IPA for DNA analysis

Nov 27, 20120 likes483 views

The document discusses interval position analysis (IPA), a method for analyzing DNA and RNA sequences. IPA calculates characteristics such as V, G, and g values based on the distances between similar elements in a sequence. The values of IPA characteristics are sensitive to the order of elements in a sequence. IPA can be used to construct phylogenetic trees and analyze local profiles of RNA sequences. Heap's law and rank distribution models are also discussed in relation to evaluating DNA segmentation.

Interval position analysis (IPA)
Common order

g=1.0308 ACCTTCATCCCCAACAAC

CACCACCATTACCACCAT
g=1.0462

g=1.0586 CACCACCATTACCACCTA

H=1.4591
IPA characteristics' values sensitive to the order of
elements in contrast to the characteristics of
Information theory (Claude Shannon)

Interval position analysis (IPA)
Special order

CT AGCT AGCT AGCT AGCT AGCT AGCT AGCT AG
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

H=2
g≈2
L→∞
g→H

If the special order of items in the sequence the values
of the characteristics of the IPA are approximately equal
to values of simular characteristics in Information theory
(Claude Shannon)

Ribosomal RNA
Shpynov S. N. (Proffessor, PHD) http://goo.gl/0XWsj
№ Organism g № Organism g
1 M.musculus 1,4174 16 Rattus norvegicus 1,4703
2 C.crocodylus 1,4186 17 Crocodylus niloticus 1,4784
3 C.familiaris 1,4280 18 I.persulcatus 1,4830
4 G.gallus 1,4284 19 Zebrias zebra 1,4907
5 Sus scrofa 1,4405 20 Kareius bicoloratus 1,4936
6 Amia calva 1,4409 21 O.moubata 1,4958
7 Homo sapiens 1,4429 22 P.humanus cap 1,5031
8 Th.thermophilus 1,4483 23 M.domestica 1,5110
9 Thermotoga thermarum 1,4483 24 S.pyogenes 1,5164
10 Gallus gallus 1,4557 25 B.anthracis 1,5221
11 Bos taurus 1,4607 26 B.burgdorferi 1,5222
12 Erinaceus europaeus 1,4636 Candidatus Nitrosopumilus
1,5239
13 Homo sapiens 1,4638 27 maritimus
14 Mus musculus 1,4656 28 M.pneumoniae 1,5329
15 Cricetulus griseus 1,4682 29 Neisseria gonorrhoeae 1,5353

Ribosomal RNA
Shpynov S. N. (Proffessor, PHD) http://goo.gl/0XWsj

g

Vertebrata Invertebrata Protozoa

Draft RNA analysis based on IPA (g) chart.

Phylogenetic trees made with ClustalW and method based on IPA (g)

IPA based
method
ClustalW

Evaluation of DNA segmentation based on the law
Heaps

Gnomic: A dictionary of genetic codes Pareto distribution http://goo.gl/PWu8B
Trifonov E, Brendel, V http://goo.gl/dZxJo Zipf’s Law http://goo.gl/Sjeum
Bradford’s law http://goo.gl/bdNjG
Heaps law http://goo.gl/u6H2F
CACCACCATTACCACCAT

Rank distribution

ln(nr) ln(Gr)

2 1
I II

3

ln(r ln(r
) )

Interval position analysis (IPA)
IPA characteristic based on distance between simular elements

N M M N V M V V N M N V
A L L A B L B B A L A B
F G G F H G H H F G F H
1 2 2 1 3 2 3 3 1 2 1 3
1 - - 1 - - - - 1 - 1 -
∆11 ∆12 ∆13 ∆14

Interval position analysis (IPA)
IPA characteristics

nj
m
V j =∏ Δ ij V =∏ V j
i=1
j=1
nj m nj
G j =∑ log 2 Δ ij G=∑ ∑ log 2 Δ ij
i =1 j =1 i=1
nj m
1 nj
g j = ∑ log Δ ij g =∑ log Δ gj
n j i=1 j=1 n

r= Δ g / D

DNA microarrays contain small spots of DNA, with each spot representing a different gene. The intensity of each spot indicates the expression level of that gene's mRNA. Microarrays allow clustering of genes and experiments based on similar expression profiles, linking related pathway components. Post-translational modifications of histone tails, such as acetylation, provide binding sites that regulate chromatin structure and transcription.

