This is the seventh school of a biennial series of international summer schools on mathematical ecology and evolution in Finland, organised by the Biomathematics Group of the University of Helsinki. The series of The Helsinki Summer School on Mathematical Ecology and Evolution is part of the EMS-ESMTB Schools in Applied Mathematics.

After the two-year break forced upon by the pandemic, we are looking forward to continue this series in August 2022, if only the covid situation permits.

The job description: "This research position focuses on the science
of bacterial evolution. It will consist of researching theoretical
principles, but could include translational applications. Phylogenomic
and bioinformatic analysis of bacterial populations in nature or
in laboratory experiments will be a key component of the work. Prior
experience is an asset though training will be possible at PMI.
Likewise, laboratory microbiological, molecular, and biochemical
skills are an asset though not essential. Communication and critical
thinking skills are essential for performing the work and for
communicating to the local and international scientific communities.
Participating in team or independent grant writing to obtain research
funding will be required. Student mentoring is a part of the NAU
mission and is a partial expectation."

https://hr.peoplesoft.nau.edu/psp/ph92prta/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_APP_SCHJOB.GBL?Page=HRS_APP_JBPST&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=608024&PostingSeq=1

Northern Arizona University is located in Flagstaff, Arizona, a
beautiful mountain town with a surprisingly vibrant restaurant
scene. Located a little over an hour from the Grand Canyon and ~45
min from Sedona, Flagstaff is a hiker's paradise. In fact, the city
of Flagstaff operates more than 50 miles of unpaved trails and there
are, on average, 266 sunny days per year with which to enjoy them.
At 7000 ft in elevation, Flagstaff experiences all four seasons,
but thesummers are mild and, in the winter, you can be on the ski
slopes within 30 min! https://www.flagstaffarizona.org/

As mentioned, joint projects with Professor Sheppard at Oxford
University are possible, including travel to his laboratory in the
United Kingdom. https://www.biology.ox.ac.uk/people/samuel-sheppard

Contact Information:
Paul.Keim@nau.edu

Paul S. Keim, Ph.D.
Regents Professor, &
Cowden Endowed Chair of Microbiology
Northern Arizona University
Flagstaff, AZ 86011-4073

Paul S Keim

More @ http://www.ncbs.res.in/sowdhamini/groups_sowdhamini.htm

http://www.sidowlab.org/

Applications are invited to an all-expenses paid position at a 5-day
"IdeasLab"* workshop to be held near Prague CZ in June 2022. Thirty
successful applicants will be drawn in equal number from the relevant
sectors biological evolution, A-Life anbd theoretical physics. The week's
activities will lead these thirty to form interdisciplinary teams which
each propose how they can advance frontiers of abiogenesis research. Up to
$5 million total funding will be available for developing these ideas
produced by the week's activities. Further details of the event,
including the online application form, are found at the link posted above

In this post, we’ll explore the core methods used in comparative genomics, the questions they help answer, and how they’re shaping our understanding of life.

1. Whole-Genome Alignment
Whole-genome alignment involves mapping the entire genome of one species to another. Tools like MUMmer, MAUVE, and LASTZ perform large-scale sequence alignments to detect conserved regions, rearrangements, insertions, and deletions.

Use Case:
Comparing human and chimpanzee genomes to identify evolutionary conserved sequences (ECS) and regions of divergence.

Key Challenges:
Handling repetitive sequences and genome rearrangements.

Computational complexity in large genomes.

2. Synteny and Collinearity Analysis
Synteny refers to conserved blocks of gene order across species. Tools like MCScanX, SynMap, or CHITRA (for visualizing synteny interactively) detect these blocks to understand chromosomal evolution.

Use Case:
Studying ancient genome duplications in plants.

Investigating chromosomal rearrangements in cancer genomes.

3. Ortholog and Paralog Detection
Orthologs are genes in different species that evolved from a common ancestor, while paralogs are genes duplicated within a genome. Identifying them is crucial for functional annotation and evolutionary studies.

Popular Tools:
OrthoFinder, Orthologous MAtrix (OMA), InParanoid, and EggNOG.

Use Case:
Functional prediction of uncharacterized genes based on orthologs in model organisms.

Tracing gene family evolution.

4. Phylogenomic Analysis
Phylogenomic methods combine phylogenetics and genomics to infer evolutionary trees based on genome-wide data. These methods can handle dozens to hundreds of genomes, using concatenated alignments or gene trees.

Tools:
RAxML, IQ-TREE, ASTRAL, Phylip, BEAST.

Use Case:
Resolving the evolutionary relationships between microbial species.

Studying speciation events.

5. Pan-Genome Analysis
The pan-genome consists of the core genome (shared by all strains) and the accessory genome (strain-specific genes). This is especially popular in microbial genomics.

Tools:
Roary, Panaroo, BPGA, PGAP.

Use Case:
Understanding virulence factor diversity in E. coli.

Designing broad-spectrum vaccines.

6. Comparative Transcriptomics
Comparing transcriptomes across species or conditions reveals conserved and unique expression patterns. RNA-seq data can be mapped to reference genomes to identify orthologous expression profiles.

Use Case:
Comparing stress response in extremophiles and model species.

Studying conserved regulatory networks.

7. Functional Element Comparison
Beyond genes, comparative genomics also targets non-coding regions—enhancers, promoters, miRNAs. Conservation across species often implies functional importance.

Tools:
PhastCons, GERP, phyloP (based on multiple alignments).

Use Case:
Detecting conserved non-coding elements in vertebrates.

Studying regulatory divergence in human evolution.

8. Horizontal Gene Transfer (HGT) Detection
In microbes, genes often jump across species boundaries. Comparative genomics can detect HGT by identifying genes that defy the expected phylogenetic pattern.

Tools:
HGTector, DarkHorse, AlienHunter, SIGI-HMM.

Use Case:
Tracing antibiotic resistance genes.

Exploring microbial adaptability in extreme environments.

Final Thoughts
Comparative genomics is a powerful lens to observe the diversity and unity of life. With a broad toolkit—from aligners to orthology pipelines, phylogenetic engines to visualization tools—it allows scientists to ask big questions: How did genomes evolve? What makes species unique? Where do new genes come from?

Whether you're studying extremophiles, building better crops, or exploring human ancestry, comparative genomics offers the methods to connect the dots across the tree of life.

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/