true
  - title: Analysis strategy
    content: >-
      In this exercise we will identify what small RNAs are present in
      <i>Drosophila</i> Dmel-2 tissue culture cells treated with either RNAi
      against core cleavage complex component Symplekin or blank (control) RNAi
      (published data available in GEO at <a
      href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82128">GSE82128</a>).
      In this study, biological triplicate sRNA- and mRNA-seq libraries were
      sequenced for both RNAi conditions. After removing contaminant ribosomal
      RNA (rRNA) and miRNA reads, we will identify and quantify endogenous
      siRNAs from sequenced reads and test for differential abundance. We will
      follow a popular small RNA analysis pipeline developed for piRNAs by the
      Phillip Zamore Lab and the ZLab (Zhiping Weng) at UMass Med School called
      <a href="https://github.com/bowhan/piPipes">PiPipes</a>. Although PiPipes
      was developed for analysis of piRNAs, many of the basical principles can
      be applied to other classes of small RNAs. <br><br>It is of note that this
      tutorial uses datasets that have been de-multiplexed so that the input
      data files are a single FASTQ formatted file for each sample. Because
      sRNAs are typically much smaller than fragments generated for RNA-seq or
      other types of deep sequencing experiments, single-end sequencing
      strategies are almost always used to sequence sRNAs. This tutorial uses
      the Collections feature of Galaxy to orgainze each set of replicates into
      a single group, making tool form submission easier and ensuring
      reproducibility.
    backdrop: true
  - title: Data upload and organization
    content: >-
      Due to the large size of the original sRNA-seq datasets, we have
      downsampled them to only inlcude a subset of reads. These datasets are
      avaialble at <a href="https://doi.org/10.5281/zenodo.826906">Zenodo</a>
      where you can find the FASTQ files corresponding to replicate sRNA-seq
      experiments and additional annotation files for the Drosophila
      melanogaster genome version dm3.
    backdrop: true
  - title: History options
    element: '#history-options-button'
    content: >-
      We will start the analyses by creating a new history. Click on this button
      and then "Create New"
    placement: left
  - title: Uploading the new data
    element: '#tool-panel-upload-button .fa.fa-upload'
    content: We need to upload data. Open the Galaxy Upload Manager
    placement: right
    postclick:
      - '#tool-panel-upload-button .fa.fa-upload'
      - '#btn-reset'
  - title: Uploading the input data
    element: '#btn-new'
    content: Click on Paste/Fetch Data
    placement: right
    postclick:
      - '#btn-new'
  - title: Uploading the input data
    element: .upload-text-column .upload-text .upload-text-content.form-control
    content: >-
      Insert the links here.<br> The input is available on <a
      href="https://zenodo.org/record/826906#.Wc5SUWiCzIU">Zenodo</a>.
    placement: right
    textinsert: >-
      https://zenodo.org/record/826906/files/Blank_RNAi_sRNA-seq_rep1_downsampled.fastqsanger.gz

      https://zenodo.org/record/826906/files/Blank_RNAi_sRNA-seq_rep2_downsampled.fastqsanger.gz

      https://zenodo.org/record/826906/files/Blank_RNAi_sRNA-seq_rep3_downsampled.fastqsanger.gz

      https://zenodo.org/record/826906/files/dm3_miRNA_hairpin_sequences.fa.gz

      https://zenodo.org/record/826906/files/dm3_rRNA_sequences.fa.gz

      https://zenodo.org/record/826906/files/dm3_transcriptome_sequences_downsampled.fa.gz

      https://zenodo.org/record/826906/files/dm3_transcriptome_Tx2Gene_downsampled.tab.gz

      https://zenodo.org/record/826906/files/Symp_RNAi_sRNA-seq_rep1_downsampled.fastqsanger.gz

      https://zenodo.org/record/826906/files/Symp_RNAi_sRNA-seq_rep2_downsampled.fastqsanger.gz

      https://zenodo.org/record/826906/files/Symp_RNAi_sRNA-seq_rep3_downsampled.fastqsanger.gz
  - title: Data types
    content: |-
      Make sure the datatypes are adjusted.
