<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/44375?offset=170 https://bioinformaticsonline.com/bookmarks/view/44675/variant-calling-pipeline Sat, 19 Oct 2024 12:23:40 -0500 https://bioinformaticsonline.com/bookmarks/view/44675/variant-calling-pipeline <![CDATA[Variant Calling Pipeline]]> The variantcalling.nf nextflow script will take any number of samples with paired-end reads in FASTQ format, map reads using Bowtie2, process BAM files, and finally call variants using BCFtools v1.21 and/or Freebayes v1.3.6. If part of the pipeline is unsuccessful for a sample then these errors are ignored.

Pipeline flowchart:

 
 

Dependencies (version tested)

  • Nextflow (24.04.4)
  • Java (18.0.2.1)
  • Python (3.10)
  • Perl (5.32.1)
  • Bowtie2 (2.5.3)
  • SAMtools (1.19.2)
  • GATK4 (4.5)
  • BCFtools (1.21)
  • Freebayes (1.3.6)

Address of the bookmark: https://github.com/Tom-Jenkins/nextflow-pipelines/blob/main/docs/variant-calling.md

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline Mon, 10 Jul 2017 05:56:07 -0500 https://bioinformaticsonline.com/bookmarks/view/33847/omega2-metagenome-assembly-pipeline <![CDATA[Omega2: metagenome assembly pipeline]]> Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.

Address of the bookmark: http://omega.omicsbio.org/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites Mon, 05 Nov 2018 08:12:45 -0600 https://bioinformaticsonline.com/bookmarks/view/38067/metaplotr-a-perlr-pipeline-for-plotting-metagenes-of-nucleotide-modifications-and-other-transcriptomic-sites <![CDATA[MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites]]> An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.

Address of the bookmark: https://github.com/olarerin/metaPlotR

]]> Neel https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes Tue, 20 Aug 2019 09:47:14 -0500 https://bioinformaticsonline.com/bookmarks/view/39856/tritex-sequence-assembly-pipeline-for-triticeae-genomes <![CDATA[TRITEX sequence assembly pipeline for Triticeae genomes]]>

The pipeline is open-source and hosted in a public Bitbucket repository.

TRITEX has been run on highly inbred genotypes of barley (Hordeum vulgare), tetraploid wheat (Triticum turgidum) and hexaploid wheat (T. aestivum) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.

A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in Himmelbach et al. 2018. Refer to the technical notes of 10X Genomics on how to generate Chromium data.

Address of the bookmark: https://tritexassembly.bitbucket.io/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/41030/slr-superscaffolder-a-scaffold-assemble-pipeline-for-stlfr-reads Fri, 14 Feb 2020 14:23:30 -0600 https://bioinformaticsonline.com/bookmarks/view/41030/slr-superscaffolder-a-scaffold-assemble-pipeline-for-stlfr-reads <![CDATA[SLR-superscaffolder: A scaffold assemble pipeline for stLFR reads.]]> This is a scaffold assembler designed for stLFR reads[1]. It uses the link-reads information from stLFR reads to assemble contigs to scaffolds.

Here is an illustration of this pipeline:

 image

Address of the bookmark: https://github.com/BGI-Qingdao/SLR-superscaffolder

]]> Jit 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/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads Sun, 14 Mar 2021 09:42:47 -0500 https://bioinformaticsonline.com/bookmarks/view/42946/aligngraph2-similar-genome-assisted-reassembly-pipeline-for-pacbio-long-reads <![CDATA[AlignGraph2: similar genome-assisted reassembly pipeline for PacBio long reads]]> AlignGraph2 is the second version of AlignGraph for PacBio long reads. It extends and refines contigs assembled from the long reads with a published genome similar to the sequencing genome.

More at https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab022/6146772

Address of the bookmark: https://github.com/huangs001/AlignGraph2

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes Sat, 08 Jun 2024 16:25:08 -0500 https://bioinformaticsonline.com/bookmarks/view/44561/bactopia-a-flexible-pipeline-for-complete-analysis-of-bacterial-genomes <![CDATA[Bactopia: a flexible pipeline for complete analysis of bacterial genomes]]> Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!

Bactopia was inspired by Staphopia, a workflow we (Tim Read and myself) released that is targeted towards Staphylococcus aureus genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.

Bactopia uses Nextflow to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from Bioconda and Conda-Forge to make installation as simple as possible for all users.

To highlight the use of Bactopia and Bactopia Tools, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title: Bactopia: a flexible pipeline for complete analysis of bacterial genomes

Bactopia Workflow

Address of the bookmark: https://bactopia.github.io/latest/

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/39683/gffcompare-program-for-processing-gtfgff-files Tue, 09 Jul 2019 13:35:13 -0500 https://bioinformaticsonline.com/bookmarks/view/39683/gffcompare-program-for-processing-gtfgff-files <![CDATA[GffCompare: Program for processing GTF/GFF files]]> The program gffcompare can be used to compare, merge, annotate and estimate accuracy of one or more GFF files (the “query” files), when compared with a reference annotation (also provided as GFF).

Address of the bookmark: https://ccb.jhu.edu/software/stringtie/gffcompare.shtml

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data Mon, 13 Nov 2017 05:10:23 -0600 https://bioinformaticsonline.com/bookmarks/view/34292/automatic-filtering-trimming-error-removing-and-quality-control-for-fastq-data <![CDATA[Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data]]> Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

Address of the bookmark: https://github.com/OpenGene/AfterQC

]]> Rahul Nayak