Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

• It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
• It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

Address of the bookmark: http://www.phrap.org/consed/consed.html

Metagenomic simulators are a way of dealing with this issue; they produce artificial sequencing datasets in which both the composition of the microorganisms and the expected results are known. Researchers can then test a pipeline on such a controlled dataset and see precisely how well it functions. However, as metagenomic studies have become larger, more diverse, and more complex, simply generating artificial reads is no longer sufficient. The simulators now have to replicate community variation, sequencing errors, different sequencing platforms, massive data volumes, and even laboratory-based biases.

MIDASim (2024) was designed with a particular emphasis on microbial community dynamics. Unlike previous tools which regarded each simulated sample as individual, MIDASim is able to efficiently generate realistic multi-sample microbiome datasets, covering the cases where changes over time are being tracked. This feature is particularly useful when investigating how microbial communities vary between people, different experimental setups, or at various times. By overcoming the computational limitations of earlier simulation methods, MIDASim has made it easier to carry out large and complex microbiome simulation studies.

A major challenge is scale. Since modern metagenomic projects can involve millions or even billions of sequencing reads, generating such large synthetic datasets can become a significant computational problem. In order to tackle this issue, Izzy (2025) was developed. As a high-throughput metagenomic read simulator, Izzy is designed with speed in mind while still reproducing the typical patterns of sequencing errors. The people who developed it stated that it can be as much as 60 times faster than earlier tools, which makes large-scale simulations far more practical. Now, researchers can begin to ask not how long a simulation will take but rather what size dataset they want to test.

At the same time, the sequencing technology became more diverse. Researchers were not any longer restricted to using Illumina short reads since Oxford Nanopore and PacBio long-read sequencing also became important in the field of metagenomic research. In order to address these new requirements, MeSS (Metagenomic Sequence Simulator, 2025) was developed. The programme is based on a Snakemake framework and is therefore compatible with the Illumina, Oxford Nanopore and PacBio platforms. MeSS has also been designed to operate efficiently and to use less memory than earlier tools; it provides ready-made templates for the microbiomes of different sites in the human body, thus giving researchers a simple means of beginning to generate realistic simulated communities.

The simulation problem became even more specialized. Suppose that sequencing is not based on uniform sampling of DNA? In the case of targeted sequencing and hybridization-capture experiments, particular sequences are deliberately enriched, and the probability of capturing a target depends on both the probe and the target sequence. A standard model based on uniform sampling might fail to take into account this important feature of real experiments. That is why RAmpSim has been designed specifically to handle simulations for capture-based and targeted sequencing. Instead of depending solely on the assumption of uniform sampling, it includes a thermodynamic nearest-neighbor energy model to represent probe–target interactions and sequence-dependent enrichment. As a result, it is especially suitable for applications such as hybridization capture in metagenomics, where the efficiency of recovering a sequence can depend strongly on how it relates to the capture probes. RAmpSim thus reflects a wider trend towards simulations that aim not only to reproduce sequencing output but also key aspects of the experimental process itself.

MIDASim, Izzy, MeSS and RAmpSim all demonstrate how fast metagenomic simulation is evolving. MIDASim is able to deal with realistic variation within a large number of and changing microbial communities. Izzy overcomes the problem of generating very large datasets. MeSS provides support for a number of sequencing technologies in an efficient and repeatable manner. RAmpSim increases the experimental realism for capture-based sequencing. Although each of these tools addresses a separate issue, they all contribute to advancing the field.

Modern metagenomic simulation therefore aims not only at producing artificial FASTQ files. Researchers nowadays desire simulated datasets that reflect community changes, incorporate the characteristics of the sequencing platform, include real error patterns, take into account computational scale, and account for experimental biases. As metagenomic workflows become more advanced, the simulators have to keep up with this development. The tools are now going beyond that of simple data generators; they produce controlled versions of complex metagenomic experiments and thus provide researchers with something that ordinary sequencing data seldom does: a dataset in which the answer is known before any analysis takes place.

Prerequisites

• A fresh Vultr Cloud Compute instance with Ubuntu 18.04 and root access.

Step 1: Install Apache, MySQL, and PHP

Elgg requires MySQL, PHP, and a web server. Before you can install Elgg, you will need to install the Apache web server, MySQL, and PHP.

Update the repository list.

apt-get update

Install the Apache web server.

apt-get install apache2 -y

Install MySQL.

apt-get install mysql-server -y

Complete the MySQL installation by executing the following command.

/usr/bin/mysql_secure_installation

During the installation, you will be asked to enter a root password. Enter a secure password. This will be the MySQL root password.

Would you like to setup VALIDATE PASSWORD plugin? [Y/N] N
New password: password
Re-enter new password: password
Remove anonymous users? [Y/N] Y
Disallow root login remotely? [Y/N] Y
Remove test database and access to it? [Y/N] Y
Reload privilege tables now? [Y/N] Y

Install PHP 7.2, as well as the PHP modules required by Elgg.

apt-get install php7.2 libapache2-mod-php7.2 php7.2-common php7.2-sqlite3 php7.2-curl php7.2-intl php7.2-mbstring php7.2-xmlrpc php7.2-mysql php7.2-gd php7.2-xml php7.2-cli php7.2-zip -y

Step 2: Create a MySQL database for Elgg

Elgg will require a MySQL database. Log into the MySQL console.

mysql -u root -p

When prompted for a password, enter the MySQL root password you set in step 1. Once you are logged in to the MySQL console, create a new database.

CREATE DATABASE elgg;

Create a new MySQL user and grant it privileges to the newly created database. You can replace username and password with the username and password of your choice.

GRANT ALL PRIVILEGES on elgg.* to 'username'@'localhost' identified by 'password';
FLUSH PRIVILEGES;

Exit the MySQL console.

exit

Step 3: Download and Install Elgg

Download the latest version of Elgg.

cd /var/www/html
rm -r index.html
wget https://elgg.org/download/elgg-2.3.7.zip

Unzip the downloaded archive and move the files to the root of the Apache web server.

apt install unzip
unzip elgg-2.3.7.zip
mv ./elgg-2.3.7/* . && rm elgg-2.3.7.zip && rm -r elgg-2.3.7

Create a data directory for Elgg.

sudo mkdir -p /var/www/html/data

Set the appropriate file permissions.

sudo chown -R www-data:www-data /var/www/html/
sudo chmod -R 755 /var/www/html/

Step 4: Configure Apache for Elgg

Elgg requires the Apache rewrite module. Enable the Apache rewrite module.

sudo a2enmod rewrite

Create an Apache configuration file for the Elgg installation.

sudo nano /etc/apache2/sites-available/elgg.conf

Paste the following snippet to the file, replacing example.com with your own domain name.

<VirtualHost *:80>
     DocumentRoot /var/www/html/
     ServerName example.com
     <Directory /var/www/html/>
          Options FollowSymlinks
          AllowOverride All
          Require all granted
     </Directory>
     ErrorLog ${APACHE_LOG_DIR}/error.log
     CustomLog ${APACHE_LOG_DIR}/access.log combined
</VirtualHost>

Enable the configuration and restart the Apache server.

 sudo a2ensite elgg.conf
 sudo systemctl restart apache2.service

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

https://cran.r-project.org/web/packages/chromoMap/index.html

We first operate a rapid inspection of the different BAM files using samtools flagstat. Illumina provided chr21 read mapping obtained with their GA IIx deep sequencing platform <ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam>, aligned to the b36/hg18 reference genome)

Address of the bookmark: https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare

Address of the bookmark: https://fuma.ctglab.nl/