The document discusses several topics related to gene duplication and read mapping. It first defines gene duplication as the duplication of a region of DNA containing a gene. It then discusses homologs, orthologs, and paralogs that result from gene duplication and speciation. The document next describes read mapping as aligning short DNA reads to a reference genome. It explains several data structures used for genome indexing in read mapping, including keyword trees, suffix trees, suffix arrays, and the Burrows-Wheeler transform.
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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
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genomics, sorting by reversals algorithms are often used to
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time solution of this problem, several improvements has been
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exponential complexity in both time and space. To efficiently
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RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
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.
This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
Dna data compression algorithms based on redundancyijfcstjournal
Carl Jung said, 'Collective unconscious' i.e. we are all connected to each other in some way or the other via our DNA.In frequent cases there are four bases in a DNA. They are a (Adenine),c (Cytosine),g(Guanine)
and t (Thymine).Each of these bases can be represented by two bits as 2 powers 2 =4 i.e.a–00,c–01,g–11 and t–10 respectively, although this choice is random.Soredundancy within a sequence is more likely to exist.That’s why in this paper wehave explored different types of repeat to compress DNA.These are direct repeats, palindrome or reverse direct repeat , inverted exact repeats or complementary palindrome or exact reverse complement, inverted approximate repeats or approximate complementary palindrome or approximate reverse complement, interspersed or dispersed repeats,
flanking repeats or terminal repeats etc. Better compression gives better network speed and save storage space.
Apollo is a web-based, collaborative genomic annotation editing platform. We need annotation editing tools to modify and refine precise location and structure of the genome elements that predictive algorithms cannot yet resolve automatically.
This presentation is an introduction to how the manual annotation process takes place using Apollo. It is addressed to the members of the American Chestnut & Chinese Chestnut Genomics research community.
Apollo - A webinar for the Phascolarctos cinereus research communityMonica Munoz-Torres
Web Apollo is a web-based, collaborative genomic annotation editing platform. We need annotation editing tools to modify and refine precise location and structure of the genome elements that predictive algorithms cannot yet resolve automatically.
This presentation is an introduction to how the manual annotation process takes place using Web Apollo. It is addressed to the members of the Phascolarctos cinereus research community.
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Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
4. Mutation
DNA Mutation refers to sudden, random changes
in DNA sequences which leads to different
phenotypic expressions.
A T C C G A
A T G C C G A
Insertion
7. Gene Duplication
Gene duplication (or chromosomal duplication or
gene amplification) is a major mechanism
through which new genetic material is generated
during molecular evolution. It can be defined as
any duplication of a region of DNA that contains a
gene.
8. Homolog, Ortholog, Paralog and Speciation
• Homolog - A gene related to a second
gene by descent from a common
ancestral DNA sequence
• Ortholog - Orthologs are genes in
different species that evolved from a
common ancestral gene by speciation*
• Paralog - Paralogs are genes related by
duplication within a genome
• Speciation* - Speciation is the origin of a
new species capable of making a living in
a new way from the species from which
it arose
10. Read Mapping
Mapping refers to the process of
aligning short reads to and finding
the starting position in a reference
sequence (typically Genome).
Short read generally are reads with a
length of 30-350 base pairs.
11. Genome Indexing (Keyword Tree)
▹ Stores a set of keywords in a rooted labeled
tree.
▹ Each edge is labeled with a letter from an
alphabet.
▹ Any two edges coming out of the same
vertex have distinct labels.
▹ Every keyword stored can be spelled on a
path from root to some leaf.
▹ Furthermore, every path from root to leaf
gives a keyword.
Keywords
▹ Apple
▹ Apropos
▹ Banana
▹ Bandana
▹ Orange
12. Genome Indexing (Suffix Tree)
▹ Similar to Keyword Tree
▹ Suffixes of the text are keywords
▹ Edges that form paths are collapsed
▹ Each edge is labeled with a substring of the
text
▹ All internal edges have at least two outgoing
edges.
▹ Leaves are labeled by the index of the
pattern.
Suffix tree of ATCATG
13. Genome Indexing (Suffix Array)
▹ More space efficient than suffix tree
▹ Suffix tree index for human genome is
about 47 GB
▹ Lexicographically sort all the suffixes
▹ Store the starting indices of the suffixes
along with the original string
Generate Suffix Array of
ATCATG
1 ATCATG$
2 TCATG$
3 CATG$
4 ATG$
5 TG$
6 G$
7 $
Sort the suffixes
lexicographically
7 $
1 ATCATG$
4 ATG$
3 CATG$
6 G$
2 TCATG$
5 TG$
14. Genome Indexing (Burrows Wheeler Transform)
▹ Given Sequence – abaaba
▹ Add $ as ending notation –
abaaba$
▹ By Shifting each alphabet to the
right once, generate all the
rotations
▹ Lexicographically Sort all the
rotations
▹ The very last column will be
denoted as BWT (T)
15. Genome Indexing (Burrows Wheeler Transform)
▹ Given Sequence – abaaba
▹ Add $ as ending notation –
abaaba$
▹ Lexicographically sorted all
rotations will generate BWT
Matrix which will be denoted as
BWM (T)
▹ Suffix Array generated from all
the rotations will be called SA (T)
▹ BWM can be derived from any
given BWT (T)
16. Genome Indexing (Burrows Wheeler Transform)
LF (Last to First) Mapping
▹ Generate Burrows Wheeler
Matrix for a given sequence
▹ Assign numbers to distinguish
same characters
▹ Assign the numbers in a
ascending manner for each
character
17. Genome Indexing (Burrows Wheeler Transform)
Find out the row starting with b1 using LF Mapping
1. Start from the row containing $ in the First Column
2. Find out what’s in Last Column of that row (here its a0)
3. Compare it with query (b1)
4. If MATCH, then
- Find b1 in First Column
- Print row number
- Terminate
5. If No MATCH, then
- Find the row with that element in the First column
- Go to Step 2 and Repeat
Start
18. Genome Indexing (Burrows Wheeler Transform)
Find Original Gene using LF Mapping if
BWT (T) is Given
1. Original Gene = abaaba (Not Given)
2. Given BWT (T) = abba$aa
3. Store it as Last Column
4. Draw the First Column by sorting
the elements of Last Column
Lexicographically
5. Assign numbers to distinguish
characters in an ascending manner
6. Start LF Mapping from Starting
Element ($)
7. For each element found in the
LAST column, write it from right to
left
$ a0
a0 b0
a1 b1
a2 a1
a3 $
b0 a2
b1 a3
F L
$abaaba
FINISH
Start
19. Whales and DolphinsTheir ancestors had back legs once, they could walk
Humans have tails
While they are inside the womb! It dissolves
eventually
Birds came from Dinosaurs
And they both descended from Reptiles
Bacterium
All livings beings can be traced back to a
bacterium