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

https://salzberg-lab.org/

What Are Chromosome Rearrangements?

Chromosome rearrangements are structural changes that alter the normal configuration of chromosomes. These changes can involve large segments of DNA — from thousands to millions of base pairs — and can occur spontaneously, be inherited, or result from exposure to mutagens (like radiation or chemicals).

Common Types of Rearrangements:

1. Deletions – Loss of a chromosome segment

2. Duplications – Repetition of a segment

3. Inversions – A segment breaks off, flips, and reattaches

4. Translocations – Segments exchange places between non-homologous chromosomes

5. Insertions – A segment is inserted into another part of the genome

These changes can disrupt genes directly or affect gene regulation, leading to disease.

How Do Chromosome Rearrangements Cause Disease?

The impact of a rearrangement depends on which genes are involved, how much DNA is affected, and when the rearrangement occurs (in development vs. adulthood). Here are some key mechanisms:

• Gene disruption: Breaking a gene can lead to loss of function or the creation of a non-functional protein.

• Gene fusion: Joining parts of two genes may form a novel hybrid gene with new functions (common in cancer).

• Dosage effects: Extra or missing gene copies can disturb the balance of gene expression.

• Position effects: Moving a gene to a new regulatory environment may silence or over-activate it.

Chromosome Rearrangements in Human Disease

1. Developmental Disorders

• Cri-du-chat syndrome: Caused by a deletion on chromosome 5p. Affected infants often have a high-pitched cry and intellectual disability.

• Williams syndrome: Results from a microdeletion on chromosome 7q, affecting genes related to cardiovascular and cognitive function.

2. Cancer

Cancer is perhaps the most striking example of disease caused by chromosome rearrangements.

• Chronic Myeloid Leukemia (CML): Caused by a translocation between chromosomes 9 and 22, forming the Philadelphia chromosome. This creates the BCR-ABL fusion gene, which drives uncontrolled cell growth.

• Burkitt lymphoma: Involves translocation of the MYC gene, leading to excessive cell division.

• Ewing sarcoma: A fusion of EWSR1 and FLI1 genes through translocation promotes tumor development.

3. Infertility and Miscarriages

Balanced rearrangements (like inversions or translocations) in carriers may not cause disease directly but can result in:

• Recurrent miscarriages

• Infertility

• Birth defects in offspring

Detecting Rearrangements

Thanks to modern genomics, chromosome rearrangements can now be detected with high precision using:

• Karyotyping – Classic method for detecting large rearrangements

• FISH (Fluorescence In Situ Hybridization) – Uses fluorescent probes to target specific DNA sequences

• Array CGH (Comparative Genomic Hybridization) – Detects copy number changes across the genome

• Whole Genome Sequencing (WGS) – Identifies even small or complex rearrangements at base-pair resolution

Looking Forward: The Future of Chromosome Medicine

Understanding chromosome rearrangements is now central to:

• Personalized medicine

• Genetic counseling

• Targeted therapies, especially in cancer (e.g., tyrosine kinase inhibitors for BCR-ABL fusion)

With the rise of long-read sequencing and single-cell genomics, even previously “invisible” rearrangements are being uncovered, offering new insights into both rare diseases and common conditions.

Final Thoughts

Chromosome rearrangements remind us that genetics isn't just about which genes we have — but where they are, how they're arranged, and when they're active. As our tools grow sharper, so does our ability to diagnose, understand, and treat diseases rooted in genomic architecture.

In a way, the genome is like a book not just defined by its words, but also by how the chapters are ordered. Rearranging them can create a new story — sometimes harmful, sometimes insightful — and understanding these changes is key to writing a healthier future.

Address of the bookmark: http://www.vital-it.ch/

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

Canu is a hierachical assembly pipeline which runs in four steps:

• Detect overlaps in high-noise sequences using MHAP
• Generate corrected sequence consensus
• Trim corrected sequences
• Assemble trimmed corrected sequences

Read the documentation

New release https://github.com/marbl/canu/releases

Address of the bookmark: https://github.com/marbl/canu

Main features of YASS are:

• multiple, possibly overlapping seeds and a new hit criterion to ensure a good sensitivity/selectivity trade-off
• transition-constrained spaced seeds to improve sensitivity (transition mutations are purine to purine [A<->G] or pyrimidine to pyrimidine [C<->T])
• using different scoring schemes with bit-score and E-value evaluated according to the sequence background frequencies
• parameterizable output filter for low complexity repeats
• reporting of various alignment statistical parameters (mutation bias along triplets, transition/transversion)
• post-processing step to group gapped alignments

Address of the bookmark: http://bioinfo.lifl.fr/yass/

Address of the bookmark: https://github.com/al2na/methylKit

Anvi’o is an analysis and visualization platform for ‘omics data.

Please find the methods paper here: https://peerj.com/articles/1319/

Anvi’o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper

An analysis and visualization platform for 'omics data http://merenlab.org/projects/anvio

Paper https://peerj.com/articles/1839/

Address of the bookmark: https://github.com/meren/anvio