Adrenoleucodistrofía, seminario biología molecular David Herrera Correa

This document discusses adrenoleukodystrophy (ALD), which is caused by mutations in the ABCD1 gene. The ABCD1 gene contains 10 exons and encodes a peroxisomal membrane protein involved in fatty acid metabolism. The study examines a patient with ALD caused by a splicing mutation, c.1780+2T>G, in the ABCD1 gene. Experiments such as PCR, RT-PCR, and bioinformatics analysis predict this mutation causes abnormal splicing. The results suggest the mutation leads to exon skipping or intron retention, preventing proper transcription of the ABCD1 gene and explaining the patient's lack of the ALD protein.

Genomic DisordersMihaiela Fazacas

This document discusses genomic disorders caused by DNA rearrangements such as deletions, duplications, inversions, and insertions ranging from kilobases to megabases. These rearrangements are mediated by mechanisms involving non-allelic homologous recombination between low-copy repeats, also called segmental duplications. Several genomic disorders are described that result from rearrangements between specific low-copy repeats, including deletions causing Wolf-Hirschhorn syndrome, Williams-Beuren syndrome, and Prader-Willi syndrome. Diagnostic methods for detecting DNA rearrangements are also summarized, such as fluorescence in situ hybridization, pulsed-field gel electrophoresis, array comparative genomic hybridization, and multiplex ligation-

Collegepart B.Burgering Deel 1BMW, Utrecht University

The document discusses various techniques for studying signal transduction and transcription control, including examples of signaling pathways like RTK/Ras/MAPK and Wnt pathways. It describes methods to measure transcriptional activity like reporter assays, and techniques to determine if regulation occurs through transcription like RT-PCR, protein expression analysis, and chromatin immunoprecipitation followed by PCR or microarray analysis.

Dr. Marçal Gallemí. Unitat de Genètica Molecular Vegetal / About plant respon...xrbiotech

Plants respond to proximity of other vegetation through shade avoidance syndrome (SAS). When plants detect low red (R) to far-red (FR) light ratios, which indicates nearby vegetation, they activate SAS even before being shaded. This single low R:FR signal regulates SAS throughout a plant's lifecycle, inducing responses like hypocotyl elongation and flowering time changes. The transcription factor ATHB4 is a direct target of phytochrome photoreceptors that perceive R:FR light. Studies on ATHB4 deletion fragments show that its N-terminal domain is required for SAS responses to low R:FR, suggesting it acts through DNA-binding to regulate gene expression and mediate shade avoidance.

Bioinformática y supercomputación. Razones para hacerse bioinformático en la UMAM. Gonzalo Claros

Tissue Culture and Cloning WorkSatrupa Das

ชีวะ Bio Supanapimpim

1. DNA contains the genetic instructions used in the development and functioning of all living organisms. It is made up of four chemical bases (A, T, C, G) that form base pairs between strands. 2. DNA replicates through a semi-conservative process where the double helix unwinds and each strand acts as a template for new partner strands. This ensures genetic information is preserved as cells divide. 3. Genes encoded in DNA are expressed via transcription of DNA to mRNA and translation of mRNA to proteins. Transfer RNA (tRNA) molecules match mRNA codons to amino acids during protein synthesis.

Dragon's DNAuniversity of education,Lahore

The document discusses DNA sequencing software. It describes a fast and accurate DNA sequencing assembly software for Windows that can assemble DNA sequences into contigs and directly compare trace data to nucleotide data. It handles over 100,000 samples from various sequence and file formats and accelerates proofreading and comparing nucleotides to trace peaks. Several other related DNA sequencing software are also mentioned such as DNA DYNAMO, DNA MASTER, and Mesto DNA program starter.