      <ul>
        <li>Set the datatype of the read (.fastqsanger) files to fastq</li>
        <li>Set the datatype of the annotation (.tab) file to tab and assign the Genome as dm3</li>
        <li>Set the datatype of the reference (.fa) files to fasta and assign the Genome as dm3</li>
      </ul>
    backdrop: false
  - title: Uploading the input data
    element: '#btn-start'
    content: Click on "Start" to start loading the data to history
    placement: right
    postclick:
      - '#btn-start'
  - title: Uploading the input data
    element: '#btn-close'
    content: >-
      The upload may take a while.<br> Hit the close button to close this
      window.
    placement: right
    postclick:
      - '#btn-close'
  - title: Rename the input data
    element: '.history-right-panel .list-items > *:first'
    content: >-
      Rename the files in your history to something meaningful (e.g.
      Blank_RNAi_sRNA_rep1.fastq)<br><br> <ul>
        <li>Click on the pencil icon beside the file to "Edit Attributes"</li>
        <li>Change the name in "Name" to get only the name of the sample</li>
      </ul>
    position: left
  - title: Build a data set
    element: '.history-right-panel .list-items > *:first'
    content: |-
      Build a Dataset list for each set of replicate FASTQ files <ul>
        <li>Click the <i>Operations on multiple datasets</i> check box at the top of the history panel</li>
        <li>Check the three boxes next to the blank RNAi (control) sRNA-seq samples</li>
        <li>Click <i>For all selected…</i> and choose <i>Build dataset list</i></li>
        <li>Ensure that only the three blank samples are selected, and enter a name for the new collection (e.g. Blank RNAi sRNA-seq)</li>
        <li>Click Create list</li>
      </ul>
    position: left
  - title: Build a data set
    content: Repeat for the three <i>Symplekin</i> RNAi samples
    backdrop: false
  - title: Read quality checking
    content: >-
      Read quality scores (phred scores) in FASTQ-formatted data can be encoded
      by one of a few different encoding schemes. Most Galaxy tools assume that
      input FASTQ files are using the Sanger/Illumina 1.9 encoding scheme. If
      the input FASTQ files are using an alternate encoding scheme, then some
      tools will not interpret the quality score encodings correctly. It is good
      practice to confirm the quality encoding scheme of your data and then
      convert to Sanger/Illumina 1.9, if necessary. We can check the quality
      encoding scheme using the <a
      href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/">FastQC</a>
      tool (further described in the <a
      href="https://galaxyproject.github.io/training-material/topics/sequence-analysis">NGS-QC
      tutorial</a>).
    backdrop: true
  - title: Read quality checking
    element: '#tool-search-query'
    content: Search for FastQC tool
    placement: right
    textinsert: FastQC
  - title: Read quality checking
    element: '#tool-search'
    content: Click on the "FastQC" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fdevteam%2Ffastqc%2Ffastqc%2F0.68"]
  - title: Read quality checking
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of FASTQ read files to assess the
      overall read/base quality and quality score encoding scheme using the
      following parameters <ul> <li><b>Short read data from your current
      history</b> Click the “Dataset collection” tab and then select the blank
      RNAi sRNA-seq dataset collection </li> </ul>
    position: left
  - title: Read quality checking
    content: Repeat for the <i>Symplekin</i> RNAi dataset collection.
    backdrop: false
  - title: Questions
    content: |-
      <ul>
        <li>What quality score encoding scheme is being used for each sample?</li>
        <li>What is the read length for each sample?</li>
        <li>What does the base/read quality look like for each sample?</li>
        <li>Are there any adaptors present in these reads? Which one(s)?</li>
      </ul>
    backdrop: false
  - title: Quality checking
    element: '.history-right-panel .list-items > *:first'
    content: >-
      Check the FASTQC output and scroll down to the “Adapter Content” section.
