https://academic.oup.com/bib/article/22/3/bbaa123/5868070

Address of the bookmark: https://github.com/hzi-bifo/Quasimodo

Address of the bookmark: https://proksee.ca/

ABySS Explorer

Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata

http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

BamView

Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation

http://www.sanger.ac.uk/resources/software/artemis/

DNannotator 

Annotation web toolkit for regional genomic sequences

http://bioapp.psych.uic.edu/DNannotator.htm

JVM 

Java Visual Mapping tool for NGS reads

http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501

LookSeq 

Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation

http://lookseq.sourceforge.net

MagicViewer 

Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome

http://bioinformatics.zj.cn/magicviewer/

MapView 

Alignments of huge-scale single-end and pair-end short reads

http://omictools.com/mapview-s1367.html

MultiPipMaker

Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a ‘percent identity plot’ (pip)

http://pipmaker.bx.psu.edu/pipmaker/

PileLineGUI 

Handling genome position files in NGS studies

http://sing.ei.uvigo.es/pileline/pilelinegui.html

SAMtools tview 

Simple and fast text alignment viewer; NGS compatible

http://www.htslib.org/

SEWAL

Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run

http://www.sourceforge.net/projects/sewal

STAR 

A web-based integrated solution to management and visualization of sequencing data

http://wanglab.ucsd.edu/star/browser

SVA 

Software for annotating and visualizing sequenced human genomes

http://www.svaproject.org

Viewer (IGV) 

Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution

https://www.broadinstitute.org/igv/

ZOOM Lite 

NGS data mapping and visualization software

http://bioinfor.com/zoom/lite/

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

http://molbiol-tools.ca/Convert.htm

Address of the bookmark: http://molbiol-tools.ca/Convert.htm

##################################################
[Mon May 7 11:29:18 2018] Reduction...
#file name genome size contigs heterozygous size [%] heterozygous contigs [%] identity [%] possible joins homozygous size [%] homozygous contigs [%]
/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
test/run1/contigs.fa 163897 245 66377 40.50 221 90.20 94.854 0 97520 59.50 24 9.80

##################################################
[Mon May 7 11:29:29 2018] Estimating parameters of libraries...
Aligning 19504 mates per library...
Insert size statistics Mates orientation stats
FastQ files read length median mean stdev FF FR RF RR
test/5000_1.fq.gz test/5000_2.fq.gz 50 4998 4990.20 721.47 0 4674 0 0
test/600_1.fq.gz test/600_2.fq.gz 100 599 598.63 47.68 0 10000 0 0

##################################################
[Mon May 7 11:29:29 2018] Scaffolding...
iteration 1.1: test/run1/contigs.reduced.fa 24 97520 39.355 17 94157 7321 2195 0 29603
19505 pairs. 17302 passed filtering [88.71%]. 1627 in different contigs [8.34%].
1526 pairs. 558 in different contigs [36.57%].
iteration 1.2: test/run1/_sspace.1.1.fa 3 97626 39.344 3 97626 87536 6063 821 87536
19505 pairs. 17607 passed filtering [90.27%]. 182 in different contigs [0.93%].
1077 pairs. 124 in different contigs [11.51%].
iteration 2.1: test/run1/_sspace.1.2.fa 3 97626 39.344 3 97626 87536 6063 821 87536
19505 pairs. 15112 passed filtering [77.48%]. 1295 in different contigs [6.64%].
3417 pairs. 396 in different contigs [11.59%].
iteration 2.2: test/run1/_sspace.2.1.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
19505 pairs. 15152 passed filtering [77.68%]. 0 in different contigs [0.00%].
3398 pairs. 0 in different contigs [0.00%].

##################################################
[Mon May 7 11:29:34 2018] Gap closing...
iteration 1.1: test/run1/scaffolds.fa 1 99133 39.344 1 99133 99133 99133 2328 99133

##################################################
[Mon May 7 11:29:35 2018] Final reduction...
#file name genome size contigs heterozygous size [%] heterozygous contigs [%] identity [%] possible joins homozygous size [%] homozygous contigs [%]
[WARNING] Nothing reduced!
test/run1/scaffolds.filled.fa 99390 1 0 0.00 0 0.00 0.000 0 99390 100.00 1 100.00

