<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34569?offset=200 https://bioinformaticsonline.com/bookmarks/view/41493/coronavirus-resources Wed, 25 Mar 2020 17:11:33 -0500 https://bioinformaticsonline.com/bookmarks/view/41493/coronavirus-resources <![CDATA[Coronavirus Resources !]]> 2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse 

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data 

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

]]> Neel https://bioinformaticsonline.com/bookmarks/view/41937/merqury-evaluate-genome-assemblies-with-k-mers Fri, 03 Jul 2020 19:29:34 -0500 https://bioinformaticsonline.com/bookmarks/view/41937/merqury-evaluate-genome-assemblies-with-k-mers <![CDATA[merqury: Evaluate genome assemblies with k-mers]]> Often, genome assembly projects have illumina whole genome sequencing reads available for the assembled individual. The k-mer spectrum of this read set can be used for independently evaluating assembly quality without the need of a high quality reference. Merqury provides a set of tools for this purpose.

More at https://www.biorxiv.org/content/10.1101/2020.03.15.992941v1.full

Address of the bookmark: https://github.com/marbl/merqury

]]> Jit https://bioinformaticsonline.com/researchlabs/view/42900/svardal-lab Sat, 20 Feb 2021 10:01:19 -0600 <![CDATA[Svardal lab]]> In the Svardal lab they are interested how the astonishing natural diversity we see on earth came into being, by which forces it formed and how it is changing today. Hence, they are trying to understand the process of evolution, with mathematical models and through the analysis of genome sequencing data.

Genomes, and in particular differences between them, are a crucial source of information to understand evolution and biology in general. They provide a record of the evolutionary past of populations, their relatedness patterns, their demography, and their adaptations.

More at https://www.uantwerpen.be/en/staff/hannes-svardal/svardal-lab/

]]> https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding Sat, 08 May 2021 21:25:00 -0500 https://bioinformaticsonline.com/bookmarks/view/43057/hapsolo-an-optimization-approach-for-removing-secondary-haplotigs-during-diploid-genome-assembly-and-scaffolding <![CDATA[HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding]]> HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. 

Address of the bookmark: https://github.com/esolares/HapSolo

]]> Jit https://bioinformaticsonline.com/researchlabs/view/43293/josefa-gonzalez-lab Thu, 19 Aug 2021 08:52:56 -0500 <![CDATA[Josefa González Lab]]> Lab focus on understanding how organisms adapt to their environments. They combine omics approaches with detailed molecular and phenotypic analyses to get a comprehensive picture of adaptation. Our aim at being internationally recognized as a leading lab in the field of environmental adaptation.
Lab share our passion for science with the general public by leading outreach projects aimed at increasing science awareness.

More at https://www.biologiaevolutiva.org/gonzalez_lab/

]]> https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps Fri, 10 Dec 2021 06:22:54 -0600 https://bioinformaticsonline.com/blog/view/43634/illumina-based-assembly-pipeline-steps <![CDATA[Illumina based assembly pipeline steps !]]> Illumina
  1. Merge re-sequenced FastQ files (cat)
  2. Read QC (FastQC)
  3. Adapter trimming (fastp)
  4. Removal of host reads (Kraken 2; optional)
  5. Variant calling
    1. Read alignment (Bowtie 2)
    2. Sort and index alignments (SAMtools)
    3. Primer sequence removal (iVar; amplicon data only)
    4. Duplicate read marking (picard; optional)
    5. Alignment-level QC (picard, SAMtools)
    6. Genome-wide and amplicon coverage QC plots (mosdepth)
    7. Choice of multiple variant calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
      • Lineage analysis (Pangolin)
      • Clade assignment, mutation calling and sequence quality checks (Nextclade)
      • Individual variant screenshots with annotation tracks (ASCIIGenome)
    8. Intersect variants across callers (BCFTools)
  6. De novo assembly
    1. Primer trimming (Cutadapt; amplicon data only)
    2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
  7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)
]]> Surabhi Chaudhary 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/researchlabs/view/43817/bioinfo-lab Fri, 04 Mar 2022 00:17:00 -0600 <![CDATA[Bioinfo Lab]]> The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.

Contact:
Computer Science Building (Science Park 3)
Altenberger Str. 69, A-4040 Linz, Austria
Tel. +43 732 2468 4520 / Fax +43 732 2468 4539
E-mail secretary@bioinf.jku.at

http://www.bioinf.jku.at/

]]> https://bioinformaticsonline.com/bookmarks/view/44307/genomenotebook Thu, 20 Apr 2023 13:19:01 -0500 https://bioinformaticsonline.com/bookmarks/view/44307/genomenotebook <![CDATA[genomenotebook]]> https://dbikard.github.io/genomenotebook/

Install

pip install genomenotebook

How to use

Create a simple genome browser with a search bar. The sequence appears when zooming in.

import genomenotebook as gn

g=gn.GenomeBrowser(genome_path, gff_path, init_pos=10000)
g.show()

Tracks can be added to visualize your favorite genomics data. See Examples for more !!!!

Address of the bookmark: https://dbikard.github.io/genomenotebook/

]]> Abhi 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