      You can see that Illumina Universal Adapters are present in <b>>80%</b> of
      the reads. The next step is to remove these artificial adaptors because
      they are not part of the biological sRNAs. If a different adapter is
      present, you can update the <b>Adapter sequence to be trimmed off</b> in
      the Trim Galore!
    position: left
  - title: Adaptor trimming
    content: >-
      sRNA-seq library preparation involves adding an artificial adaptor
      sequence to both the 5’ and 3’ ends of the small RNAs. While the 5’
      adaptor anchors reads to the sequencing surface and thus are not
      sequenced, the 3’ adaptor is typically sequenced immediately following the
      sRNA sequence. In the example datasets here, the 3’ adaptor sequence is
      identified as the Illumina Universal Adapter, and needs to be removed from
      each read before aligning to a reference. We will be using the Galaxy tool
      Trim Galore! which implements the <a
      href="https://cutadapt.readthedocs.io/en/stable/">cutadapt</a> tool for
      adapter trimming.
    backdrop: true
  - title: Adaptor trimming
    element: '#tool-search-query'
    content: Search for Trim Galore! tool
    placement: right
    textinsert: Trim Galore
  - title: Adaptor trimming
    element: '#tool-search'
    content: Click on the "Trim Galore!" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fbgruening%2Ftrim_galore%2Ftrim_galore%2F0.4.3.0"]
  - title: Adaptor trimming
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of FASTQ read files to remove Illumina
      adapters from the 3’ ends of reads with the following parameters <ul>
      <li><b>Is this library paired- or single-end?</b> Single-end</li>
      <li><b>Reads in FASTQ format</b> Click the “Dataset collection” tab and
      then select the blank RNAi sRNA-seq dataset</li> <li><b>Trimming
      reads?</b> Illumina universal</li> <li><b>Trim Galore! advanced
      settings</b> Full parameter list</li> <li><b>Trim low-quality ends from
      reads in addition to adapter removal</b> 0</li> <li><b>Overlap with
      adapter sequence required to trim a sequence</b> 6</li> <li><b>Discard
      reads that became shorter than length INT</b> 12</li> <li><b>Generate a
      report file</b> Yes</li> </ul>
    position: left
  - title: Adaptor trimming
    content: >-
      Repeat for the <i>Symplekin</i> RNAi dataset collection.

      <br><br>We don’t want to trim for quality because the adapter-trimmed
      sequences represent a full small RNA molecule, and we want to maintain the
      integrity of the entire molecule. We increase the minimum read length
      required to keep a read because small RNAs can potentially be shorter than
      20 nt (the default value). We can check out the generated report file for
      any sample and see the command for the tool, a summary of the total reads
      processed and number of reads with an adapter identified, and histogram
      data of the length of adaptor trimmed. We also see that a very small
      percentage of low-quality bases have been trimmed
    backdrop: true
  - title: Adaptor trimming
    content: >-
      Run <b>FastQC</b> on each collection of trimmed FASTQ read files to assess
      whether adapters were successfully removed.
    backdrop: true
  - title: Questions
    content: |-
      <ul>
        <li>What is the read length?</li>
        <li>Are there any adaptors present in these reads? Which one(s)?</li>
      </ul>
    backdrop: false
  - title: Adaptor trimming
    content: >-
      An interesting thing to note from our FastQC results is the Sequence
      Length Distribution results. While many RNA-seq experiments have normal
      distribution of read lengths, an unusual spike at 22nt is observed in our
      data. This spike represents the large set of endogenous siRNAs that occur
      in the cell line used in this study, and is actually confirmation that our
      dataset captures the biological molecule we are interested in.
    backdrop: true
  - title: Adaptor trimming
    content: >-
      Now that we have converted to fastqsanger format and trimmed our reads of
      the Illumina Universal Adaptors, we will align our trimmed reads to
      reference Drosophila rRNA and miRNA sequences (dm3) to remove these
      artifacts. Interestingly, Drosophila have a short 2S rRNA sequence that is
      30nt long and typically co-migrates with sRNA populations during gel
      electrophoresis. rRNAs make up a very large proportion of all non-coding
      RNAs, and thus need to be removed. Oftentimes, experimental approaches can
      be utilized to deplete or avoid capture of rRNAs, but these methods are
      not always 100% efficient. We also want to remove any miRNA sequences, as
      these are not relevant to our analysis. After removing rRNA and miRNA
      reads, we will analyze the remaining reads, which may be siRNA or piRNA
      sequences.
