Address of the bookmark: http://www.phrap.org/consed/consed.html

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.

Here is a list of what is changing:

1. All databases at the root level will be dbV5.
2. The dbV5 file naming,  “_v5” will be removed. Databases with  no “_vX” descriptor will be dbV5.
3. dbV4 tarballs will be renamed with "_v4", files included in tarball will not be renamed.
4. dbV4 databases will be moved to a v4 subdirectory.
5. As of 1/13/20 the Cloud directory will be frozen with no more new entries.
6. The will be no more updates to dbV4 databases.
7. The FASTA directory will contain nr, nt, swissprot, and pdbaa files.

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

The changes in nr preserve the taxonomic diversity of the entries in the database while reducing the number of titles for identical sequences. GenPept accessions are still accessible via www.ncbi.nlm.nih.gov/protein/$GENBANK_ACCESSION or the IPG website https://www.ncbi.nlm.nih.gov/ipg/.

The "Identical Proteins" link in the alignments section of the webBLAST results takes you to a full list of all accessions associated with a sequence.

For BLAST+ users downloading nr_v5: the database is now approximately 50% smaller, resulting in faster downloads and BLAST searches, and smaller disk space requirements. The database is downloadable at:  ftp://ftp.ncbi.nlm.nih.gov/blast/db/v5/

For BLAST+ there is a cleanup script to help you manage the transition to this smaller database. The script removes unused database volumes: ftp://ftp.ncbi.nlm.nih.gov/blast/temp/cleanup-blastdb-volumes.py

Here are the new rules on how we keep titles in nr_v5:

1.    We keep all refseq, swissprot, pir and PDB titles.

2.    We keep any GenPept titles with a TAXID that has not already been seen in the record.

3.    We keep at least five GenPept titles regardless of whether the TAXIDS have been seen before or not in this record.

Address of the bookmark: http://www.ub.edu/dnasp/

https://ftp.ncbi.nlm.nih.gov/refseq/release/viral/

Address of the bookmark: https://ftp.ncbi.nlm.nih.gov/refseq/release/viral/

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

where, c is the count of a k-mer from reads, m is the mode of counts of read k-mers, and n is the copy of the k-mer in the assembly.

Address of the bookmark: https://github.com/liu3zhenlab/KAD