BOL: Related items

GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach

Jit — Sat, 01 Oct 2016 15:15:29 -0500

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html

Genome Stability Laboratory

Mon, 07 Mar 2016 04:16:32 -0600

The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims.

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

Nemo – A stochastic, individual-base, genetically explicit simulation platform

Jit — Sat, 01 Oct 2016 14:45:02 -0500

A recombination map has been added for all multi-locus traits. The map positions (chromosomal) for neutral markers (e.g. SNPs) and loci under selection (QTLs, deleterious mutations, DMIs) can now be specified explicitly, or set at random. The map can hold an unlimited number of loci of different types jointly, at any recombination scale (cM or lower). The effects of linkage can thus be finely explored.
A new trait coding for (Bateson-)Dobzhansky-Muller incompatibility loci. Multiple haploid or diploid pairs of incompatible loci can be spread throughout the genome and affect individual fitness.
Multi-type selection: Individual fitness can be jointly determined by different types of loci under selectinon, such as QTLs coding for quantitative traits under spatially variable selection, universally deleterious mutations, and Dobzhansky-Muller incompatibility loci.
An unlimited number of quantitative traits under different forms of selection can be modelled, based on universally pleiotropic loci with several bi- or multi-allelic models.
Spatial and temporal variation of selection on quantitative traits is possible, modelling shifts of environmental conditions over time.
The dispersal matrix describing the movement of individuals among sub-populations can be replaced by a connectivity matrix and a reduced dispersal matrix describing migration only among the connected sub-populations. This offers a substantial gain in computing time and system memory when simulating very large grids.
Input parameters' arguments may be specified in separate files. This is particularly convenient when specifying large matrices.
Many adjustments have been made for refined control of the input of parameters and data output. See updates in the manual.

Address of the bookmark: http://nemo2.sourceforge.net/index.html

Structural variation: the hidden genomic treasure

Jit — Sat, 10 Dec 2016 16:19:09 -0600

Genome re-sequencing projects have revealed substantial amounts of genetic variation between individuals extending beyond single nucleotide polymorphisms (SNPs) and short indels. Structural Variations (SVs) and Copy Number Variations (CNVs) are a major source of genomic variation. However, compared to SNPs, accurate detection, genotyping and understanding of CNVs is lagging behind due to much greater analytical challenges related to SV/CNV detection and analysis. In our lab we analyse SVs/CNVs using high-throughput sequencing and different analytical approaches. The most‐studied structural variants are copy number variations (CNVs) which can be generated by several different mechanisms including non‐allelic homologous recombination, non‐homologous end‐joining and deoxyribonucleic acid (DNA) replication‐related fork stalling and template switching. CNVs are closely related to segmental duplications (SDs): SDs can stimulate the formation of CNVs and themselves started out as CNVs, but became fixed in a species. Structural variation can be neutral but has also influenced our phenotypic evolution, for example our susceptibility to disease and our ability to digest certain types of food. Our understanding of the extent of structural variation is increasing rapidly, but it will be much more difficult to understand its phenotypic consequences.

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

sockeye

Jit — Fri, 17 Feb 2017 08:51:16 -0600

This sockeye software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Mon, 28 Feb 2022 23:27:37 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)


Number of extant species	200
Number of extant genes	4303993
Number of ancestral species	196
Number of ancestral genes	4624213
Number of ancestral synteny blocks	83342

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

Nieduszynski Group

Fri, 26 Sep 2014 19:35:06 -0500

Complete, accurate replication of the genome is essential for life. All chromosomes in eukaryotic cells must be duplicated and then segregated to daughter cells to ensure genetic integrity and produce the large number of cells that make up a multicellular organism. We are using genetic, genomic and computational methods to understand how chromosome replication is regulated to ensure genome stability. By focusing on the basic biology that underpins cell growth and division we aim to provide new insights that may help our understanding of diseases such as cancer and congenital disorders.

More http://www.nieduszynski.org/index.php
http://www.path.ox.ac.uk/research/cell-biology-and-pathology/conrad-nieduszynski-group

A 3D Map of the Human Genome

Fri, 12 Dec 2014 22:27:55 -0600

Suhas Rao and Miriam Huntley (of the Aiden Lab) describe a 3D map of the human genome at kilobase resolution, revealing the principles of chromatin looping. Guest Origami Folding: Sarah Nyquist. Suhas S.P. Rao*, Miriam H. Huntley*, Neva C. Durand, Elena K. Stamenova, Ivan D. Bochkov, James T. Robinson, Adrian L. Sanborn, Ido Machol, Arina D. Omer, Eric S. Lander, Erez Lieberman Aiden. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell.

COSMIC

Jit — Sat, 01 Oct 2016 15:04:10 -0500

The accurate description and annotation of structural variants can be complex. This is due to the different resolution that variants are reported from traditional cytogenetic coordinates down to the actual base pair positions. Furthermore, multiple rearrangements in a single area of the genome can make cataloguing and interpreting their effects challenging.

The Rearrangement Overview page describes the one or more breakpoints which make up a structural variant. A breakpoint is defined as a region or point where the sample sequence has altered from the reference sequence. Minimum interpretation is made of this data. One variant event can consist of one or multiple breakpoints. The Syntax (shown above the table) gives a detailed description of the variant and its location (e.g. chr11:g.36585230_76606619del, a deletion of roughly 40Mb on chromosome 11). Syntax is based on HGVS mutation nomenclature recommendations [http://www.hgvs.org/rec.html].

http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

Address of the bookmark: http://cancer.sanger.ac.uk/cosmic/help/rearrangement/overview

EAGER

Jit — Sat, 10 Dec 2016 18:07:23 -0600

The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.

EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z

Address of the bookmark: https://github.com/apeltzer/EAGER-GUI