    backdrop: false
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: >-
      To first identify rRNA-originating reads (which we are not interested in
      in this case), we will align the reads to a reference set of rRNA
      sequences using <a
      href="https://ccb.jhu.edu/software/hisat2/index.shtml">HISAT2</a>, an
      accurate and fast tool for aligning reads to a reference.
    backdrop: true
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search-query'
    content: Search for HISAT2 tool
    placement: right
    textinsert: HISAT2
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: Click on the "HISAT2" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fhisat2%2Fhisat2%2F2.0.5.2"]
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of trimmed reads to align to reference
      rRNA sequences with the following parameters <ul> <li><b>Single end or
      paired reads?</b> Individual unpaired reads</li> <li><b>Reads</b> Click
      the “Dataset collection” tab and then select the blank RNAi sRNA-seq
      dataset of trimmed FASTQ files</li> <li><b>Source for the reference genome
      to align against</b> Use a genome from history</li> <li><b>Select the
      reference genome</b> Dmel_rRNA_sequences.fa</li> <li><b>Spliced alignment
      parameters</b> Specify spliced alignment parameters</li> <li><b>Specify
      strand-specific information</b> Second Strand (F/FR)</li> </ul>
    position: left
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: >-
      We now need to extract the unaligned reads from the output BAM file for
      aligning to reference miRNA sequences. We can do this by using the
      <b>Filter SAM or BAM, output SAM or BAM</b> tool to obtain reads with a
      bit flag = 4 (meaning the read is unaligned) and then converting the
      filtered BAM file to FASTQ format with the <b>Convert from BAM to
      FastQ</b> tool.
    backdrop: true
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search-query'
    content: 'Search for Filter SAM or BAM, output SAM or BAM tool'
    placement: right
    textinsert: 'Filter SAM or BAM, output SAM or BAM'
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: 'Click on the "Filter SAM or BAM, output SAM or BAM" tool to open it'
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fdevteam%2Fsamtool_filter2%2Fsamtool_filter2%2F1.1.2"]
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of HISAT2 output BAM files with the
      following parameters <ul> <li><b>SAM or BAM file to filter</b> Click the
      “Dataset collection” tab and then select the blank RNAi sRNA-seq dataset
      of aligned HISAT2 BAM files</li> <li><b>Filter on bitwise flag</b>
      Yes</li> <li><b>Only output alignments with all of these flag bits set</b>
      Check the box next to “The read in unmapped”</li> </ul>
    position: left
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search-query'
    content: Search for Convert from BAM to FastQ tool
    placement: right
    textinsert: Convert from BAM to FastQ
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: Click on the "Convert from BAM to FastQ" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fbedtools%2Fbedtools_bamtofastq%2F2.26.0.0"]
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of filtered HISAT2 output BAM files
      with the following parameters <ul> <li><b>Convert the following BAM file
      to FASTQ:</b> Click the “Dataset collection” tab and then select the blank
      RNAi sRNA-seq dataset of filtered HISAT2 BAM files</li> </ul>
    position: left
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: >-
      Next we will align the non-rRNA reads to a known set of miRNA hairpin
      sequences to identify miRNA reads.
