BOL: Related items

UCSC SARS-CoV-2 Genome Browser

Jit — Thu, 06 Jan 2022 06:48:40 -0600

The UCSC SARS-CoV-2 Genome Browser (https://genome.ucsc.edu/covid19.html) is an adaptation of our popular genome-browser visualization tool for this virus, containing many annotation tracks and new features, including conservation with similar viruses, immune epitopes, RT–PCR and sequencing primers and CRISPR guides. We invite all investigators to contribute to this resource to accelerate research and development activities globally.

Address of the bookmark: https://www.nature.com/articles/s41588-020-0700-8

MUM&Co is a simple bash script that uses Whole Genome Alignment information provided by MUMmer (v4) to detect variants.

Rahul Nayak — Wed, 27 Apr 2022 04:34:12 -0500

MUM&Co is able to detect:
Deletions, insertions, tandem duplications and tandem contractions (>=50bp & <=150kb)
Inversions (>=1kb) and translocations (>=10kb)

Address of the bookmark: https://github.com/SAMtoBAM/MUMandCo

Steps to find all the repeats in the genome !

Neel — Thu, 31 Aug 2023 02:43:28 -0500

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

Install RepeatMasker: First, you need to install RepeatMasker on your system. You can download it from the RepeatMasker website[0].

Prepare the genome sequence: Make sure you have the genome sequence in a FASTA file format. Let's assume the file is named "genome.fasta".

./RepeatMasker -pa -nolow -norna -no_is -div -lib RepeatMaskerLib.embl -gff -xsmall -small -poly -species -dir -length - genome.fasta

Replace the following placeholders with appropriate values:

: The number of processors/threads you want to use for parallel processing.
: The divergence value for the species you are analyzing. You can find divergence values for different species in the RepeatMasker documentation[0].
: The name of the species you are analyzing.
: The directory where you want the output files to be saved.
and : The minimum and maximum lengths of the repeats you want to find (in this case, 2 and 9).

Analyze the output: RepeatMasker will generate several output files, including a .out file. You can parse this file to extract the information you need. There is a Perl tool called "one_code_to_find_them_all.pl" that can help you parse RepeatMasker output files[0]. You can download it from the source provided.

Use the provided Perl script: Once you have the "one_code_to_find_them_all.pl" script, you can run it to conveniently parse the RepeatMasker output files. Here's an example of how to use it:

perl one_code_to_find_them_all.pl --rm --length

Replace with the path to your RepeatMasker .out file, and with the path to a file containing the lengths of the reference elements.

This script will generate several output files, including .log.txt and .copynumber.csv, which contain quantitative information about the identified repeat elements.

Remember to adjust the parameters and options according to your specific needs and the characteristics of your genome.

UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

BioStar — Mon, 05 Aug 2024 23:01:29 -0500

UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

Using state of the art tools, easily extended for other viruses
Tool and database updates for critical components via Conda
Built using modern design patterns with Conda and Snakemake
Extensible and easy to customize
Submission Ready Genomes
Customizable reporting with comprehensive visualization

https://ikim-essen.github.io/uncovar/

Github https://github.com/IKIM-Essen/uncovar

Address of the bookmark: https://ikim-essen.github.io/uncovar/

Genome Simulation with SLiM and msprime

BioStar — Fri, 31 Jan 2025 12:47:43 -0600

Genome simulation is an essential tool in population genetics, enabling researchers to model evolutionary processes and study genetic variation. Two widely used simulation tools in this field are SLiM and msprime. While both serve different purposes, they can be used together with the slendr framework to compare simulation outputs effectively.

Overview of SLiM and msprime

SLiM: Forward Genetic Simulator

SLiM is a free, open-source tool designed for forward genetic simulations. It allows researchers to model complex evolutionary scenarios, including selection, recombination, and demographic events, making it particularly useful for studying adaptation and selection in populations.

Key Features of SLiM:

Simulates population evolution forward in time
Supports custom evolutionary models using an embedded scripting language
Allows modeling of spatial and ecological dynamics
Provides high flexibility and extensibility for user-defined scenarios
Available on GitHub as an open-source project

msprime: Ancestry and Mutation Simulator

msprime is an efficient, open-source tool that simulates ancestry and mutations using a coalescent framework. It is known for its high-speed performance and low memory requirements, making it a popular choice for large-scale genomic simulations.

Key Features of msprime:

Implements coalescent simulations for ancestry modeling
Efficiently simulates large population histories
Supports the addition of mutations to genealogies
Developed using an open-source community model
Often faster and more memory-efficient than alternative simulators

Using SLiM and msprime with slendr

Both SLiM and msprime can be integrated with slendr, a framework that facilitates structured population genetic simulations. This integration allows for seamless comparison of simulation outputs.

How They Work Together:

SLiM and msprime simulations can be analyzed within slendr.
The ts_read() function in slendr enables loading and comparing tree sequence outputs from both simulators.
This integration allows researchers to validate simulation results and gain deeper insights into evolutionary processes.

Performance Considerations

While SLiM offers powerful forward simulations with extensive customization, msprime is often preferred for its speed and memory efficiency when simulating ancestry and mutations. The choice between the two depends on the research goals:

For detailed evolutionary modeling with selection and recombination: Use SLiM.
For large-scale coalescent simulations with mutations: Use msprime.
For comparing different simulation models and their outputs: Use slendr to integrate SLiM and msprime results.

Conclusion

SLiM and msprime are valuable tools for genome simulation, each serving distinct but complementary purposes in population genetics research. By leveraging the strengths of both simulators with slendr, researchers can conduct robust and efficient evolutionary simulations, enhancing our understanding of genetic diversity and adaptation.

For more information, check out the official GitHub repositories for SLiM and msprime, and explore the slendr framework for streamlined simulation workflow

LRCstats: Long Read Correction Statistics

Jit — Fri, 05 Jan 2018 04:04:20 -0600

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats

LSC: Improving PacBio Long Read Accuracy by Short Read Alignment

Abhimanyu Singh — Thu, 06 Sep 2018 16:27:35 -0500

Added Command line argument support.
Multi-stage execution modes.
Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
Better compressed intermediate files.
Added utilities folder.
Added support for multiple short read files.
Removed use of configuration file.

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

INC-Seq: accurate single molecule reads using nanopore sequencing

Jit — Mon, 27 Nov 2017 10:38:56 -0600

INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system.

Address of the bookmark: https://github.com/CSB5/INC-Seq

TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads

Surabhi Chaudhary — Tue, 15 May 2018 02:53:11 -0500

TAndem REpeat ANalyzer -TAREAN – is a computational pipeline for unsupervised identification of satellite repeats from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.

How to use TAREAN:

Install a local instance of the pipeline using its source code available from bitbucket repository.
Use public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean

GenomeMapper: Simultaneous alignment of short reads against multiple genomes

Jit — Fri, 25 May 2018 09:29:44 -0500

GenomeMapper is a short read mapping tool designed for accurate read alignments. It quickly aligns millions of reads either with ungapped or gapped alignments. It can be used to align against multiple genomes simulanteously or against a single reference. If you are unsure which one is the appropriate GenomeMapper, you might want to use the latter https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768987/

Address of the bookmark: http://1001genomes.org/software/genomemapper.html