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Step-by-Step Guide to Running Genome Assembly

Abhi — Fri, 13 Dec 2024 11:35:55 -0600

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.

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.
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.
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:

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

quast contigs.fasta -o quast_output
BUSCO
BUSCO checks genome completeness by identifying conserved genes:

busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome
Assembly Graph Visualization
Visualize assembly graphs with Bandage:

Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

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

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

JBrowse: Embeddable genome browser built completely with JavaScript and HTML5

Jit — Fri, 29 Jun 2018 09:19:56 -0500

JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl. Headline Features: Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed. Scales easily to multi-gigabase genomes and deep-coverage sequencing. Quickly open and view data files on your computer without uploading them to any server. Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more. BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed. Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks. Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance. Can run as a stand-alone app on OSX and Windows using the Electron platform Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry https://jbrowse.org/

Address of the bookmark: https://github.com/GMOD/jbrowse

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Mon, 28 Feb 2022 23:27:37 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)


Number of extant species	200
Number of extant genes	4303993
Number of ancestral species	196
Number of ancestral genes	4624213
Number of ancestral synteny blocks	83342

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

Eugene V. Koonin Lab

Tue, 09 Jan 2018 05:01:15 -0600

Interested in understanding the evolution of life. To obtain glimpses of such understanding, we employ existing and new methods of computational biology to perform research in several major areas.

https://www.ncbi.nlm.nih.gov/research/groups/koonin/

MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads

Rahul Nayak — Fri, 11 May 2018 05:07:45 -0500

MECAT is an ultra-fast Mapping, Error Correction and de novo Assembly Tools for single molecula sequencing (SMRT) reads. MECAT employs novel alignment and error correction algorithms that are much more efficient than the state of art of aligners and error correction tools. MECAT can be used for effectively de novo assemblying large genomes. For example, on a 32-thread computer with 2.0 GHz CPU , MECAT takes 9.5 days to assemble a human genome based on 54x SMRT data, which is 40 times faster than the current PBcR-Mhap pipeline. MECAT performance were compared with PBcR-Mhap pipeline, FALCON and Canu(v1.3) in five real datasets. The quality of assembled contigs produced by MECAT is the same or better than that of the PBcR-Mhap pipeline and FALCON.

https://www.nature.com/articles/nmeth.4432

Address of the bookmark: https://github.com/xiaochuanle/MECAT

ChopStitch: exon annotation and splice graph construction using transcriptome assembly and whole genome sequencing data

Rahul Nayak — Tue, 03 Jul 2018 04:14:52 -0500

ChopStitch is a new method for finding putative exons and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data. ChopStitch identifies exon-exon boundaries in de novo assembled RNA-seq data with the help of a Bloom filter that represents the k-mer spectrum of WGSS reads. The algorithm also detects base substitutions in transcript sequences corresponding to sequencing or assembly errors, haplotype variations, or putative RNA editing events. The primary output of our tool is a FASTA file containing putative exons. Further, exon edges are interrogated for alternative exon-exon boundaries to detect transcript isoforms, which are reported as splice graphs in dot output format.

Address of the bookmark: https://github.com/bcgsc/ChopStitch

LR_Gapcloser: a tiling path-based gap closer that uses long reads to complete genome assembly

Rahul Nayak — Thu, 14 May 2020 15:09:52 -0500

LR_Gapcloser is a gap closing tool using long reads from studied species. The long reads could be downloaed from public read archive database (for instance, NCBI SRA database ) or be your own data. Then they are fragmented and aligned to scaffolds using BWA mem algorithm in BWA package. In the package, we provided a compiled bwa, so the user needn't to install bwa. LR_Gapcloser uses the alignments to find the bridging that cross the gap, and then fills the long read original sequence into the genomic gaps.

Address of the bookmark: https://github.com/CAFS-bioinformatics/LR_Gapcloser

Genobuntu: A software package containing more than 70 software and packages oriented towards NGS and genome assembly

BioStar — Tue, 11 Dec 2018 05:15:57 -0600

Genobuntu is a software package containing more than 70 software and packages oriented towards NGS. In its current version, Genobuntu supports pre assembly tools, genome assemblers as well as post assembly tools.

Commonly used biological software and example script files for different assembly pipelines have also been provided, where the example script files can be updated to suit one’s experimental needs. Genobuntu attempts to reduce the amount of time and energy needed to build software workstations and it can also act as a good teaching source for a class room setting.

https://sourceforge.net/projects/genobuntu/

Address of the bookmark: https://sourceforge.net/projects/genobuntu/

Genome assembly tutorial "Genome Assembly for short and long reads"

Jit — Sat, 19 Jan 2019 17:29:53 -0600

In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:

How to perform basic quality checks on the input data
How to run a short read assembler on Illumina data
How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
How to improve the accuracy of a long read assembly using short reads
How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab

Genome assembly forensics: finding the elusive mis-assembly

Jit — Sat, 26 Jan 2019 18:02:01 -0600

We present the first collection of tools aimed at automated genome assembly validation. This work formalizes several mechanisms for detecting mis-assemblies, and describes their implementation in our automated validation pipeline, called amosvalidate. We demonstrate the application of our pipeline in both bacterial and eukaryotic genome assemblies, and highlight several assembly errors in both draft and finished genomes. The software described is compatible with common assembly formats and is released, open-source, at http://amos.sourceforge.net.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2397507/

http://amos.sourceforge.net/wiki/index.php/AMOS

Address of the bookmark: http://amos.sourceforge.net/wiki/index.php/AMOS