<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36476?offset=200 https://bioinformaticsonline.com/news/view/44342/ncbi-datasets%E2%80%AFpages Wed, 12 Jul 2023 06:29:31 -0500 https://bioinformaticsonline.com/news/view/44342/ncbi-datasets%E2%80%AFpages <![CDATA[NCBI Datasets pages]]> Update! Assembly and Genome record pages now redirect to new NCBI Datasets pages. NCBI Datasets is a new resource that makes it easier to find and download genome data. Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2023/07/11/ncbi-datasets-genome-assembly-pages/ #NCBICGR

Effective July 10, 2023, NCBI’s Assembly and Genome record pages now redirect to new NCBI Datasets pages. As previously announced, these updates are part of our ongoing effort to modernize and improve your user experience. NCBI Datasets is a new resource that makes it easier to find and download genome data.   

The following pages have been updated:
  • The NCBI Assembly record pages now redirect to the new NCBI Datasets Genome record pages that describe assembled genomes and provide links to related NCBI tools such as Genome Data Viewer and BLAST.  
  • The NCBI Genome record pages now redirect to the NCBI Datasets Taxonomy record pages that provide a taxonomy-focused portal to genes, genomes, and additional NCBI resources.   

During this transition, you will have the option to return to the legacy Genome and Assembly record pages. We will remove the legacy pages in early 2024.  

]]> BioStar https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly Fri, 13 Dec 2024 11:35:55 -0600 https://bioinformaticsonline.com/blog/view/44722/step-by-step-guide-to-running-genome-assembly <![CDATA[Step-by-Step Guide to Running Genome Assembly]]> Genome assembly is a critical process in bioinformatics, enabling the reconstruction of an organism's genome from short DNA sequence reads. Whether you’re working on a new microbial genome or a complex eukaryotic organism, this guide will walk you through the steps of genome assembly using state-of-the-art tools and best practices.

What is Genome Assembly?

Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:

  • De Novo Assembly: Without a reference genome.
  • Reference-Guided Assembly: Using a reference genome to guide the assembly process.

Step 1: Preparing Your Data

Before starting the assembly, ensure that your raw sequencing data is high quality.

  1. Input Data

    • Short Reads: Illumina sequencing generates short, accurate reads ideal for scaffolding.
    • Long Reads: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.

  2. Quality Control (QC)
    Use tools like FastQC or MultiQC to assess the quality of your reads:

    fastqc reads.fastq multiqc .

    Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.

  3. Read Trimming and Filtering
    Trim low-quality bases and adapters using Trimmomatic or Cutadapt:

    trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36

Step 2: Choosing an Assembly Strategy

Select an assembly strategy based on your data type:

  • Short-Read Assemblers:

    • SPAdes: Popular for microbial genomes.
    • Velvet: Fast for smaller genomes.

  • Long-Read Assemblers:

    • Canu: Ideal for long-read datasets.
    • Flye: Versatile for small and large genomes.

  • Hybrid Assemblers:

    • MaSuRCA: Combines short and long reads.
    • Unicycler: Optimized for bacterial genomes.

Step 3: Running the Assembly

3.1. SPAdes (Short-Read Assembly)

SPAdes is an excellent choice for small genomes, such as bacteria.

spades.py -1 trimmed_R1.fastq -2 trimmed_R2.fastq -o spades_output

The output includes assembled contigs (contigs.fasta) and scaffolds (scaffolds.fasta).

3.2. Canu (Long-Read Assembly)

Canu is designed for high-error long reads from PacBio or Nanopore.

canu -p genome -d canu_output genomeSize=4.7m -nanopore-raw reads.fastq

The output will be in canu_output/genome.contigs.fasta.

3.3. Hybrid Assembly with Unicycler

Unicycler combines short and long reads for improved assemblies.

unicycler -1 trimmed_R1.fastq -2 trimmed_R2.fastq -l long_reads.fastq -o unicycler_output

Step 4: Assessing Assembly Quality

After assembly, evaluate its quality using the following tools:

  1. QUAST
    QUAST generates assembly statistics, such as N50, genome size, and GC content:

    quast contigs.fasta -o quast_output

  2. BUSCO
    BUSCO checks genome completeness by identifying conserved genes:

    busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome

  3. Assembly Graph Visualization
    Visualize assembly graphs with Bandage:

    Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

  1. Polishing
    Improve assembly accuracy using tools like Pilon (for short reads) or Racon (for long reads).

    racon long_reads.fasta mapped_reads.sam contigs.fasta > polished_contigs.fasta

  2. Scaffolding
    Link contigs into scaffolds using tools like SSPACE or Opera-LG if required.

  3. Annotation
    Annotate the assembled genome using Prokka for prokaryotes or Maker for eukaryotes.

    prokka --outdir annotation_output --prefix genome contigs.fasta

Step 6: Sharing and Archiving

  1. Submit to Public Repositories
    Share your assembly in databases like NCBI GenBank, ENA, or DDBJ.

  2. Metadata Preparation
    Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.

Best Practices

  • Always perform quality checks at each stage to ensure data integrity.
  • Use multiple tools to cross-validate results when working with complex genomes.
  • Document parameters and software versions for reproducibility.

