Address of the bookmark: http://ufmg-simba.sourceforge.net/

Protocol Overview / Introduction

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes.

https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/

Address of the bookmark: https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/

Check out more at https://cab.spbu.ru/software/spades/

More at https://ucdavis-bioinformatics-training.github.io/

Address of the bookmark: https://ucdavis-bioinformatics-training.github.io/2020-Genome_Assembly_Workshop/snakemake/snakemake_intro

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.  

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.

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.

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.

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.

For the full list of features, improvements and fixes, see the release notes:https://ncbi.nlm.nih.gov/tools/gbench/releasenotes

New Features

• BLAST Tool: added support for local custom BLAST databases
• Graphical Sequence View: added log scaling option for graph tracks
• Generic Table View: new tutorial added

Bug Fixes and Improvements

• Project Tree View: Genomic Collections/Assemblies now show accessions, not just names
• Tree View: layout updated to better accommodate nodes of different sizes
• Table Import Dialog (MacOS): fixed issue with table visibility
• Fixed bug where different molecules IDs in GenBank could resolve to the same sequence
• Graphical Sequence View: fixed issue where sequence track was not shown for some sequences
• Graphical Sequence View: fixed protein coloration methods
• Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images
• Create Gene Model tool: fixed scenario when gene model tool failed with local sequences
• Search View: ORF Finder – fixed incorrect protein lengths
• Fixed bug with not opening project file (.gbp) on a click
• Fixed issues in GVF import
• Fixed BLAST Search tool against NCBI databases not working
• Fixed tblastn (protein BLAST) not working in standalone mode
• Fixed GTF export failure

As with most programs that run on UNIX, the text output is in some specific format. If the program is popular enough, there will be one or more parsers written for that format – these are just utilities written to help you retrieve whatever information you are interested in from the output.

Let’s conclude this tutorial by converting the BLAST output in out.txt into a spreadsheet format, using a Python script. 

First, we need to get the script. We’ll do that using the ‘git’ program:

git clone https://github.com/ngs-docs/ngs-scripts.git /root/ngs-scripts

We’ll discuss ‘git’ more later; for now, just think of it as a way to get ahold of a particular set of files. In this case, we’ve placed the files in /root/ngs-scripts/, and you’re looking to run the script blast/blast-to-csv.py using Python:

python /root/ngs-scripts/blast/blast-to-csv.py out.txt

This outputs a spread-sheet like list of names and e-values. To save this to a file, do:

python /root/ngs-scripts/blast/blast-to-csv.py out.txt > ~out.csv

If you have Excel installed, try double clicking on it.