    backdrop: true
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search-query'
    content: Search for HISAT2  tool
    placement: right
    textinsert: HISAT2
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: Click on the "HISAT2" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fhisat2%2Fhisat2%2F2.0.5.2"]
  - title: Heirarchical alignment to rRNA and miRNA reference sequences
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of filtered HISAT2 output FASTQ files
      to align non-rRNA reads to reference miRNA hairpin sequences using the
      following parameters <ul> <li><b>Single end or paired reads?</b>
      Individual unpaired reads</li> <li><b>Reads</b> Click the “Dataset
      collection” tab and then select the blank sRNA-seq dataset of non-rRNA
      FASTQ files</li> <li><b>Source for the reference genome to align
      against</b> Use a genome from history</li> <li><b>Select the reference
      genome</b> Dmel_rRNA_sequences.fa</li> <li><b>Spliced alignment
      parameters</b> Specify spliced alignment parameters</li> <li><b>Specify
      strand-specific information</b> Second Strand (F/FR)</li> </ul>
    position: left
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Hierarchical read alignment to remove rRNA/miRNA reads
    content: >-
      For this tutorial we are not interested in miRNA reads, so we need to
      extract <i>unaligned</i> reads from the output BAM files. To do this,
      repeat the <b>Filter SAM or BAM, output SAM or BAM</b> and <b>Convert from
      BAM to FastQ</b> steps for each dataset collection. Finally, rename the
      converted FASTQ files something meaningful (e.g. “non-r/miRNA control RNAi
      sRNA-seq”).

      <br>In some instances, miRNA-aligned reads are the desired output for
      downstream analyses, for example, if we are investigating how loss of key
      miRNA pathway components affect levels of pre-miRNA and mature miRNAs. In
      this case, the BAM output of HISAT2 can directly be used in subsequent
      tools as it contains the subset of reads aligned to miRNA sequences.
    backdrop: true
  - title: Small RNA subclass distinction
    content: >-
      In <i>Drosophila</i>, non-miRNA small RNAs are typically divided into two
      major groups: endogenous siRNAs which are 20-22nt long and piRNAs which
      are 23-29nt long. We want to analyze these sRNA subclasses independently,
      so next we are going to filter the non-r/miRNA reads based on length using
      the <b>Manipulate FASTQ</b> tool.
    backdrop: true
  - title: Extract subclasses
    element: '#tool-search-query'
    content: Search for Manipulate FASTQ  tool
    placement: right
    textinsert: Manipulate FASTQ
  - title: Extract subclasses
    element: '#tool-search'
    content: Click on the "Manipulate FASTQ" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fdevteam%2Ffastq_manipulation%2Ffastq_manipulation%2F1.0.1"]
  - title: Extract subclasses
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of non-r/miRNA reads to identify
      siRNAs (20-22nt) using the following parameters <ul> <li><b>FASTQ
      File:</b> Click the “Dataset collection” tab and then select the blank
      RNAi sRNA-seq dataset of non-r/miRNA FASTQ files</li> <li><b>Match
      Reads:</b> Click “Insert Match Reads”</li> <li><b>Match Reads by:</b> Set
      to “Sequence Content”</li> <li><b>Match by:</b> ^.{12,19}$ ^.{23,50}$</li>
      <li><b>Manipulate Reads:</b> Click “Insert Manipulate Reads”</li>
      <li><b>Manipulate Reads on:</b> Set to “Miscellaneous actions”</li>
      <li><b>Miscellaneous Manipulation Type:</b> Set to “Remove Read”</li>
      </ul>
    position: left
  - title: Extract subclasses
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Extract subclasses
    content: >-
      Rename each resulting dataset collection something meaningful (i.e. “blank
      RNAi - siRNA reads (20-22nt)”)
    backdrop: true
  - title: Extract subclasses
    content: >-
      The regular expression in the <b>Match by</b> parameter tells the tool to
      identify sequences that are length 12-19 or 23-50 (inclusive), and the
      <b>Miscellaneous Manipulation Type</b> parameter tells the tool to remove
      these sequences. What remains are sequences of length 20-22nt. We will now
      repeat these steps to identify sequences in the size-range of piRNAs
      (23-29nt).
