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findGSEP

R-CMD-check

Accurate estimating genome size is a crucial task in sequencing projects. Current methods often struggle with polyploidy or become inefficient when dealing with species that exceed a ploidy level of six. To address these challenges, we introduce findGSEP, an enhanced version of findGSE. findGSEP utilizes a segmented fitting approach to fit a normal distribution to polyploid species within a segmented framework. This ap-proach simplifies the process of single fitting while significantly expanding the range of ploidy levels it can handle. Moreover, findGSEP offers users interactive tools through both an open-source R application and a web application, facilitating reliable and precise estimation of genome size.

News 🌟

We have released our backend-server findGSEP and provide a CPU-based version of findGSEP online platform. Please check it out!!!

Installation & Usage

Instructions for running Jellyfish:

  1. Download and install jellyfish from: Jellyfish Release

  2. Count kmers using jellyfish:

    Note: Adjust the memory (-s) and threads (-t) parameters according to your server. This example uses 1 thread and 5GB of RAM. The kmer length (-m) may need to be scaled if you have low coverage or a high error rate. Always use ‘canonical kmers’ (-C).

  3. Export the kmer count histogram:

    Note: The thread count (-t) should be scaled according to your server.

  4. Upload reads.histo to findGSEP.

Using KMC:

  1. Download and install KMC from: KMC GitHub

  2. Count kmers using KMC:

    Note: Adjust the memory (-m) and threads (-t) parameters according to your server. This example uses 1 thread and 5GB of RAM. The kmer length (-k) may need to be scaled if you have low coverage or a high error rate. The -ci1 option ensures that kmers with a count of at least 1 are included.

  3. Export the kmer count histogram:

    Note: This will create the histogram file reads_kmc.histo.

  4. Upload reads_kmc.histo to findGSEP.

Instructions for installing findGSEP package

  1. Install devtools:
install.packages("devtools")
  1. Install directly from GitHub:
devtools::install_github("sperfu/findGSEP")

Data

You can check our demo dataset at our webserver or drive for complete data. We have provide precalculated histo file whose ploidy number ranging from tetraploid to octoploid.

Usage:

# Set options (optional):

options(warn = -1)

# Define input parameters:

path <- "histo_files"
samples <- "your_file.histo"
sizek <- 21
exp_hom <- 200
ploidy <- 4
output_dir <- "outfiles"
xlimit <- -1
ylimit <- -1
range_left <- exp_hom * 0.2
range_right <- exp_hom * 0.2

#Call the findGSEP function with specified parameters:

findGSEP(path, samples, sizek, exp_hom, ploidy, range_left, range_right, xlimit, ylimit, output_dir)

# For any questions, usage inquiries, or reporting potential bugs, please contact the author.

After running, You will find ‘your_file.histo_hap_genome_size_est.pdf’ in your output_dir folder, please give it a try!!!

Parameter settings

You can reference to our paramenter setting for those species we used in our webserver or demo dataset.

Species Expected Hom(Mb) Ploidy number Size k
Chinese sturgeon 100 8 21
Strawberry 100 8 21
Wheat 150 6 21
Redwood 80 6 21
Cotton 150 4 21
Javanica 200 4 21
Potato 180 4 21
Floridensis 220 4 21
Crayfish 35 3 21
Enterolobii 130 3 21
Incognita 200 3 21
Seabass 80 2 21
Bird 40 2 21
Drosophila 50 2 21
Pear 100 2 21
Oyster 50 2 21

Note:

If you enconter problem when installing devtools, especially for those packages below, please consider install them using conda install command:

conda install -c conda-forge r-gert
conda install -c conda-forge r-textshaping
conda install -c conda-forge r-ragg
conda install -c conda-forge r-pkgdown

If you enconter issues like:

  1. could not find function “brewer.pal”

  2. could not find function “alpha”

Solutions:

library(RColorBrewer)
library(ggplot2)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.