Address of the bookmark: https://www.kegg.jp/blastkoala/

What is Bacterial Comparative Genomics?

Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:

• Identify conserved and unique genes.

• Explore genetic determinants of pathogenicity.

• Understand bacterial evolution and phylogenetics.

• Investigate horizontal gene transfer and its role in antibiotic resistance.

Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.

Key Steps in Bacterial Comparative Genomics

1. Genome Sequencing and Assembly: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.

2. Genome Annotation: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.

3. Genome Alignment: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.

4. Comparative Analyses:

   • Core and Pan-genome Analysis: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.

   • Phylogenetic Analysis: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.

   • Functional Enrichment Analysis: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.

Here are some additional bioinformatics tools that can aid bacterial comparative genomics:

• OrthoFinder: For accurate ortholog identification across multiple genomes.

• PanOCT: Specifically designed for pan-genome clustering and annotation.

• FASTANI: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.

• CIRCOS: For visually comparing genomic data through circular genome plots.

• Galaxy Platform: A user-friendly web-based platform offering numerous genomic analysis tools.

• BLAST: Essential for sequence alignment and similarity searches.

• PhyloSift: Focused on phylogenetic analysis of microbial genomes using marker genes.

These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.

Applications of Bacterial Comparative Genomics

1. Understanding Pathogenicity: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of Escherichia coli strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.

2. Antibiotic Resistance Research: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.

3. Microbial Ecology and Evolution: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.

4. Vaccine Development: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like Neisseria meningitidis.

5. Biotechnology Applications: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.

Challenges in Bacterial Comparative Genomics

While the field has made significant strides, several challenges remain:

• Data Overload: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.

• Genome Plasticity: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.

• Annotation Accuracy: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.

• Interpreting Non-Coding Regions: Understanding the functional significance of non-coding genomic regions remains a challenge.

Future Directions

The integration of bacterial comparative genomics with other ‘omics’ approaches—such as transcriptomics, proteomics, and metabolomics—promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.

Conclusion

Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.

It looks simple, but inside its microscopic body is a huge library of DNA. Most of this DNA has been passed down from its parents. But sometimes, bacteria can pick up new pieces of DNA from other bacteria.

These new pieces can give them useful abilities — such as surviving antibiotics, causing disease, or adapting to a new environment.

Scientists call some of these special DNA regions genomic islands.

But there is a problem: How do we find these hidden islands?

That is where GIPSy2 comes in.

Researchers developed GIPSy2, a computer tool that acts like a detective. It scans bacterial DNA and looks for unusual patterns that might show where a genomic island is hiding.

Instead of looking at just one clue, GIPSy2 uses several methods to investigate the DNA. It can also study many bacterial genomes, making it useful when researchers have large amounts of genetic data.

In their study, the researchers tested GIPSy2 on different bacteria. The tool was able to identify genomic regions connected with important features such as antibiotic resistance, disease-causing abilities, metabolism, and relationships with other organisms.

Think of it like searching for hidden islands on a huge map. The DNA is the ocean, and genomic islands are the small pieces of land that may contain valuable information.

GIPSy2 does not give scientists the final answer by itself. Its predictions still need to be carefully checked and, when possible, confirmed through experiments.

But it gives researchers a faster way to explore bacterial genomes and discover clues about how bacteria change and survive.

And that is why this small piece of software could help scientists better understand the invisible world of bacteria — one hidden genomic island at a time.

GIPSy2 is currently available on Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10222587

https://fhs.mcmaster.ca/gupta-lab/index.html

Awards of $5 million to four grantees have been made in fiscal year 2013 under the Genomic Sequencing and Newborn Screening Disorders research program. The program will be funded at $25 million over five years, as funds are made available.

"Hundreds of US babies will be pioneers in genomic medicine through a US$25-million programme to sequence their genomes soon after they are born."

