BOL: Related items

RefKA: A fast and efficient long-read genome assembly approach for large and complex genomes

Rahul Nayak — Fri, 01 May 2020 03:00:40 -0500

RefKA, a reference-based approach for long read genome assembly. This approach relies on breaking up a closely related reference genome into bins, aligning k-mers unique to each bin with PacBio reads, and then assembling each bin in parallel followed by a final bin-stitching step.

Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

LINKS

Jit — Fri, 04 Nov 2016 06:19:01 -0500

LINKS is a genomics application for scaffolding genome assemblies with long reads, such as those produced by Oxford Nanopore Technologies Ltd. It can be used to scaffold high-quality draft genome assemblies with any long sequences (eg. ONT reads, PacBio reads, another draft genomes, etc)

Paper at https://gigascience.biomedcentral.com/articles/10.1186/s13742-015-0076-3

Address of the bookmark: https://github.com/warrenlr/LINKS/

quarTeT: a telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification.

Abhi — Sat, 08 Jun 2024 15:54:36 -0500

quarTeT is a collection of tools for T2T genome assembly and basic analysis in automatic workflow.

Task include:

AssemblyMapper : reference-guided genome assembly
GapFiller : long-reads based gap filling
TeloExplorer : telomere identification
CentroMiner : centromere candidate prediction

https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false

Address of the bookmark: http://www.atcgn.com:8080/quarTeT/home.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

CrossMap

Jitendra Narayan — Mon, 08 Feb 2016 15:47:00 -0600

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).

It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.

CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.

We do not recommend using CrossMap to convert genome coordinates between species.

More at http://crossmap.sourceforge.net/

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

E-MEM: Efficient computation of Maximal Exact Matches

Jit — Thu, 15 Dec 2016 09:30:43 -0600

E-MEM is a C++/OpenMP program designed to efficiently compute MEMs between large genomes. See the README file for instructions on how to use E-MEM.

E-MEM source code

The source code can be downloaded here.

If you use E-MEM, please cite:

N. Khiste, L. Ilie, E-MEM: Efficient computation of Maximal Exact Matches for very large genomes, Bioinformatics 31(4) (2015) 509 -- 514.

For any questions, please contact Lucian Ilie: ilie@uwo.ca

Address of the bookmark: http://www.csd.uwo.ca/~ilie/E-MEM/

Easyfig

Poonam Mahapatra — Fri, 29 Apr 2016 05:49:39 -0500

Easyfig has moved to github, for newer releases of Easyfig please visit our new webpage - https://mjsull.github.io/Easyfig. Easyfig is a Python application for creating linear comparison figures of multiple genomic loci with an easy-to-use graphical user interface (GUI).

More at http://easyfig.sourceforge.net/

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

QUAST: quality assessment tool for genome assemblies

Jitendra Prajapati — Wed, 06 Apr 2016 18:23:33 -0500

QUAST evaluates genome assemblies. For metagenomes, please see MetaQUAST project.
It can works both with and without a given reference genome.
The tool accepts multiple assemblies, thus is suitable for comparison.

More at http://bioinf.spbau.ru/quast

http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

Finding Patterns in Biological Sequences

Jit — Thu, 22 Dec 2016 10:30:49 -0600

In this report we provide an overview of known techniques for discovery of patterns of biological sequences (DNA and proteins). We also provide biological motivation, and methods of biological verification of such patterns. Finally we list publicly available tools and databases for pattern discovery. On-line supplement is available through http://genetics.uwaterloo.ca/∼tvinar/cs798g/motif.

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf

SPAdes

Abhimanyu Singh — Tue, 19 Apr 2016 08:37:08 -0500

SPAdes – St. Petersburg genome assembler – is intended for both standard isolates and single-cell MDA bacteria assemblies. This manual will help you to install and run SPAdes. SPAdes version 3.7.1 was released under GPLv2 on March 8, 2016 and can be downloaded from http://bioinf.spbau.ru/en/spades.

Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html

Address of the bookmark: http://bioinf.spbau.ru/spades