<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36632?offset=70 https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes Thu, 13 Aug 2020 10:06:19 -0500 https://bioinformaticsonline.com/bookmarks/view/42038/pyparanoid-a-pipeline-for-rapid-identification-of-homologous-gene-families-in-a-set-of-genomes <![CDATA[PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes]]> PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/43062/jcvi-utility-libraries Sat, 08 May 2021 22:04:02 -0500 https://bioinformaticsonline.com/bookmarks/view/43062/jcvi-utility-libraries <![CDATA[JCVI utility libraries]]> Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

Address of the bookmark: https://github.com/tanghaibao/jcvi

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data Wed, 06 Sep 2023 00:14:28 -0500 https://bioinformaticsonline.com/bookmarks/view/44375/phyloherb-a-high%E2%80%90throughput-phylogenomic-pipeline-for-processing-genome-skimming-data <![CDATA[PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data]]> Phylogenomic Analysis Pipeline for Herbarium Specimens

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

  • Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/44597/imagine-in-silico-metagenomics-pipeline Sat, 06 Jul 2024 04:32:18 -0500 https://bioinformaticsonline.com/bookmarks/view/44597/imagine-in-silico-metagenomics-pipeline <![CDATA[iMAGine - in silico MetAGenomics pipeline]]> iMAGine is a metagenomic workflow which includes filtering, assembling, and binning.

This workflow includes the following tools which are needed to be installed in the system.

  1. fastp
  2. spades assembler
  3. QUAST
  4. bwa
  5. samtools
  6. metabat2
  7. CheckM

Address of the bookmark: https://github.com/avishekdutta14/iMAGine

]]> BioStar https://bioinformaticsonline.com/news/view/34711/1mb-long-dna-with-nanopore-technology Tue, 19 Dec 2017 18:49:28 -0600 https://bioinformaticsonline.com/news/view/34711/1mb-long-dna-with-nanopore-technology <![CDATA[1mb long DNA with Nanopore technology]]> The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics Thu, 29 Apr 2021 23:39:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics <![CDATA[CoverM: Read coverage calculator for metagenomics]]> CoverM aims to be a configurable, easy to use and fast DNA read coverage and relative abundance calculator focused on metagenomics applications.

CoverM calculates coverage of genomes/MAGs coverm genome (help) or individual contigs coverm contig (help). Calculating coverage by read mapping, its input can either be BAM files sorted by reference, or raw reads and reference genomes in various formats.

Address of the bookmark: https://github.com/wwood/CoverM

]]> Neel https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus Fri, 05 Jan 2018 04:12:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus <![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]> proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment Mon, 25 Mar 2019 12:46:47 -0500 https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment <![CDATA[ChIPulate: A Python3 framework to simulate read counts in a ChIP-seq experiment]]> ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength.

Address of the bookmark: https://github.com/vishakad/chipulate

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification Sat, 03 Jun 2023 20:15:04 -0500 https://bioinformaticsonline.com/bookmarks/view/44329/metabuli-%EB%B6%84%EB%A6%AC-improves-metagenomic-read-classification <![CDATA[Metabuli 분리 improves metagenomic read classification]]> Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

 

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny Mon, 17 Feb 2020 05:38:46 -0600 https://bioinformaticsonline.com/bookmarks/view/41125/chromonomer-a-tool-set-for-repairing-and-enhancing-assembled-genomes-through-integration-of-genetic-maps-and-conserved-synteny <![CDATA[Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny]]> Chromonomer is a program designed to integrate a genome assembly with a genetic map. Chromonomer tries very hard to identify and remove markers that are out of order in the genetic map, when considered against their local assembly order; and to identify scaffolds that have been incorrectly assembled according to the genetic map, and split those scaffolds.

Address of the bookmark: http://catchenlab.life.illinois.edu/chromonomer/

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