    backdrop: true
  - title: Extract subclasses
    element: '#tool-search-query'
    content: Search for Manipulate FASTQ  tool
    placement: right
    textinsert: Manipulate FASTQ
  - title: Extract subclasses
    element: '#tool-search'
    content: Click on the "Manipulate FASTQ" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fdevteam%2Ffastq_manipulation%2Ffastq_manipulation%2F1.0.1"]
  - title: Extract subclasses
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of non-r/miRNA reads to identify
      sequences in the size-range of piRNAs (23-29nt) using the following
      parameters <ul> <li><b>FASTQ File:</b> Click the “Dataset collection” tab
      and then select the blank RNAi sRNA-seq dataset of non-r/miRNA FASTQ
      files</li> <li><b>Match Reads:</b> Click “Insert Match Reads”</li>
      <li><b>Match Reads by:</b> Set to “Sequence Content”</li> <li><b>Match
      by:</b> ^.{12,22}$ ^.{30,50}$</li> <li><b>Manipulate Reads:</b> Click
      “Insert Manipulate Reads”</li> <li><b>Manipulate Reads on:</b> Set to
      “Miscellaneous actions”</li> <li><b>Miscellaneous Manipulation Type:</b>
      Set to “Remove Read”</li> </ul>
    position: left
  - title: Extract subclasses
    content: Repeat for the Symplekin RNAi dataset collection.
    backdrop: true
  - title: Extract subclasses
    content: >-
      Rename each resulting dataset collection something meaningful (i.e. “blank
      RNAi - siRNA reads (23-29nt)”)
    backdrop: true
  - title: Extract subclasses
    element: '#tool-search-query'
    content: Search for FastQC  tool
    placement: right
    textinsert: FastQC
  - title: Extract subclasses
    element: '#tool-search'
    content: Click on the "FastQC" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fdevteam%2Ffastqc%2Ffastqc%2F0.69"]
  - title: Extract subclasses
    element: '#tool-search'
    content: >-
      Run FastQC on each collection of siRNA and piRNA read files to confirm the
      correct read lengths.
    position: left
  - title: Extract subclasses
    element: '.history-right-panel .list-items > *:first'
    content: >-
      Inspect the outputs. You should notice that we have ~32k reads that are
      20-22nt long in our Reads_20-22_nt_length_siRNAs file and ~16k reads that
      are 23-29nt long in our Reads_23-29_nt_length_piRNAs file, as expected.
      Given that we had ~268k trimmed reads in the Blank RNAi replicate 3 file,
      siRNAs represent ~12.1% of the reads and piRNAs represent ~6.2% of the
      reads in this experiment. There is no distinct peak in the piRNA range (
      you can find the example of the real piRNA profile in Figure 1 from [Colin
      D. Malone et al, 2009](http://dx.doi.org/10.1016/j.cell.2009.03.040)).
    position: left
  - title: Extract subclasses
    content: >-
      The next step in our analysis pipeline is to identify which RNA features -
      e.g. protein-coding mRNAs, transposable elements - the siRNAs and piRNAs
      align to. Most fly piRNAs originate from and thus align to transposable
      elements (TEs) but some also originate from genome piRNA clusters or
      protein-coding genes. siRNAs are thought to inhibit TE mobility and
      potentially target mRNAs for degradation via an RNA-interference (RNAi)
      mechanism. Ultimately, we want to know whether an RNA feature (TE, piRNA
      cluster, mRNA, etc.) has significantly different numbers of siRNAs or
      piRNAs targeting it. To determine this, we will use <b>Salmon</b> to
      simultaneously align and quantify siRNA reads against known target
      sequences to estimate siRNA abundance per target. For the remainder of the
      tutorial we will be focusing on siRNAs, but similar approaches can be done
      for piRNAs, miRNAs, or any other subclass of small, non-coding RNAs.
    backdrop: true
  - title: siRNA abundance estimation
    content: >-
      We want to identify which siRNAs are differentially abundance between the
      blank and Symplekin RNAi conditions. To do this we will implement a
      counting approach using <b>Salmon</b> to quantify siRNAs per RNA feature.
      Specifically, we will analyzing mRNA and TE elements by counting siRNAs
      against a FASTA file of transcript sequences, not the reference genome.
      This approach is especially useful in this case because we are interested
      in TEs, which occur at many copies (100s - 1000s) in the genome, and thus
      will result in multiple alignments if we align to the genome with a tool
      like <b>HISAT2</b>. Further, we will be counting abundance of siRNAs that
      align sense or antisense to an RNA feature independently, as opposite
      sense alignments are correlated with unique downstream silencing effects.
      Then, we will provide this information to <b>DESeq2</b> to generate
      normalized counts and significance testing for differential abundance of
      siRNAs per feature.
    backdrop: true
  - title: siRNA abundance estimation
    element: '#tool-search-query'
    content: Search for Salmon tool
    placement: right
    textinsert: Salmon
  - title: siRNA abundance estimation
    element: '#tool-search'
    content: Click on the "Salmon" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fbgruening%2Fsalmon%2Fsalmon%2F0.8.2"]
  - title: siRNA abundance estimation
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of siRNA reads (20-22nt) to quantify
      the abundance of antisense siRNAs at relevant targets. We will focus on
      abundance of siRNAs on mRNAs and transposable elements using the following
      parameters <ul> <li><b>Select a reference transcriptome from your history
      or use a built-in index?</b> Use one from the history</li> <li><b>Select
      the reference transcriptome:</b> Select the reference mRNA and TE fasta
      file</li> <li><b>The size should be odd number:</b> 19</li>
      <li><b>FASTQ/FASTA file:</b> Click the “Dataset collection” tab and then
      select the Blank RNAi siRNA (20-22nt) reads</li> <li><b>Specify the
      strandedness of the reads:</b> read 1 (or single-end read) comes from the
      reverse strand (SR)</li> <li><b>Additional Options:</b> Click to expand
      options</li> <li><b>Incompatible Prior:</b> 0</li> </ul>
    position: left
  - title: siRNA abundance estimation
    content: Repeat for the Symplekin RNAi siRNAs (20-22nt) reads dataset collection.
    backdrop: true
  - title: siRNA abundance estimation
    element: '#tool-search'
    content: Click on the "Salmon" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fbgruening%2Fsalmon%2Fsalmon%2F0.8.2"]
  - title: siRNA abundance estimation
    element: '#tool-search'
    content: >-
      Execute the tool on each collection of siRNA reads (20-22nt) to quantify
      the abundance of sense siRNAs at relevant targets by using the following
      parameters <ul> <li><b>Select a reference transcriptome from your history
      or use a built-in index?</b> Use one from the history</li> <li><b>Select
      the reference transcriptome:</b> Select the reference mRNA and TE fasta
      file</li> <li><b>The size should be odd number:</b> 19</li>
      <li><b>FASTQ/FASTA file:</b> Click the “Dataset collection” tab and then
      select the Blank RNAi siRNA (20-22nt) reads</li> <li><b>Specify the
      strandedness of the reads:</b> read 1 (or single-end read) comes from the
      forward strand (SF)</li> <li><b>Additional Options:</b> Click to expand
      options</li> <li><b>Incompatible Prior:</b> 0</li> </ul>
    position: left
  - title: siRNA abundance estimation
    content: Repeat for the Symplekin RNAi siRNAs (20-22nt) reads dataset collection.
    backdrop: true
  - title: siRNA abundance estimation
    content: >-
      The output of <b>Salmon</b> includes a table of RNA features, estimated
      counts, transcripts per million, and feature length. We will be using the
      TPM quantification column as input to <b>DESeq2</b> in the next section.
    backdrop: true
  - title: siRNA abundance estimation
    content: >-
      <a
      href="https://bioconductor.org/packages/release/bioc/html/DESeq2.html">DESeq2</a>
      is a great tool for differential expression analysis, but we also employ
      it here for estimation of abundance of reads targeting each of our RNA
      features. As input, <b>DESeq2</b> can take transcripts per million (TPM)
      counts produced by <b>Salmon</b> for each feature. TPMs are estimates of
      the relative abundance of a given transcript in units, and analogously can
      be used here to quantify siRNAs that align either sense or antisense to
      transcripts.