##################################################
[Mon May 7 11:29:35 2018] Reporting statistics...
#fname contigs bases GC [%] contigs >1kb bases in contigs >1kb N50 N90 Ns longest
test/contigs.fa 245 163897 40.298 24 117391 3975 233 0 29603
test/run1/contigs.fa 245 163897 40.298 24 117391 3975 233 0 29603
test/run1/contigs.reduced.fa 24 97520 39.355 17 94157 7321 2195 0 29603
test/run1/_sspace.1.1.fa 3 97626 39.344 3 97626 87536 6063 821 87536
test/run1/_sspace.1.2.fa 3 97626 39.344 3 97626 87536 6063 821 87536
test/run1/_sspace.2.1.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/_sspace.2.2.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/scaffolds.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/_gapcloser.1.1.fa 1 99390 39.689 1 99390 99390 99390 2 99390
test/run1/scaffolds.filled.fa 1 99390 39.689 1 99390 99390 99390 2 99390
test/run1/scaffolds.reduced.fa 1 99390 39.689 1 99390 99390 99390 2 99390

##################################################
[Mon May 7 11:29:35 2018] Cleaning-up...
#Time elapsed: 0:00:17.376924

In this tutorial, we will show you how to install and configure Elgg social networking platform on Ubuntu 20.04.

Prerequisites

• A fresh Ubuntu 20.04 VPS on the Atlantic.net Cloud Platform
• A valid domain name pointed to your server IP
• A root password configured on your server

Step 1 – Create Atlantic.Net Cloud Server

First, log in to your Atlantic.Net Cloud Server. Create a new server, choosing Ubuntu 20.04 as the operating system with at least 2GB RAM. Connect to your Cloud Server via SSH and log in using the credentials highlighted at the top of the page.

Once you are logged in to your Ubuntu 20.04 server, run the following command to update your base system with the latest available packages.

apt-get update -y

Step 2 – Install Apache, MariaDB and PHP

Elgg runs on Apache web server, is written in PHP, and uses MySQL/MariaDB as a database backend, so you will need to install the Apache, MariaDB, PHP and other required PHP extensions to your server. You can install all of them with the following command:

apt-get install apache2 mariadb-server php libapache2-mod-php php-common php-sqlite3 php-curl 
php-intl php-mbstring php-xmlrpc php-mysql php-gd php-xml php-cli php-zip unzip wget -y

After installing all the packages, edit the php.ini file and change some recommended settings.

nano /etc/php/7.4/apache2/php.ini

Change the following values:

max_execution_time = 300
memory_limit = 512M
upload_max_filesize = 100M
date.timezone = Asia/Kolkata

Save and close the file, then restart the Apache service to apply the configuration changes.

systemctl restart apache2

Step 3 – Create a Database for Elgg

Next, you will need to create a database and user for Elgg. First, log in to MySQL shell with the following command:

mysql

Once logged in, create a database and user with the following command:

CREATE DATABASE elgg;
CREATE USER 'elgg'@'localhost' IDENTIFIED BY 'secure-password';

Next, grant all the privileges to the elgg database with the following command:

GRANT ALL ON elgg.* TO 'elgg'@'localhost' IDENTIFIED BY 'secure-password' WITH GRANT 
OPTION;

Next, flush the privileges and exit from the MariaDB shell with the following command:

FLUSH PRIVILEGES;
EXIT;

At this point, the MariaDB database is created for Elgg.

Step 4 – Install Elgg

First, download the latest version of Elgg from its official website using the following command:

wget https://elgg.org/download/elgg-3.3.13.zip

Once the download is completed, unzip the downloaded file with the following command:

unzip elgg-3.3.13.zip

Next, move the extracted directory to the Apache root directory:

mv elgg-3.3.13 /var/www/html/elgg

Next, create a data directory and set proper ownership and permissions to the Elgg directory:

mkdir /var/www/html/data
chown -R www-data:www-data /var/www/html/elgg
chown -R www-data:www-data /var/www/html/data
chmod -R 755 /var/www/html/elgg

Once you are finished, you can proceed to the next step.

Step 5 – Configure Apache for Elgg

Next, you will need to configure Apache to serve Elgg. You can configure it by creating a new Apache virtual host configuration file:

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

Add the following lines:

<VirtualHost *:80>
ServerAdmin admin@example.com
DocumentRoot /var/www/html/elgg/
ServerName elgg.example.com
Options FollowSymLinks
AllowOverride All
ErrorLog /var/log/apache2/elgg-error_log
CustomLog /var/log/apache2/elgg-access_log common
</VirtualHost>

Save and close the file, then enable the virtual host and Apache rewrite module with the following command:

a2ensite elgg.conf
a2enmod rewrite

Finally, restart the Apache service to apply the changes:

systemctl restart apache2

Step 6 – Access Elgg Web Interface

Now, open your web browser and access the Elgg web interface using the URL http://elgg.example.com. You should see the Elgg welcome screen:

Again, running spades.py will show you the options:

spades.py

This produces:

SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o <output_dir>

Basic options:
-o      <output_dir>    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    <filename>      file with interlaced forward and reverse paired-end reads
-1      <filename>      file with forward paired-end reads
-2      <filename>      file with reverse paired-end reads
-s      <filename>      file with unpaired reads
--pe<#>-12      <filename>      file with interlaced reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-1       <filename>      file with forward reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-2       <filename>      file with reverse reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-s       <filename>      file with unpaired reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-<or>    orientation of reads for paired-end library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--s<#>          <filename>      file with unpaired reads for single reads library number <#> (<#> = 1,2,..,9)
--mp<#>-12      <filename>      file with interlaced reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-1       <filename>      file with forward reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-2       <filename>      file with reverse reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-s       <filename>      file with unpaired reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-<or>    orientation of reads for mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--hqmp<#>-12    <filename>      file with interlaced reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-1     <filename>      file with forward reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-2     <filename>      file with reverse reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-s     <filename>      file with unpaired reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-<or>  orientation of reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--nxmate<#>-1   <filename>      file with forward reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--nxmate<#>-2   <filename>      file with reverse reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--sanger        <filename>      file with Sanger reads
--pacbio        <filename>      file with PacBio reads
--nanopore      <filename>      file with Nanopore reads
--tslr  <filename>      file with TSLR-contigs
--trusted-contigs       <filename>      file with trusted contigs
--untrusted-contigs     <filename>      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  <cp>    restart run with updated options and from the specified check-point ('ec', 'as', 'k<int>', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       <filename>      file with dataset description in YAML format
-t/--threads    <int>           number of threads
                                [default: 16]
-m/--memory     <int>           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       <dirname>       directory for temporary files
                                [default: <output_dir>/tmp]
-k              <int,int,...>   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    <float>         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  <33 or 64>      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]

As you can see this is also a “pipeline” of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:

spades.py -t 4 \
          -m 32 \
          -k 31,51,71 \
          --only-assembler \
          -1 miseq.1.fastq -2 miseq.2.fastq \
          --nanopore minion.fastq \
          -o hybrid_assembly

In turn, these parameters mean

• use 4 threads
• max memory is 32Gb
• use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71
• only run the assembler, not the correction algorithm (for speed)
• read 1 and read 2 of the MiSeq data
• the nanopore data
• put the output in folder “hybrid_assembly”

Following are the genome assembly tools based on string graph:

1.SGA (String Graph Assembler) https://github.com/jts/sga

Assembles large genomes from high coverage short read data. SGA is designed as a modular set of programs, which are used to form an assembly pipeline. SGA implements a set of assembly algorithms based on the FM-index. As the FM-index is a compressed data structure, the algorithms are very memory efficient. The SGA assembly has three distinct phases. The first phase corrects base calling errors in the reads. The second phase assembles contigs from the corrected reads. The third phase uses paired end and/or mate pair data to build scaffolds from the contigs. The output of this software is a PDF report that allows the properties of the genome and data quality to be visually explored. By providing more information to the user at the start of an assembly project, this software will help increase awareness of the factors that make a given assembly easy or difficult, assist in the selection of software and parameters and help to troubleshoot an assembly if it runs into problems.

2. SAGE: String-overlap Assembly of GEnomes https://github.com/lucian-ilie/SAGE2

SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.

3. FSG: Fast String Graph

The new integrated assembler has been assessed on a standard benchmark, showing that fast string graph (FSG) is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads. Moreover, we have studied the effect of coverage rates on the running times.

4.  BASE https://github.com/dhlbh/BASE

It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs. BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

5. Fermi https://github.com/lh3/fermi/

Fermi is a de novo assembler with a particular focus on assembling Illumina short sequence reads from a mammal-sized genome. In addition to the role of a typical assembler, fermi also aims to preserve heterozygotes which are often collapsed by other assemblers. Its ultimate goal is to find a minimal set of unitigs to represent all the information in raw reads.

If you want to learn about String Graph assembler, please read the following papers -

i) The Fragment Assembly String Graph - E. W. Myers

This paper describes the String Graph concept.

ii) Efficient construction of an assembly string graph using the FM-index - Jared T. Simpson and Richard Durbin

This earlier paper from Simpson and Durbin

iii) Efficient de novo assembly of large genomes using compressed data structures - Jared T. Simpson and Richard Durbin

y a number of metrics, including:

• Overall assembly length
• Number of scaffolds/contigs
• Length of longest scaffold/contig
• Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns
• CEGMA completeness
• Scaffold/contig length/count distribution

assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.

Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.

Address of the bookmark: https://github.com/rjchallis/assembly-stats