Initial steps towards a production platform for DNA sequence analysis on the ...Barbera van Schaik

DNA analysis on your laptop: Spot the differencesBarbera van Schaik

Dna baserAyesha Iram

The document discusses two bioinformatics software tools: DNA Baser and Darwin. DNA Baser is a tool for manual and automatic DNA sequence assembly, analysis, editing, and more. It allows for automation of sequence assembly through functions like end trimming, vector removal, and batch assembly of thousands of sequences. Darwin is an interpreted computer language for research in bioscience that provides libraries and functions for tasks like sequence comparison, alignment, phylogenetics, and more.

137920Mohammad Adil

Genome assembly: then and now (with notes) — v1.2Keith Bradnam

DNA of building software products - Fast track methodProductNation/iSPIRT

Chenoweth os bridge 2015 ppdreamwidth

Kishor PresentationKishor Tappita

Genome and Proteome data integration in RDFNadia Anwar

The document summarizes integrating genome and proteome data from Francisella tularensis in RDF. It discusses integrating data from multiple sources, including genome annotations, proteomics experiments, and transcriptomics data. Semantic data integration across "omes" data silos is demonstrated using RDF and the open source Sesame framework. Reifying biological statements, such as identified peptides and abundances, allows more complex queries across the integrated data.

Profile A.I.Macan Markar & Co.Arjuna Dangalla

Biology for Computer Engineers:Part 1(www.ubio.in)ubio Biotechnology Systems Pvt Ltd

Application of Marker Assisted Selection (MAS) for the improvement of Bean Co...CIAT

The document summarizes efforts to develop common bean varieties in Rwanda resistant to Bean Common Mosaic Necrotic Virus (BCMNV) using Marker Assisted Selection (MAS). Researchers screened 219 bean varieties and identified genes conferring resistance. They developed 86 breeding lines by crossing donor lines containing resistance genes with local varieties. These lines were selected using linked markers and for resistance to BCMNV and other diseases. Participatory plant breeding involved farmers in selection. The integration of conventional breeding and MAS was successful in pyramiding resistance genes and developing lines adapted to Rwanda.

Biology DNA AnalysiseLearningJa

- DNA molecules are very long and consist of millions of base pairs. To study their structure, restriction enzymes are used to cut the DNA molecules into smaller, easier to analyze fragments at specific recognition sites. - The fragments produced can be separated by gel electrophoresis based on their size, with shorter fragments traveling farther through the gel. This produces a pattern called a genetic fingerprint that can be used for applications like genetic profiling in criminal cases. - The human genome project aimed to map all human genes by determining the full DNA sequence. While about 3% of human DNA codes for proteins, other non-coding "junk DNA" may have undiscovered functions and contains regions of repeated sequences that vary between individuals.

Recent biotechnology innovationsMuhammed sadiq

The document summarizes several recent biotechnology innovations, including using oil-eating bacteria to clean up oil spills, using a protein called GDF 11 to improve aging brains and muscles in mice, developing advanced biofuels from cellulosic biomass, using 3D x-ray filming to study insect movements, discovering anti-psychotic drugs that kill brain cancer, developing affordable genome sequencing technology, engineering immune cells to attack cancer, creating RNA detection probes without harming cells, and assessing monoclonal antibody therapies using ADCC reporter assays.

Biotechnological toools & their applicationsRishikesh Mishra

Back to Basics: Fundamental Concepts and Special Considerations in gDNA Isola...QIAGEN

This document provides an overview of genomic DNA (gDNA) isolation. It discusses key considerations for gDNA isolation including sample stabilization, disruption, and storage. Common isolation technologies like silica membrane and magnetic bead kits are described. The document reviews measuring gDNA concentration and quality via UV spectroscopy and gel electrophoresis. It also provides guidance on selecting appropriate QIAGEN gDNA isolation kits based on sample type.

Analysis and Interpretation of Cell-free DNAQIAGEN

Statistical approaches for the interpretation of DNA evidencehindahaned

This document discusses different statistical approaches for interpreting DNA evidence, including allele-centric and genotype-centric approaches. It describes probability of exclusion/inclusion, random match probability, and likelihood ratios. Likelihood ratios are considered the preferred approach as they make use of more information, provide a better estimation of evidence strength, and can model uncertainties in DNA sample composition. International consensus now recommends probabilistic and likelihood ratio approaches over classical methods for DNA evidence interpretation.

