<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36512?offset=360 https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite Wed, 03 Jan 2018 05:05:46 -0600 https://bioinformaticsonline.com/bookmarks/view/35041/seal-sequence-alignment-evaluation-suite <![CDATA[Seal: SEquence ALignment evaluation suite]]> Seal is a comprehensive sequencing simulation and alignment tool evaluation suite. This software (implemented in Java) provides several utilities that can be used to evaluate alignment algorithms, including:

  • Reading a pre-existing reference genome from one or more FASTA files.
  • Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).
  • Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.
  • Applying alignment tools to the genome and the reads through a standardized interface.
  • Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.
  • Computing run times and measures of accuracy.

Seal has interfaces to evaluate the following software packages:

  • Bowtie
  • BWA
  • MAQ
  • mrFAST
  • mrsFAST
  • Novoalign
  • SHRiMP
  • SOAPv2

Address of the bookmark: http://compbio.case.edu/seal/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39269/ragoo-fast-reference-guided-scaffolding-of-genome-assembly-contigs Wed, 17 Apr 2019 19:45:22 -0500 https://bioinformaticsonline.com/bookmarks/view/39269/ragoo-fast-reference-guided-scaffolding-of-genome-assembly-contigs <![CDATA[RaGOO: Fast Reference-Guided Scaffolding of Genome Assembly Contigs]]> Alonge M, Soyk S, Ramakrishnan S, Wang X, Goodwin S, Sedlazeck FJ, Lippman ZB, Schatz MC: Fast and accurate reference-guided scaffolding of draft genomes. bioRxiv 2019.

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

  1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
  2. Intact ordering and orienting of contigs.
  3. Chimeric contig correction
  4. GFF lift-over
  5. Structural variant calling with and integrated version of Assemblytics
  6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

]]> BioJoker https://bioinformaticsonline.com/bookmarks/view/42310/dada2-fast-and-accurate-sample-inference-from-amplicon-data-with-single-nucleotide-resolution Tue, 10 Nov 2020 20:26:00 -0600 https://bioinformaticsonline.com/bookmarks/view/42310/dada2-fast-and-accurate-sample-inference-from-amplicon-data-with-single-nucleotide-resolution <![CDATA[DADA2: Fast and accurate sample inference from amplicon data with single-nucleotide resolution]]> The DADA2 tutorial goes through a typical workflow for paired end Illumina Miseq data: raw amplicon sequencing data is processed into the table of exact amplicon sequence variants (ASVs) present in each sample.

The DADA2 Workflow on Big Data goes through workflow optimized to run on large datasets (10s of millions to billions of reads).

An ITS-specific version of the DADA2 workflow identifies and verifiably removes primers on both ends of each ITS read, a key step due to the variable length of the ITS region.

Short demonstrations of assigning taxonomy and assigning species to sequences.

Address of the bookmark: https://benjjneb.github.io/dada2/index.html

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34620/mash-fast-genome-and-metagenome-distance-estimation-using-minhash Tue, 12 Dec 2017 17:30:12 -0600 https://bioinformaticsonline.com/bookmarks/view/34620/mash-fast-genome-and-metagenome-distance-estimation-using-minhash <![CDATA[Mash: fast genome and metagenome distance estimation using MinHash]]> Mash is normally distributed as a dependency-free binary for Linux or OSX (see https://github.com/marbl/Mash/releases). This source distribution is intended for other operating systems or for development. Mash requires c++11 to build, which is available in and GCC >= 4.8 and OSX >= 10.7.

See http://mash.readthedocs.org for more information.

Address of the bookmark: https://github.com/marbl/Mash/releases

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool Thu, 02 Aug 2018 07:39:46 -0500 https://bioinformaticsonline.com/bookmarks/view/37473/lsc-a-long-read-error-correction-tool <![CDATA[LSC :a long read error correction tool]]> Getting Started

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

  • Read the LSC_requirements for running LSC.
  • Download and set-up the LSC package.
  • Follow the tutorial to see how LSC works on some example data.
  • Read the manual if anything is unclear.
  • You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong 
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript] 
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

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

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons Thu, 03 Feb 2022 04:01:55 -0600 https://bioinformaticsonline.com/bookmarks/view/43770/chromeister-an-ultra-fast-heuristic-approach-to-detect-conserved-signals-in-extremely-large-pairwise-genome-comparisons <![CDATA[chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.]]> chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.

USAGE:

  • -query: sequence A in fasta format
  • -db: sequence B in fasta format
  • -out: output matrix
  • -kmer Integer: k>1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria
  • -diffuse Integer: z>0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1
  • -dimension Size of the output matrix and plot. Integer: d>0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended

Address of the bookmark: https://github.com/estebanpw/chromeister

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies Sat, 20 Sep 2025 09:34:04 -0500 https://bioinformaticsonline.com/bookmarks/view/44902/hite-a-fast-and-accurate-dynamic-boundary-adjustment-approach-for-full-length-transposable-elements-detection-and-annotation-in-genome-assemblies <![CDATA[HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies]]> HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

panHiTE

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

]]> LEGE https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes Mon, 25 Jun 2018 17:22:24 -0500 https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes <![CDATA[chromoMap-An R package for Interactive visualization and mapping of human chromosomes]]> chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare Wed, 19 Jan 2022 10:30:14 -0600 https://bioinformaticsonline.com/bookmarks/view/43711/vcf-compare <![CDATA[VCF Compare !]]> compare two BWA mapping methods with the online hg18-mapped data

We first operate a rapid inspection of the different BAM files using samtools flagstat. Illumina provided chr21 read mapping obtained with their GA IIx deep sequencing platform <ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam>, aligned to the b36/hg18 reference genome)

Address of the bookmark: https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies Sat, 01 Jun 2019 03:11:16 -0500 https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies <![CDATA[FUMA GWAS: Functional Mapping and Annotation of Genome-Wide Association Studies]]> FUMA is a platform that can be used to annotate, prioritize, visualize and interpret GWAS results. 
The SNP2GENE function takes GWAS summary statistics as an input, and provides extensive functional annotation for all SNPs in genomic areas identified by lead SNPs. 
The GENE2FUNC function takes a list of gene IDs (as identified by SNP2GENE or as provided manually) and annotates genes in biological context 

Address of the bookmark: https://fuma.ctglab.nl/

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