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GitHub - HaaseLab/PICB: piRNA cluster builder

PICB - piRNA Cluster Builder

PICB (piRNA Cluster Builder) is a flexible toolkit for assembling, prioritizing, and characterizing piRNA clusters.

• Motivation
• Getting started
• How to run PICB
  • Data preparation
  • Running PICB
• Parameter adjustments
• Output
• Other PICB functions
• Let's give it a try - An Example
• Authors, Citation and Acknowledgments

piRNAs (PIWI-interacting RNAs) and their PIWI protein partners play a key role in fertility and maintaining genome integrity by restricting mobile genetic elements (transposons) in germ cells. piRNAs originate from genomic regions which are called piRNA clusters.

PICB identifies genomic regions with a high density of piRNAs. This construction of piRNA clusters is performed through stepwise integration of unique and multimapping piRNAs.

Figure 1: PICB considers unique mapping piRNAs (NH=1), primary alignments of multimapping piRNAs (NH>1), and all possible alignments stepwise to build seeds, cores, and clusters. Find additional information in our recent publication.

Only very limited programming knowledge is needed to run PICB. Check out our step-by-step instructions and our demonstration below.

Please visit our publication for full context.

PICB runs in R versions ≥ 4.2. to 4.4.

It is possible to run PICB in RStudio, in an R script on your local machine or with High Performance Computing (HPC) resources or in Jupyter Notebook (using R). Keep in mind that as for any handling of large-scale sequencing data you need to have sufficient memory on your device (or cluster) allocated.

1. Load dependencies in R environment

You will need to install and load the following required R packages:

install.packages(c("data.table", "seqinr", "openxlsx", "dplyr"))
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("IRanges", "GenomicRanges", "GenomicAlignments", "Rsamtools", "Biostrings", "GenomeInfoDb", "BSgenome", "rtracklayer"))

💡 In case you have not worked with GRanges yet, we recommend reading the following GRanges Introduction.

2. Install PICB

PICB is available to install from any of the following sources. If you choose to install from GitHub, make sure to have either devtools or remotes installed.

Now load PICB in your R environment:

From now on it gets even easier.

There are just two required inputs: the BAM File and the Reference Genome.

Checklist for having the right BAM File

• NH and NM tags are included
• Indexed (.bai file required)

PICB stops when these requirements are not met and provides a descriptive error message.

Four options for providing the Reference Genome

1. Your genome is part of the BSgenome package

# Example for Drosophila melanogaster, replace with your own genome - previous installation of BSgenome required
library(BSgenome.Dmelanogaster.UCSC.dm6)
myGenome <- "BSgenome.Dmelanogaster.UCSC.dm6"

1. Chromosome names and lengths according to the BAM file

myGenome <- GenomeInfoDb::Seqinfo(
    seqnames = c("chr2L", "chr2R", "chr3L", "chr3R", "chr4", "chrX", "chrY"), 
    seqlengths = c(23513712, 25286936, 28110227, 32079331, 1348131, 23542271, 3667352))

1. Using a Seqinfo object

Or use an existing Seqinfo object:

myGenome <- GenomeInfoDb::Seqinfo(genome = "dm6")

1. Fasta with the assembled genome sequence.

myGenome <- PICBloadfasta(FASTA.NAME="/path/to/your/genome.fa")

1. Load your mapped piRNAs with PICBload

myAlignments <- PICBload(
    BAMFILE = bam_file,
    REFERENCE.GENOME = myGenome
)

1. Build piRNA clusters with PICBbuild

myClusters <- PICBbuild(
    IN.ALIGNMENTS = myAlignments,
    REFERENCE.GENOME = myGenome
)$clusters

💡 Both PICBload and PICBbuild allow wide-ranging adjustments: Read the below section Parameter adjustments to adapt to sparse reference genomes and specific limitations of the data set.

💡 As described here, PICBbuild integrates unique mapping (seeds), primary multimapping (cores) and secondary alignments stepwise. You can access the outputs of the previous steps by using $seeds or $cores instead of $clusters.

PICB allows wide-ranging parameter adjustments to adapt to e.g. sparse reference genomes and specific limitations of the data set. Tables of adjustments for both functions are shown below.

Required parameters:

• BAMFILE
• REFERENCE.GENOME

Adjustable parameters:

Required parameters:

• IN.ALIGNMENTS (output of PICBload)
• REFERENCE.GENOME (reference genome previously used in PICBload)

The library size can be adjusted as shown in our PICB demo, however in most cases this is not necessary.

PICBbuild integrates unique mapping (seeds), primary multimapping (cores) and secondary alignments stepwise using the sliding window algorithm. Each step allows following parameter adjustments.

Adjustable parameters for processing unique mapping alignments:

The called windows are reduced into seeds and further filtered:

The next step includes primary multimapping alignments using a similar but simplified algorithm. Following parameters can be adjusted:

These resulting windows are being reduced into cores and are further filtered:

Seeds and primary multimapping windows overlapping seeds merge into cores. Standalone seeds are also considered cores. Cores not overlapping with a seed are being filtered out as their transcription cannot be verified.

In the next step secondary alignments are processed identically as primary multimapping alignments. This step is dependent on whether PICBload loaded the secondary alignments (parameter IS.SECONDARY.ALIGNMENT).

Cores and secondary alignments overlapping cores merge into clusters. Standalone cores are also considered clusters. piRNA clusters were formed and include all alignment information.

The output of PICBbuild includes seeds, cores and clusters, each in GenomicRanges format. In Running PICB, we extracted directly the clusters using $clusters. Extracting the seeds and cores can be done similarly using $seeds and $cores.