Source:

http://blogs.nature.com/news/2013/09/scientists-to-sequence-hundreds-of-newborns-genomes.html

http://www.genome.gov/27554919

http://en.wikipedia.org/wiki/Sequence_assembly 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0951-y

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

Again, running spades.py will show you the options:

spades.py

This produces:

SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o <output_dir>

Basic options:
-o      <output_dir>    directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12    <filename>      file with interlaced forward and reverse paired-end reads
-1      <filename>      file with forward paired-end reads
-2      <filename>      file with reverse paired-end reads
-s      <filename>      file with unpaired reads
--pe<#>-12      <filename>      file with interlaced reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-1       <filename>      file with forward reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-2       <filename>      file with reverse reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-s       <filename>      file with unpaired reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-<or>    orientation of reads for paired-end library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--s<#>          <filename>      file with unpaired reads for single reads library number <#> (<#> = 1,2,..,9)
--mp<#>-12      <filename>      file with interlaced reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-1       <filename>      file with forward reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-2       <filename>      file with reverse reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-s       <filename>      file with unpaired reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-<or>    orientation of reads for mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--hqmp<#>-12    <filename>      file with interlaced reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-1     <filename>      file with forward reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-2     <filename>      file with reverse reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-s     <filename>      file with unpaired reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-<or>  orientation of reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9; <or> = fr, rf, ff)
--nxmate<#>-1   <filename>      file with forward reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--nxmate<#>-2   <filename>      file with reverse reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--sanger        <filename>      file with Sanger reads
--pacbio        <filename>      file with PacBio reads
--nanopore      <filename>      file with Nanopore reads
--tslr  <filename>      file with TSLR-contigs
--trusted-contigs       <filename>      file with trusted contigs
--untrusted-contigs     <filename>      file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from  <cp>    restart run with updated options and from the specified check-point ('ec', 'as', 'k<int>', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset       <filename>      file with dataset description in YAML format
-t/--threads    <int>           number of threads
                                [default: 16]
-m/--memory     <int>           RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir       <dirname>       directory for temporary files
                                [default: <output_dir>/tmp]
-k              <int,int,...>   comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff    <float>         coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  <33 or 64>      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]

As you can see this is also a “pipeline” of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:

spades.py -t 4 \
          -m 32 \
          -k 31,51,71 \
          --only-assembler \
          -1 miseq.1.fastq -2 miseq.2.fastq \
          --nanopore minion.fastq \
          -o hybrid_assembly

In turn, these parameters mean

• use 4 threads
• max memory is 32Gb
• use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71
• only run the assembler, not the correction algorithm (for speed)
• read 1 and read 2 of the MiSeq data
• the nanopore data
• put the output in folder “hybrid_assembly”

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/cope

Prokka – Standalone command line tool, takes just a few minutes per genome. This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome. Note however that the quality of functional information is not as good as RAST, and you will need several extra steps if you want to do functional profiling and pathway analysis of your genome(s)… which is in-built in RAST.

NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

PGAP: NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume.  NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

BEACON (automated tool for Bacterial GEnome Annotation ComparisON), a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

BlastKOLA: Assigns K numbers to the user's sequence data by BLAST searches, respectively, against a nonredundant set of KEGG GENES. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to this server and can be executed in an interactive mode. BlastKOALA is suitable for annotating fully sequenced genomes.

PAGIT: Provides a toolkit for improving the quality of genome assemblies created via an assembly software. PAGIT compiled four tools: (i) ABACAS which classifies and orientates contigs and estimates the sizes of gaps between them; (ii) IMAGE uses paired-end reads to extend contigs and close gaps within the scaffolds; (iii) ICORN for identifying and correcting small errors in consensus sequences and; (iv) RATT for help annotation. The software was mainly created to analyze parasite genomes of up to about 300 Mb.

MAKER: A portable and easily configurable genome annotation pipeline. MAKER allows smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. It identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER's inputs are minimal and its ouputs can be directly loaded into a Generic Model Organism Database (GMOD). They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER is available for download and can be tested online via the MAKER Web Annotation Service (MWAS).

MyPro is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/