Conclusion

Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism’s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you’re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.

]]> Abhi https://bioinformaticsonline.com/bookmarks/view/37414/arc-pipeline-which-facilitates-iterative-reference-guided-de-novo-assemblies Thu, 26 Jul 2018 09:20:26 -0500 https://bioinformaticsonline.com/bookmarks/view/37414/arc-pipeline-which-facilitates-iterative-reference-guided-de-novo-assemblies <![CDATA[ARC: pipeline which facilitates iterative, reference guided de novo assemblies]]> ARC is a pipeline which facilitates iterative, reference guided de novo assemblies with the intent of:

  1. Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.
  2. Reducing/removing reference bias as compared to mapping based approaches.

The software is designed to work in situations where a whole-genome assembly is not the objective, but rather when the researcher wishes to assemble discreet 'targets' contained within next-generation shotgun sequence data. ARC decomplexifies the traditionally difficult problem of assembly by breaking the reads into small, manageable subsets which can then be assembled quickly and efficiently in parallel. Applications include those in which the researcher wishes to de novo assemble specific content and a set of semi-similar reference targets is available to initialize the assembly process.

https://ibest.github.io/ARC/

Address of the bookmark: https://ibest.github.io/ARC/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39830/the-extensive-de-novo-te-annotator-edta Thu, 08 Aug 2019 04:05:36 -0500 https://bioinformaticsonline.com/bookmarks/view/39830/the-extensive-de-novo-te-annotator-edta <![CDATA[The Extensive de novo TE Annotator (EDTA)]]> The EDTA package was designed to filter out false discoveries in raw TE candidates and generate a high-quality non-redundant TE library for whole-genome TE annotations. Selection of initial search programs were based on benckmarkings on the annotation performance using a manually curated TE library in the rice genome.

Address of the bookmark: https://github.com/oushujun/EDTA

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization Mon, 18 Dec 2017 04:16:31 -0600 https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization <![CDATA[NanoSim: nanopore sequence read simulator based on statistical characterization.]]> NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

https://github.com/bcgsc/NanoSim

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform Fri, 10 Aug 2018 13:29:28 -0500 https://bioinformaticsonline.com/bookmarks/view/37524/fmlrc-a-long-read-error-correction-tool-using-the-multi-string-burrows-wheeler-transform <![CDATA[FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform]]> FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.

Address of the bookmark: https://github.com/holtjma/fmlrc

]]> 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/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment Thu, 06 Sep 2018 16:27:35 -0500 https://bioinformaticsonline.com/bookmarks/view/37645/lsc-improving-pacbio-long-read-accuracy-by-short-read-alignment <![CDATA[LSC: Improving PacBio Long Read Accuracy by Short Read Alignment]]>
  • Added Command line argument support.
  • Multi-stage execution modes.
  • Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
  • Better compressed intermediate files.
  • Added utilities folder.
  • Added support for multiple short read files.
  • Removed use of configuration file.

    • Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/blog/view/38277/understating-pacbio-reads-name Fri, 23 Nov 2018 07:36:46 -0600 https://bioinformaticsonline.com/blog/view/38277/understating-pacbio-reads-name <![CDATA[Understating pacbio reads name !]]> m140415_143853_42175_c100635972550000001823121909121417_s1_p0/553/3100_11230 0.99 24 └1┘└─────2─────┘└──3─┘└────────────────4────────────────┘└5┘└6┘└7┘└────8────┘└─9─┘└10┘
    1. "m" = movie
    2. Time of Run Start (yymmdd_hhmmss)
    3. Instrument Serial Number
    4. SMRT Cell Barcode
    5. Set Number (a.k.a. "Look Number". Deprecated field, used in earlier version of RS)
    6. Part Number (usually "p0", "X0" when using expired reagents)
    7. ZMW hole number
    8. Subread Region (start_stop using polymerase read coordinates)
    9. readScore
    10. barcodeScore
    ]]>     BioJoker     https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads Thu, 08 Mar 2018 05:05:25 -0600 https://bioinformaticsonline.com/bookmarks/view/35899/reference-free-prediction-of-rearrangement-breakpoint-reads <![CDATA[Reference-free prediction of rearrangement breakpoint reads]]> lideSort-BPR ( b reak p oint r eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

    https://github.com/ewijaya/slidesort-bpr

    Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

    ]]>     Neel