<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36512?offset=280 https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment Sat, 02 Jun 2018 07:36:05 -0500 https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment <![CDATA[Gblocks: eliminates poorly aligned positions and divergent regions of a DNA or protein alignment]]> Gblocks eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. This server implements the most important features of the Gblocks program to make its use as simple as possible without loosing the functionality that it is necessary in most of the cases. Other options can be changed in the stand-alone program. You can see here an example output file showing the blocks selected from a protein alignment. Further information can be found in the online documentation. 

Address of the bookmark: http://molevol.cmima.csic.es/castresana/Gblocks_server.html

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/37584/mulan-multiple-sequence-local-alignment-and-visualization-for-studying-function-and-evolution Fri, 24 Aug 2018 09:50:01 -0500 https://bioinformaticsonline.com/bookmarks/view/37584/mulan-multiple-sequence-local-alignment-and-visualization-for-studying-function-and-evolution <![CDATA[Mulan: Multiple-sequence local alignment and visualization for studying function and evolution]]> Mulan: Multiple-sequence local alignment and visualization for studying function and evolution

Mulan (http://mulan.dcode.org/), a novel method and a network server for comparing multiple draft and finished-quality sequences to identify functional elements conserved over evolutionary time. Mulan brings together several novel algorithms: the TBA multi-aligner program for rapid identification of local sequence conservation, and the multiTF program for detecting evolutionarily conserved transcription factor binding sites in multiple alignments. In addition, Mulan supports two-way communication with the GALA database; alignments of multiple species dynamically generated in GALA can be viewed in Mulan, and conserved transcription factor binding sites identified with Mulan/multiTF can be integrated and overlaid with extensive genome annotation data using GALA.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC540288/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment Tue, 09 Jul 2019 23:58:44 -0500 https://bioinformaticsonline.com/bookmarks/view/39689/msaprobs-parallel-and-accurate-multiple-sequence-alignment <![CDATA[MSAProbs - Parallel and accurate multiple sequence alignment]]> MSAProbs is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.

Address of the bookmark: http://msaprobs.sourceforge.net/homepage.htm#latest

]]> Neel https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods Tue, 28 Jan 2020 03:53:24 -0600 https://bioinformaticsonline.com/bookmarks/view/40711/vg-variation-graph-data-structures-interchange-formats-alignment-genotyping-and-variant-calling-methods <![CDATA[VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods]]> Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

  • nodes, which are labeled by sequences and ids
  • edges, which connect two nodes via either of their respective ends
  • paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

]]> Jit 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

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