The clusters follow GRanges convention including the genomic coordinates (seqnames, ranges, and strand) and metacolumns. There are in total 9 metacolumns when running PICB with default parameters:

You can use PICBoptimize to find the optimal parameters for PICBbuild for your dataset. This is especially useful if you are not sure about the quality of your data or your reference genome. The goal is to find a set of parameters that maximize the number of piRNAs accomodated in the clusters while minimizing genomic space occupied by the clusters.

Make sure to provide the values for the parameters as a vector. An example is shown below:

parameterExploration <- PICBoptimize(
    IN.ALIGNMENTS = myAlignments, 
    REFERENCE.GENOME=myGenome, 
    MIN.UNIQUE.ALIGNMENTS.PER.WINDOW=c(1,2,3,4,5)
)

This function works on numerical parameters and returns a dataframe with any combination of your chosen parameters and their corresponding number of clusters, the total width of the clusters in nucleotides, the number of reads explained by the clusters, the mean RPKM of the clusters, the fraction of reads explained by the clusters and fraction of genomic space occupied by the clusters.

Be aware that for most datasets, the default parameters will yield great results and you do not need to optimize parameters.

Next to the standard analysis, PICBstrandanalysis allows strand-specific analysis of piRNA clusters. The function computes the sense/antisense ratio of piRNAs per cluster (non-collapsed) and adds this information to a new metadata column (s_as_ratio).

myClustersWithStrandAnalysis <- PICBstrandanalysis(
    IN.ALIGNMENTS = myAlignments,
    IN.RANGES = myClusters
)

To visualize the sense/antisense ratio of the clusters, you can plot the sense/antisense ratio of the clusters in a violin plot. Higher numbers indicate a higher ratio of sense to antisense piRNAs, while values close to 1 indicate an equal ratio of sense and antisense piRNAs.

In the following, we would like to show you how easy it is to run PICB!

We created a demo to show you the most basic workflow with PICB. Download the sample subset of mapped reads and the corresponding R-based code in R Script or Jupyter Notebook format from the folder demo. Though, if you feel extra brave today, feel free to direcly use your own bam file!

Step 1: Getting started with PICB

• PICB-Tutorial 1/3: Installation of PICB

Follow the steps in Getting started to install PICB. Do not forget to load PICB with library(PICB).

Step 2: Data preparation

• PICB-Tutorial 2/3: Preparation of input

We showed different ways on how to load your genome. In the following the variant with using the BSgenome package. You will need to install your specific genome first. In our demo it is the Drosophila melanogaster genome.

library("BSgenome.Dmelanogaster.UCSC.dm6")

Now, let's store the genome into the variable myGenome.

myGenome <- "BSgenome.Dmelanogaster.UCSC.dm6"

Step 3: Running PICB

• PICB-Tutorial 3/3: Running PICBload and PICBbuild

Load the alignments with PICBload.

myAlignments <- PICBload(
    BAMFILE = system.file("extdata","Fly_Ov1_chr2L_20To21mb_filtered.bam", package="PICB"), 
    REFERENCE.GENOME = myGenome)

Next, we want to form the piRNA clusters using the PICBbuild function. Usually you would not need to include the size of the library (LIBRARY.SIZE) since PICB calculates it automatically. However, just for this demo, please include this parameter to adjust to the subset we chose to create this demo.

myClusters <- PICBbuild(
    IN.ALIGNMENTS = myAlignments,
    REFERENCE.GENOME = myGenome,
    LIBRARY.SIZE = 12799826 #usually not necessary
)$clusters

💡 Keep in mind that you just run a demo. These are not representative clusters! Though you can apply these steps to your organism of choice and sequencing data. Have fun with PICB and your piRNA clusters!

Special thanks to all contributors and supporters that starred this repository.

Our authors:

Our PICB-team:

Visit the lab website of Astrid D. Haase, M.D., Ph.D.

Our Co-authors and support:

This project is licensed under the CC0.1.0 license - see the LICENSE file for details.

Do you like this project? Please join us or give a ⭐. Let us make piRNA clusters more comparable and easy to build!

• Protocol for assembling, prioritizing, and characterizing piRNA clusters using the piRNA Cluster Builder (2025) by Franziska Ahrend*, Parthena Konstantinidou, Zuzana Loubalova, Yuejun Wang, Hernan Lorenzi, Gunter Meister, Astrid D. Haase*.
• A comparative roadmap of PIWI-interacting RNAs across seven species reveals insights into de novo piRNA-precursor formation in mammals (2024) by Parthena Konstantinidou*, Zuzana Loubalova*, Franziska Ahrend*, Aleksandr Friman*, Miguel Vasconcelos Almeida, Axel Poulet, Filip Horvat, Yuejun Wang, Wolfgang Losert, Hernan Lorenzi, Petr Svoboda, Eric A. Miska, Josien C. van Wolfswinkel, Astrid D. Haase*.
• Dynamic co-evolution of transposable elements and the piRNA pathway in African cichlid fishes (2024) by Miguel Vasconcelos Almeida*, Moritz Blumer, Chengwei Ulrika Yuan, Pío Sierra, Jonathan L. Price, Fu Xiang Quah, Aleksandr Friman, Alexandra Dallaire, Grégoire Vernaz, Audrey L. K. Putman, Alan M. Smith, Domino A. Joyce, Falk Butter, Astrid D. Haase, Richard Durbin, M. Emília Santos, Eric A. Miska*.

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