Address of the bookmark: https://schneebergerlab.github.io/syri/

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)

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

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi

https://academic.oup.com/bib/article/22/3/bbaa123/5868070

Address of the bookmark: https://github.com/hzi-bifo/Quasimodo

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/

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/books/NBK20260/

https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

Address of the bookmark: https://www.genome.gov/event-calendar/perspectives-in-comparative-genomics-and-evolution

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.

Compare structural and functional annotations of proteins between sequenced genomes.