    backdrop: true
  - title: siRNA differential abundance testing
    element: '#tool-search-query'
    content: Search for DESeq2 tool
    placement: right
    textinsert: DESeq2
  - title: siRNA differential abundance testing
    element: '#tool-search'
    content: Click on the "DESeq2" tool to open it
    placement: right
    postclick:
      - >-
        a[href$="/tool_runner?tool_id=toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fdeseq2%2Fdeseq2%2F2.11.39"]
  - title: siRNA differential abundance testing
    element: '#tool-search'
    content: >-
      Execute the tool to test for differential abundance of antisense siRNAs at
      mRNA and TE features using the following parameters <ul> <li><b>Specify a
      factor name:</b> RNAi</li> <li>Under “1 Factor level”<b> Specify a factor
      level:</b> Symplekin</li> <li>Under “1 Factor level”<b>Counts file(s):</b>
      Click the “Dataset collection” tab and then select the Symplekin RNAi
      siRNA counts from reverse strand (SR)</li> <li>Under “2 Factor
      level”<b>Specify a factor level:</b> RNAi</li> <li>Under “2 Factor
      level”:<b>Counts file(s):</b> Click the “Dataset collection” tab and then
      select the Symplekin RNAi siRNA counts from reverse strand (SR)</li>
      <li><b>Choice of Input data:</b> Select “TPM values (e.g. from sailfish
      and salmon)</li> <li><b>Tabular file with Transcript - Gene mapping:</b>
      Select reference tx2g (transcript to gene) file (available through zenodo
      link)</li> <li><b>Output normalized counts table</b> Yes</li> </ul>
    position: left
  - title: siRNA differential abundance testing
    content: >-
      Repeat DESeq2 for the sense siRNAs at mRNA and TE features changing the
      following parameters:

      <ul>
        <li>Under “1: Factor level”: <b>Counts file(s):</b> Click the “Dataset collection” tab and then select the Symplekin RNAi siRNA counts from forward strand (SF)</li>
        <li>Under “2: Factor level”: <b>Counts file(s):</b> Click the “Dataset collection” tab and then select the Symplekin RNAi siRNA counts from forward strand (SF)</li>
      </ul>
    backdrop: true
  - title: siRNA differential abundance testing
    element: '#tool-search-query'
    content: Search for Filter tool
    placement: right
    textinsert: Filter
  - title: siRNA differential abundance testing
    element: '#tool-search'
    content: Click on the "Filter" tool to open it
    placement: right
    postclick:
      - 'a[href$="/tool_runner?tool_id=Filter1"]'
  - title: siRNA differential abundance testing
    element: '#tool-search'
    content: >-
      Execute the tool to extract features with a significantly different
      antisense siRNA abundance (adjusted p-value less than 0.05). <ul>
      <li><b>Filter</b> Select the <b>DESeq2</b> result file from testing
      antisense (SR) siRNA abundances</li> <li><b>With following condition:</b>
      c7<0.05</li> </ul>
    position: left
  - title: siRNA differential abundance testing
    content: >-
      Repeat Filter for the sense siRNAs at mRNA and TE features changing the
      following parameters:

      <ul>
        <li><b>Filter:</b> Select the DESeq2 result file from testing sense (SF) siRNA abundances</li>
      </ul>
    backdrop: true
  - title: Question
    content: |-
      <ul>
        <li>How many features have a significant difference in antisense and sense siRNA abundance in the Symplekin RNAi condition?</li>
      </ul>
    backdrop: false
  - title: DESeq2
    content: >-
      For more information about <b>DESeq2</b> and its outputs, have a look at
      <a
      href="https://www.bioconductor.org/packages/release/bioc/manuals/DESeq2/man/DESeq2.pdf">DESeq2
      documentation</a>.
    backdrop: true
  - title: Conclusion
    content: >-
      Analysis of small RNAs is a complicated and intricate process due to the
      diversity in characteristics, functionality, and nuances of small RNA
      subclasses. The goal of this tutorial is to introduce you to a common
      small RNA workflow that specifically identifies changes in endogenous
      siRNA abundances that target protein-coding mRNAs and transposable
      elements. The steps presented here can be rearranged and modified based on
      small RNA features of specific systems and the needs of the user.
    backdrop: true
  - title: Key points
    content: |-
      <ul>
        <li>Analysis of small RNAs is complex due to the diversity of small RNA subclasses.</li>
        <li>Both alignment to references and transcript quantification approaches are useful for small RNA-seq analyses.</li>
      </ul>
    backdrop: true