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Elgg Installation steps !

Abhi — Wed, 07 Sep 2022 00:43:53 -0500

Elgg is an open source social networking engine that allows the creation of social environments such as campus social networks and internal collaborative platforms for organizations. Elgg offers a number of social networking features including microblogging, messaging, file-sharing and groups. This tutorial will guide you through the process of installing Elgg on a Ubuntu 18.04 VPS.

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.


     DocumentRoot /var/www/html/
     ServerName example.com
     
          Options FollowSymlinks
          AllowOverride All
          Require all granted
     
     ErrorLog ${APACHE_LOG_DIR}/error.log
     CustomLog ${APACHE_LOG_DIR}/access.log combined

Enable the configuration and restart the Apache server.

 sudo a2ensite elgg.conf
 sudo systemctl restart apache2.service

Unicycler: Hybrid assembly pipeline for bacterial genomes

Jit — Fri, 10 Nov 2017 03:58:27 -0600

Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Address of the bookmark: https://github.com/rrwick/Unicycler

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

transrate: Understanding your transcriptome assembly

Neel — Fri, 13 Jul 2018 07:49:26 -0500

Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.

Address of the bookmark: http://hibberdlab.com/transrate/index.html

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

NextDenovo: string graph-based de novo assembler for TGS long reads

Jit — Sun, 05 Jan 2020 04:08:29 -0600

NextDenovo is a string graph-based de novo assembler for TGS long reads. It uses a "correct-then-assemble" strategy similar to canu, but requires significantly less computing resources and storages. After assembly, the per-base error rate is about 97-98%, to further improve single base accuracy, please use NextPolish.

NextDenovo contains two core modules: NextCorrect and NextGraph. NextCorrect can be used to correct TGS long reads with approximately 15% sequencing errors, and NextGraph can be used to construct a string graph with corrected reads. It also contains a modified version of minimap2 for adapting input and output and producing more sensitive and accurate dovetail overlaps, and some useful utilities (see here for more details).

Address of the bookmark: https://github.com/Nextomics/NextDenovo

MEC: Contig Misassembly Correction

BioStar — Tue, 04 Feb 2020 23:40:49 -0600

MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.

Address of the bookmark: https://github.com/bioinfomaticsCSU/MEC

SvABA: Structural variation and indel detection by local assembly

Jit — Tue, 10 Mar 2020 07:52:15 -0500

Address of the bookmark: https://github.com/walaj/svaba

VICUNA: a software tool that enables consensus assembly of ultra-deep sequence derived from diverse viral or other heterogeneous populations.

biogeek — Tue, 25 Aug 2020 03:40:17 -0500

VICUNA is a de novo assembly program targeting populations with high mutation rates. It creates a single linear representation of the mixed population on which intra-host variants can be mapped. For clinical samples rich in contamination (e.g., >95%), VICUNA can leverage existing genomes, if available, to assemble only target-alike reads. After initial assembly, it can also use existing genomes to perform guided merging of contigs. For each data set (e.g., Illumina paired read, 454), VICUNA outputs consensus sequence(s) and the corresponding multiple sequence alignment of constituent reads. VICUNA efficiently handles ultra-deep sequence data with tens of thousands fold coverage.

http://software.broadinstitute.org/viral/docs/vicuna_v1.0.pdf

Address of the bookmark: https://www.broadinstitute.org/viral-genomics/vicuna