Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

1. Prepare a reference sequence (ref.fasta). Better a bacterial genome.
2. Download maq, maq-data and maqview at the download page.
3. Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
4. Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:

   maq.pl demo ref.fasta calib-30.dat
   

   where calib-30.dat is contained in maq-data.
5. View the alignment:

   cd maqdemo/easyrun;
   maqindex -i -c consensus.cns all.map;
   maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

Address of the bookmark: http://maq.sourceforge.net

Download

Git repository of SWALO is at https://github.com/atifrahman/SWALO.

Address of the bookmark: https://atifrahman.github.io/SWALO/

Address of the bookmark: https://github.com/alastair-droop/fqtools

Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation. SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.

Structure variation sequencing signature (Source: NatRev Genetics)

Related tools, databases and publications are listed below. If you know any interesing papers, please let us know in comment section:

Key concepts

Structural variation includes balanced variants such as inversions and translocations, and unbalanced ones such as duplications and deletions (copy number variations or CNVs).

Structural variants can arise by several mechanisms, including nonallelic homologous recombination (NAHR), nonhomologous end‐joining (NHEJ) and DNA replication‐based fork stalling and template switching (FoSTeS).

CNV is closely linked to segmental duplication, but is not exactly the same. Segmental duplications can stimulate CNV formation by NAHR, and themselves arise from CNVs that have become fixed.

Segmental duplications did not appear uniformly during the evolution of the Great Ape species, but rather during a burst of activity around the time of the divergence of gorilla from the human/chimpanzee ancestor.

Duplicated genes play a critical role in the evolution of a genome as they act as ‘spare parts’ than can evolve to perform new or more specialized functions.

Effects of structural variation on gene expression can be identified but only a few examples of the consequences for species biology have been documented.

Tools

CNVnatora tool for CNV discovery and genotyping from depth of read mapping.2011a,2011b

AGEa tools that implements an algorithm for optimal alignment of sequences with SVs.2011

BreakSeqa pipeline for annotation, classification and analysis of SVs at single nucleotide resolution.2010

PEMera computational and simulation framework for discovering SVs by paired-end read mapping.2009,2007

GASV https://code.google.com/archive/p/gasv/

PAIROSCOPE http://pairoscope.sourceforge.net/

SVDetect http://svdetect.sourceforge.net/Site/Home.html

BreakPtr, discovery of unbalanced structural variants (copy-number variants) with tiling microarrays Link 

R Package https://www.bioconductor.org/help/course-materials/2010/EMBL2010/Practical-4-StructuralVariants.pdf

BreakSeq, structural variant genotyping using split reads Link 

CopySeq, genotyping of unbalanced structural variants (copy-number variants) using read-depth Link 

DELLY2, integrated structural variant discovery, genotyping and visualization in deep sequencing data Link 

PEMer, structural variant discovery in 454 sequencing data by paired-end mapping Link 

TIGER, transduction inference in germline genomes using short read data Link 

MANTA https://github.com/Illumina/manta

SV-Bay https://github.com/InstitutCurie/SV-Bay

BreakDancer http://breakdancer.sourceforge.net/

Variation Hunter http://compbio.cs.sfu.ca/software-variation-hunter

Lumpy https://github.com/arq5x/lumpy-sv

ForestSV http://sebatlab.ucsd.edu/index.php/software-data 

PBSuites for long reads https://sourceforge.net/projects/pb-jelly/

Visualization

The SV visualization tool: http://genomesavant.com/savant/

InGAP-SV (http://ingap.sourceforge.net/) that is nice tools for both detection and visualisation of severals kind of structural variations (Large insertions, translocation, deletion, inversions....) 

