BOL: Related items

List of tools frequently used while genome assembly

BioStar — Tue, 22 Jan 2019 09:39:02 -0600

List of tools frequently used while genome assembly:

I have used the following assemblers

Spades (v. 3.10.1)
CANU (v. 1.6)
Unicycler (v. v0.4.1)
Miniasm (v. 0.2-r137-dirty)

I have used the following mappers

minimap2 (v. 2.0rc1-r232)
minimap (v. 0.2-r124-dirty)
bwa (v. 0.7.12-r1039)

I have used the following polishing tools

Racon (v. not available)
Pilon (v. 1.18)
Nanopolish (v. 0.8.3)

I have used the following tools to assess genome assembly characteristics

ANI.pl (https://github.com/chjp/ANI)
CheckM (v. 1.0.7)
Prokka (v. 1.12)
QUAST (v. 2.3)
mummer (v. not available)

If you have any ideas or superior tools we have missed please let us know in the comments.

SDA: Long-read sequence and assembly of segmental duplications

Jit — Tue, 05 Mar 2019 10:00:57 -0600

Segmental Duplication Assembler (SDA; https://github.com/mvollger/SDA) constructs graphs in which paralogous sequence variants define the nodes and long-read sequences provide attraction and repulsion edges, enabling the partition and assembly of long reads corresponding to distinct paralogs.

https://github.com/mvollger/SDA

Address of the bookmark: https://www.nature.com/articles/s41592-018-0236-3

CONTIGuator !

BioStar — Fri, 04 Oct 2019 01:27:58 -0500

CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).

Address of the bookmark: https://sourceforge.net/projects/contiguator/

HASLR: a tool for rapid genome assembly of long sequencing reads

LEGE — Fri, 31 Jan 2020 05:50:15 -0600

HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.

Address of the bookmark: https://github.com/vpc-ccg/haslr

Biological databases !

BioStar — Wed, 12 Feb 2020 01:16:29 -0600

Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ...

ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

Coronavirus Resources !

Neel — Wed, 25 Mar 2020 17:11:33 -0500

2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

Supernova: generates phased, whole-genome de novo assemblies from a Chromium-prepared library.

Jit — Sun, 31 May 2020 01:59:30 -0500

Supernova generates phased, whole-genome de novo assemblies from a Chromium-prepared library.

Please see Achieving Success with De Novo Assembly and System Requirements before creating your Chromium libraries for assembly.

Supernova should be run using 38-56x coverage of the genome.
• Somewhat higher coverage is sometimes advantageous.
• Supernova will exit if it finds that coverage is far from the recommended range.
• Note that at most 2.14 billion reads are allowed.
• Please note that we have not extensively tested genomes larger than human, and any genome above approximately 4 GB should be considered experimental and is not supported.

Address of the bookmark: https://support.10xgenomics.com/de-novo-assembly/software/pipelines/latest/using/running

GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.

Jit — Fri, 05 Feb 2021 21:22:24 -0600

GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.

https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf

Address of the bookmark: https://github.com/nadegeguiglielmoni/GraphUnzip

MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization

Neel — Tue, 30 Nov 2021 23:23:57 -0600

MitoZ, consisting of independent modules of de novo assembly, findMitoScaf (find Mitochondrial Scaffolds), annotation and visualization, that can generate mitogenome assembly together with annotation and visualization results from HTS raw reads.

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

odgi: optimized dynamic genome/graph implementation

Abhimanyu Singh — Tue, 01 Feb 2022 23:42:21 -0600

odgi provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi