Genetic information is stored in DNA molecules as sequences of nucleotides. DNA exists as paired strands that run in opposite directions and are complementary to each other. The genome contains an organism's complete set of DNA and is organized into chromosomes. Genomes can differ between species through point mutations that change single nucleotides or genome rearrangements that modify multiple nucleotides. Rearrangements include reversals, translocations, fissions, and fusions that change the order of genes within and between chromosomes. The minimum number of edits needed to transform one genome into another, including point mutations and rearrangements, defines their edit distance and can provide insights into evolutionary relationships.
Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes
One of the first plausible models to account for the preceding observations was
formulated by Robin Holliday.
The key features of the Holliday model are the formation of heteroduplex DNA; the
creation of a cross bridge; its migration along the two heteroduplex strands,
termed branch migration; the occurrence of mismatch repair; and the
subsequent resolution, or splicing, of the intermediate structure to yield different
typesof recombinant molecules.
Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes Eukaryotic and Prokaryotic Chromosomes
One of the first plausible models to account for the preceding observations was
formulated by Robin Holliday.
The key features of the Holliday model are the formation of heteroduplex DNA; the
creation of a cross bridge; its migration along the two heteroduplex strands,
termed branch migration; the occurrence of mismatch repair; and the
subsequent resolution, or splicing, of the intermediate structure to yield different
typesof recombinant molecules.
RECOMBINATION MOLECULAR BIOLOGY PPT UPDATED new.pptxSabahat Ali
This ppt is about recombination and where it occurs. Types of recombination and models of recombination along with many factors in prokaryotic and eukaryotic recombination
N-terminal tails of histones are the most accessible regions for modifications. These post-translational modification (PTM) of histones is a crucial step in epigenetic regulation of a gene.
RECOMBINATION MOLECULAR BIOLOGY PPT UPDATED new.pptxSabahat Ali
This ppt is about recombination and where it occurs. Types of recombination and models of recombination along with many factors in prokaryotic and eukaryotic recombination
N-terminal tails of histones are the most accessible regions for modifications. These post-translational modification (PTM) of histones is a crucial step in epigenetic regulation of a gene.
Exploring the Solution Space of Sorting by Reversals: A New ApproachIDES Editor
Analysing genome rearrangements is a problem
from the vast domain of comparative genomics and
computational biology. Several studies have shown that closely
related species have essentially the same set of genes however
their gene orders differ. The differences in the gene order are
the results of various large-scale evolutionary events of which
reversal is the most common rearrangement event. The
problem of finding the shortest sequence of reversals that can
transform one genome into another is called the sorting by
reversals problem. The length of such a sequence is the
reversal distance between the two genomes. In comparative
genomics, sorting by reversals algorithms are often used to
propose evolutionary scenarios of large-scale genomic
mutations between species. Following the first polynomial
time solution of this problem, several improvements has been
published on the subject. In 2008, Braga et al. proposed an
algorithm to perform the enumeration of traces that sort a
signed permutation by reversals. This algorithm has
exponential complexity in both time and space. To efficiently
handle the traces, Baudet and Dias proposed a depth first
approach in 2010. However, one of the limitations of the
proposed algorithm was that it cannot provide the count of
number of solutions in each trace. In this paper we are
presenting an algorithm to list the normal forms of each trace
in depth first manner and provide count of the total number of
solutions in the solution space.
Advance Microbiology slides which discuss about molecular genetics. This slides can also be use for those who are taking Masters of Education Major in Science
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Recombination
Breaking and rejoining of two parental DNA molecules to produce new DNA molecules
Types of recombination
Definition of recombination
Gene Conversion – Characteristics
Holliday model
Holliday junction cleavage
A very general lecture on the Epigenomics Roadmap and its main contributions.
This lecture was composed for the students of "Genomic and Epigenomic Medicine 2015/2016 (15 credits)"
http://www.uu.se/en/admissions/master/selma/Kurser/?kKod=3MG025&lasar=15/16&typ=1
A course of the Master's program in Molecular Medicine at Uppsala University
http://www.uu.se/en/admissions/master/selma/program/?pKod=MBK2M
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
Genome rearrangment
1.
2.
3. Basic Biology: DNA
Genetic information is stored in
deoxyribonucleic acid (DNA)
molecules.
A single DNA molecule is a sequence
of nucleotides
adenine (A)
cytosine (C)
guanine (G)
thymine (T)
nitrogenous
base
pentose
sugar
phosphate
Nucleotide DNA molecule
4. Basic Biology: DNA
Paired DNA strands are in
reverse complementary
orientation.
One in forward, 5’ to 3’ direction
The other in reverse, 3’ to 5’
direction
Both strands are
complementary.
A pairs with a T
G pairs with a C
forward
strand
reverse
strand
5’
3’
3’
5’
Image modified with the permission of the
National Human Genome Research Institute
(NHGRI), artist Darryl Leja.
5. Basic Biology: Genome
• The genome is the
entire hereditary
information of an
organism.
• Genomes are
partitioned into
chromosomes.
• A chromosome can
be linear
(eukaryotes), or
circular
(prokaryotes). Image modified with the permission of the
National Human Genome Research Institute
(NHGRI), artist Darryl Leja.
7. Changes in Genomic Sequences
Genomes of different species (even of
closely related individuals) differ from
one another.
These differences are caused by
point mutations, in which only one
nucleotide is changed, and
genome rearrangements, where multiple
nucleotides are modified.
8. Point Mutations
Insertion …ATGGCG… → …
ATGTGCG…
Deletion …ATGTGCG…→ …
ATGGCG…
Substitution …ATGTGCG… → …
ATGCGCG…
…ATG-GCATGTGCGATGTGCG…
…ATGTGCATG-GCGATGCGCG…
DNA sequence alignment showing matches, mismatches,
and insertions/deletions
10. Reversal:
A reversal is an operation that transforms one
signed permutation into another, reversing the
order or a contiguous protein and flipping the
sign.
Translocation:
It is process of exchange of genetic material
between chromosomes. A balanced translocation
results in no gain or loss of material.
Furthermore, while an unbalanced translocation
may result in trisomy or monosomy of a
particular chromosome segments.
11. Fission:
It is the division of a single entity into two or
more parts and then regeneration of those parts
into separate entities resembling the original.
Fusion:
It is the process in which several unicellular cells
combine to form a multinuclear cell.
13. Signed Reversals
5’ ATGCCTGTACTA 3’
3’ TACGGACATGAT 5’
5’ ATGTACAGGCTA 3’
3’ TACATGTCCGAT 5’
Break
and
Invert
Taken and modified from An Introduction to Bioinformatics Algorithms by Neil Jones and Pavel Pevzner
14. Levenshtein’s Edit Distance
Let A and B be two sequences
(genomes). The minimum number of
edit operations that transforms A into B
defines the edit distance, dedit, between
A and B.
Possible edit operations:
point mutations
genome rearrangements
15. A Word Puzzle
To transform a start word into a target
word, change, add, or delete characters
until the target is reached.
Example: start “spices” target “lice”:
○ spices → slices → slice → lice
○ spices → spice→ slice→ lice
16. Edit Distance Using Point
Mutations
S1=AGCTT, S2=AGCCTG, S3=ACAG
AGCTT AGCTG AGCCTG
⇒ dedit(S1,S2) = 2
AGCTT AGCTG AGCAG ACAG
⇒ dedit(S1,S3) = 2
AGCCTG AGCTG AGCAG ACAG
⇒ dedit(S2,S3) = 2
T→G insert C
T→G T→A delete G
delete C T→A delete G
17. Edit Distance and Evolution
The edit distance is often used to infer evolutionary
relationships.
Parsimony assumption: the minimum number of changes
reflects the true evolutionary distance
Parsimonious phylogeny inferred from edit distances
18. Levenshtein’s Edit Distance
Let A and B be two sequences
(genomes). The minimum number of
edit operations that transforms A into B
defines the edit distance, dedit, between
A and B.
Possible edit operations:
point mutations
genome rearrangements
19. Rearrangements and Anagrams
An anagram is a rearrangement of a
word or phrase into another word or
phrase.
○ eleven plus two → twelve plus one
○ forty five → over fifty
Please visit the Internet Anagram web
server at
http://wordsmith.org/anagram/.
21. Genome Comparison: Human -
Mouse
Humans and mice
have similar genomes,
but their genes are in a
different order.
How many edits
(rearrangements) are
needed to transform
human into mouse?
245 rearrangements
Taken and modified from An Introduction to Bioinformatics Algorithms by Neil Jones and Pavel Pevzner
22. Transforming Mice into Humans
a) Mouse and
human share a
common ancestor
b) They share the
same genes, but in a
different order
c) A series of
rearrangements transforms
one genome into the other
23. Web Tools
GRIMM Web Server
computes signed and unsigned reversal
distances between permutations.
Cinteny
a web server for synteny identification and
the analysis of genome rearrangement
24. DCJ Genome Rearrangements
The DCJ model uses Double-Cut-and-
Join genome rearrangement operations.
DCJ operations break and rejoin one or
two intergenic regions (possibly on
different chromosomes).
25. Genome Representation
In the DCJ model, a genome is
grouped into chromosomes
(linear/circular).
A gene g on the forward strand
is represented by [-g,+g]
A gene g on the reverse strand
is represented by [+g,-g]
Telomeres are represented by
the special symbol ‘o’.
An adjacency (intergenic
region) is encoded by the
unordered pair of neighboring
gene/telomere ends.
Example.
linear c1=(o 1 -2 3 4 o)
circular c2=(5 6 7)
26. Research paper on DCJ rearrangements
http://www.lirmm.fr/~rivals/CoCoGEN/articles/B
erard_RECOMBCG08.pdf
Editor's Notes
Redo this slide. Have a look at http://lib.bioinfo.pl/courses/view/693