Tools table: http://www.nature.com/nbt/journal/v29/n8/fig_tab/nbt.1904_T2.html

Variation Viewer https://www.ncbi.nlm.nih.gov/variation/view/

Papers

http://www.nature.com/nmeth/journal/v9/n2/full/nmeth.1858.html

http://journal.frontiersin.org/researchtopic/1412/structural-variations-in-genomes-ecological-and-evolutionary-implications

http://www.mi.fu-berlin.de/wiki/pub/ABI/GenomicsLecture10Materials/structural-variation.pdf

http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1479-3

https://www.ncbi.nlm.nih.gov/dbvar/content/overview/

http://www.nature.com/subjects/structural-variation

https://eichlerlab.gs.washington.edu/news/NatMeth_Feb2012.pdf

https://www.ncbi.nlm.nih.gov/pubmed/19477992 ***

https://www.ncbi.nlm.nih.gov/pubmed/22452995

http://biorxiv.org/content/early/2016/09/06/073833

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479793/

http://www.nature.com/articles/srep18501

http://www.genetics.org/content/202/1/351

http://www.cs.cmu.edu/~sssykim/teaching/s13/slides/Lecture_SVI.pdf

https://www.omicsonline.org/open-access/structural-variation-detection-from-next-generation-sequencing-2469-9853-S1-007.php?aid=69055

http://schatzlab.cshl.edu/presentations/2016/2016.01.12.PAG.Structural%20Variations.pdf

The arguments accepted by ScaffMatch are:

  -w) Working directory -- this is the directory where ScaffMatch files are stored. These are .sam files produced after mapping reads to contigs and the resulting scaffolds file `scaffolds.fa` fasta file;

  -c) Contig fasta file;

  -m) Command line argument with no options. It is used when .sam files are used instead of reads .fastq files. Do not use this option if you provide reads files;

  -1) (Comma separated list of) either .fastq or .sam file(s) corresponding to the first read of the read pair;

  -2) (Comma separated list of) either .fastq or .sam file(s) corresponding to the second read of the read pair;

  -i) (Comma separated list of) insert size(s) of the library(-ies);

  -s) (Comma separated list of) library(-ies) standard deviation(s) of insert size(s);

  -t) Bundle threshold. Pairs of contigs supported by number of read pairs less than the value of this argument are discarded. Optional argument, by default it is equal to 5;

  -g) Matching heuristics: use `max_weight` for Maximum Weight Matching heuristics with the Insertion step, use `backbone` for Maximum Weight Matching heuristics without the Insertion step, use `greedy` for Greedy Matching heuristics;

  -l) Log file - where to store the logs. Optional argument. By default, stdout is used.

Address of the bookmark: http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch

Address of the bookmark: https://github.com/mahulchak/quickmerge

More at http://chibba.agtec.uga.edu/duplication/mcscan/

Address of the bookmark: http://chibba.agtec.uga.edu/duplication/mcscan/

C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth (2015). doi:10.1038/nmeth.3505.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html

Address of the bookmark: https://github.com/hall-lab/speedseq

Address of the bookmark: http://www.australianprostatecentre.org/research/software/jcircos

With Single Molecule, Real-Time (SMRT) Sequencing, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:

• Uncover missing heritability linked to structural variation
• Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease
• Resolve structural variation across the complete size spectrum with basepair resolution

Following are the SV tools, which can assist you to achieve your goal.

Sniffles: Structural variation caller using third generation sequencing

Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled! 

More at https://github.com/fritzsedlazeck/Sniffles

MultiBreak-SV: It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.

There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.

More at https://github.com/raphael-group/multibreak-sv

Parliament: A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?

Publication about the algorithm and “…the first long-read characterization of structural variation in a diploid human personal genome…” (HS1011) - “Assessing structural variation in a personal genome—towards a human reference diploid genome”

More at https://sourceforge.net/projects/parliamentsv/

https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf

PBHoney: the structural variation discovery tool 

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract

More at https://sourceforge.net/projects/pb-jelly/

SMRT-SV: Structural variant and indel caller for PacBio reads

Structural variant (SV) and indel caller for PacBio reads based on methods from Chaisson et al. 2014.

SMRT-SV provides an official software package for tools described in Chaisson et al. 2014 and adds several key features including the following.

• Unified variant calling user interface with built-in cluster compute support
• Small indel calling (2-49 bp)
• Improved inversion calling (screenInversions)
• Quality metric for SV calls based on number of local assemblies supporting each call
• Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions
• Genotyping of SVs with Illumina paired-end reads from WGS samples

More at https://github.com/EichlerLab/pacbio_variant_caller