<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/8504?offset=1310 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/bookmarks/view/43846/the-complete-sequence-of-a-human-genome Thu, 31 Mar 2022 23:58:18 -0500 https://bioinformaticsonline.com/bookmarks/view/43846/the-complete-sequence-of-a-human-genome <![CDATA[The complete sequence of a human genome]]> The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.

Address of the bookmark: https://www.science.org/doi/10.1126/science.abj6987

]]> Neel https://bioinformaticsonline.com/opportunity/view/19580/internship-program-for-bioinformatics-biotechnology-mba-mca-no-of-vacancy-5 Mon, 15 Dec 2014 08:11:02 -0600 <![CDATA[Internship Program for Bioinformatics / Biotechnology / MBA / MCA (No. Of Vacancy: 5)]]> ArrayGen is offering an Internship Program for Post graduate Bioinformatics / Biotechnology / MBA / MCA students and professionals. ArrayGen Technologies provide an excellent opportunity to gain research experience and explore if a scientific career is right for you. Currently we offer positions to outstanding students interested in Next Generation Sequencing (NGS) data analysis or marketing or software development. Applications are accepted throughout the year. Accepted students will be notified through email.

]]> https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation Tue, 25 Apr 2023 20:58:52 -0500 https://bioinformaticsonline.com/bookmarks/view/44311/jbrowse-2-a-modular-genome-browser-with-views-of-synteny-and-structural-variation <![CDATA[JBrowse 2: a modular genome browser with views of synteny and structural variation]]>
  • igvjs - a create-react-app with igv package from npm installed. the igv.js is instrumented to output "DONE" to the console when finished, and to have an increased fetchSizeLimit (which is otherwise git in CRAM longread tests)
  • jb2-web - stock instance of jbrowse-web v1.7.5
  • jb1 - stock instance of jbrowse 1 v1.16.11
  • jb2 embedded - a create-react-app with @jbrowse/react-linear-genome-view

    • Address of the bookmark: https://github.com/GMOD/jb2profile

    ]]>     Abhi     https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes Fri, 15 Mar 2024 04:59:14 -0500 https://bioinformaticsonline.com/bookmarks/view/44483/baclife-an-automated-genome-mining-tool-for-identification-of-lifestyle-associated-genes <![CDATA[bacLIFE: an automated genome mining tool for identification of lifestyle associated genes]]> bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated lifestyle-associated genes (LAGs), and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:

    • Clustering module Predicts, clusters and annotates the genes of every input genome
    • Lifestyle prediction Employs a machine learning model to forecast bacterial lifestyle or other specified metadata
    • Analitical module (Shiny app) Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.

    You can find the complete wiki here [https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki]

    Address of the bookmark: https://github.com/Carrion-lab/bacLIFE

    ]]>     BioStar     https://bioinformaticsonline.com/file/view/88/regular-expression-cheat-sheet Tue, 09 Jul 2013 17:38:42 -0500 https://bioinformaticsonline.com/file/view/88/regular-expression-cheat-sheet <![CDATA[Regular Expression Cheat Sheet]]> The Regular Expression are the sole of Perl language, and for bioinformatician it is just a magical stick to resolve gingatic string data. We did not find any good and user friendly regular expression cheat sheet, hence write our own cheat sheet. The Regular Expressions Cheat Sheet, a quick reference guide for regular expressions, including symbols, ranges, grouping, assertions and some sample patterns to get you started.

    ]]>     Jitendra Narayan     https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome Sat, 24 Aug 2013 18:49:03 -0500 https://bioinformaticsonline.com/videolist/watch/3917/the-story-of-you-encode-and-the-human-genome <![CDATA[The Story of You: ENCODE and the human genome]]>

    Ever since a monk called Mendel started breeding pea plants we've been learning about our genomes. In 1953, Watson, Crick and Franklin described the structure of the molecule that makes up our genomes: the DNA double helix. Then, in 2001, scientists wrote down the entire 3-billion letter code contained in the average human genome. Now they're trying to interpret that code; to work out how it's used to make different types of cells and different people. The ENCODE project, as it's called, is the latest chapter in the story of you. To read the ENCODE research papers and more, visit http://www.nature.com/ENCODE

    ]]>         https://bioinformaticsonline.com/videolist/watch/4960/genome-epigenome-new-understanding-of-the-pathogens-in-your-food Fri, 27 Sep 2013 11:30:45 -0500 https://bioinformaticsonline.com/videolist/watch/4960/genome-epigenome-new-understanding-of-the-pathogens-in-your-food <![CDATA[Genome + Epigenome = New Understanding of the Pathogens in Your Food]]> UC Davis's Bart Weimer describes foodborne pathogens and their proclivity for rapid genome rearrangement. The 100K Pathogen Genome Project he leads is using PacBio long-read sequencing to close genomes and analyze methylation; Weimer reports that his team has already discovered new epigenetic modifications in Salmonella and Listeria with the technology. www.pacb.com/microbe]]> https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid Sun, 26 Oct 2014 07:45:58 -0500 https://bioinformaticsonline.com/file/view/18653/genetic-code-amino-acid <![CDATA[Genetic code - Amino Acid]]> The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

    The image summarise all in one.

    More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

    ]]>     Poonam Mahapatra