Evaluating allelic drop-out probabilities using a Monte-Carlo simulation appr...hindahaned

IPA for DNA analysis

1. Interval position analysis (IPA) Common order g=1.0308 ACCTTCATCCCCAACAAC CACCACCATTACCACCAT g=1.0462 g=1.0586 CACCACCATTACCACCTA H=1.4591 IPA characteristics' values sensitive to the order of elements in contrast to the characteristics of Information theory (Claude Shannon)

2. Interval position analysis (IPA) Special order CT AGCT AGCT AGCT AGCT AGCT AGCT AGCT AG 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 H=2 g≈2 L→∞ g→H If the special order of items in the sequence the values of the characteristics of the IPA are approximately equal to values of simular characteristics in Information theory (Claude Shannon)

3. Ribosomal RNA Shpynov S. N. (Proffessor, PHD) http://goo.gl/0XWsj № Organism g № Organism g 1 M.musculus 1,4174 16 Rattus norvegicus 1,4703 2 C.crocodylus 1,4186 17 Crocodylus niloticus 1,4784 3 C.familiaris 1,4280 18 I.persulcatus 1,4830 4 G.gallus 1,4284 19 Zebrias zebra 1,4907 5 Sus scrofa 1,4405 20 Kareius bicoloratus 1,4936 6 Amia calva 1,4409 21 O.moubata 1,4958 7 Homo sapiens 1,4429 22 P.humanus cap 1,5031 8 Th.thermophilus 1,4483 23 M.domestica 1,5110 9 Thermotoga thermarum 1,4483 24 S.pyogenes 1,5164 10 Gallus gallus 1,4557 25 B.anthracis 1,5221 11 Bos taurus 1,4607 26 B.burgdorferi 1,5222 12 Erinaceus europaeus 1,4636 Candidatus Nitrosopumilus 1,5239 13 Homo sapiens 1,4638 27 maritimus 14 Mus musculus 1,4656 28 M.pneumoniae 1,5329 15 Cricetulus griseus 1,4682 29 Neisseria gonorrhoeae 1,5353

4. Ribosomal RNA Shpynov S. N. (Proffessor, PHD) http://goo.gl/0XWsj g Vertebrata Invertebrata Protozoa Draft RNA analysis based on IPA (g) chart.

5. Phylogenetic trees made with ClustalW and method based on IPA (g) IPA based method ClustalW

6. Local g profile Local g profiles for 3 different organisms (Ribosomal RNA) RNA length ≈ 1800 CACCACCATTACCACCAT window size = 100 step = 2 nucleotide

7. Evaluation of DNA segmentation based on the law Heaps Gnomic: A dictionary of genetic codes Pareto distribution http://goo.gl/PWu8B Trifonov E, Brendel, V http://goo.gl/dZxJo Zipf’s Law http://goo.gl/Sjeum Bradford’s law http://goo.gl/bdNjG Heaps law http://goo.gl/u6H2F CACCACCATTACCACCAT

8. Rank distribution ln(nr) ln(Gr) 2 1 I II 3 ln(r ln(r ) )

9. Interval position analysis (IPA) IPA characteristic based on distance between simular elements N M M N V M V V N M N V A L L A B L B B A L A B F G G F H G H H F G F H 1 2 2 1 3 2 3 3 1 2 1 3 1 - - 1 - - - - 1 - 1 - ∆11 ∆12 ∆13 ∆14

10. Interval position analysis (IPA) IPA characteristics nj m V j =∏ Δ ij V =∏ V j i=1 j=1 nj m nj G j =∑ log 2 Δ ij G=∑ ∑ log 2 Δ ij i =1 j =1 i=1 nj m 1 nj g j = ∑ log Δ ij g =∑ log Δ gj n j i=1 j=1 n r= Δ g / D

IPA for DNA analysis

Recommended

More Related Content

Viewers also liked (20)

IPA for DNA analysis