Gene prediction is the process of determining where a coding gene might be in a genomic sequence. Functional proteins must begin with a Start codon (where DNA transcription begins), and end with a Stop codon (where transcription ends).
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Open reading frame is part of reading frame that contains no stop codons or region of amino acids coding triple codons.
ORF starts with start codon and ends at stop codon.
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
Owing to the fragmented nature of EST reads, it is worthwhile to attempt to organize the reads into assemblies that provide a consensus view of the sampled transcripts.
Such fragmented, EST data or gene sequence data, placed in correct context, and indexed by gene such that all expressed data concerning a single gene is in a single index class, and each index class contains the information for only one gene is an EST cluster.
Nuclear magnetic resonance spectroscopy, most commonly known as NMR spectroscopy or magnetic resonance spectroscopy (MRS), is a spectroscopic technique to observe local magnetic fields around atomic nuclei.
Genomics is the study of an organism's entire genome, which is the complete set of genetic material present in its DNA. This includes all the genes, non-coding regions, and regulatory sequences. Genomics involves sequencing and analyzing the DNA to identify genes, variations (such as single nucleotide polymorphisms or SNPs), and other structural features of the genome.
How Genomics & Data analysis are intertwined each other (1).pdfNusrat Gulbarga
Genomics and data analysis are closely linked because genomics generates vast amounts of data, which requires sophisticated computational and analytical tools to process and interpret. Genomics involves sequencing, assembling, and annotating the genome, which produces large datasets that require bioinformatics and computational analysis. Data analysis techniques such as machine learning, statistical analysis, and data visualization are critical for interpreting genomic data, identifying patterns, and making meaningful conclusions. In turn, genomic data analysis helps to advance our understanding of genetics, biology, and disease, leading to new discoveries and advances in medicine, agriculture, and other fields. Without data analysis, genomic research would be limited in its ability to extract insights from the vast amounts of genomic data that are generated. Genomics and data analysis are intertwined because genomics generates vast amounts of data that require advanced computational and statistical methods to interpret and analyze. Genomics is the study of an organism's entire genetic makeup, including DNA sequences, gene expression patterns, and epigenetic modifications. With the advent of high-throughput sequencing technologies, genomics has generated an enormous amount of data that requires sophisticated computational tools to analyze and interpret.
Data analysis plays a crucial role in genomics because it helps to identify genetic variations and their functional significance, understand gene expression patterns, and predict the effects of genetic modifications. Sophisticated statistical methods and machine learning algorithms are used to analyze genomic data and identify patterns, associations, and correlations. Data analysis also plays a critical role in personalized medicine, where genomic data is used to identify individualized treatments for patients based on their genetic makeup. Overall, genomics and data analysis are intertwined because they complement each other and are both essential for understanding the complexities of the genetic code and its effects on health and disease. Genomics and data analysis are intertwined because genomics is the study of the entire genetic material of an organism, and data analysis is necessary to interpret and make sense of the vast amount of genomic data generated. Genomics involves sequencing, assembling, and analyzing DNA, RNA, and protein sequences. The resulting data are massive, complex, and require advanced computational tools and techniques to be analyzed effectively. Data analysis helps to identify genes, regulatory elements, and mutations that are responsible for specific traits or diseases. It also helps to compare genomic sequences across different species and populations. Without data analysis, it would be impossible to extract useful information from the vast amount of genomic data produced by sequencing technologies.
Newtons law of motion ~ II sem ~ m sc bioinformaticsNusrat Gulbarga
In the first law, an object will not change its motion unless a force acts on it. In the second law, the force on an object is equal to its mass times its acceleration. In the third law, when two objects interact, they apply forces to each other of equal magnitude and opposite direction.
Cheminformatics (sometimes referred to as chemical informatics or chemoinformatics) focuses on storing, indexing, searching, retrieving, and applying information about chemical compounds. ... Virtual libraries can contain information on likely synthesis methods and predicted stability of the reaction products.
Genomes, omics and its importance, general features III semesterNusrat Gulbarga
'Omic' technologies are primarily aimed at the universal detection of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample. ... Mass spectrometry is the most common method used for the detection of analytes in proteomic and metabolomic research.
Architecture of prokaryotic and eukaryotic cells and tissuesNusrat Gulbarga
The cells of all prokaryotes and eukaryotes possess two basic features: a plasma membrane, also called a cell membrane, and cytoplasm. However, the cells of prokaryotes are simpler than those of eukaryotes. For example, prokaryotic cells lack a nucleus, while eukaryotic cells have a nucleus
Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions. The proteome is the entire set of proteins that is produced or modified by an organism or system. Proteomics has enabled the identification of ever increasing numbers of protein.
Cheese is a dairy product, derived from milk and produced in wide ranges of flavors, textures and forms by coagulation of the milk protein casein. It comprises proteins and fat from milk, usually the milk of cows, buffalo, goats, or sheep.
Generation in computer terminology is a change in technology a computer is/was being used.
Initially, the generation term was used to distinguish between varying hardware technologies.
Nowadays, generation includes both hardware and software, which together make up an entire
computer system
Cell biology is the study of cell structure and function, and it revolves around the concept that the cell is the fundamental unit of life. Focusing on the cell permits a detailed understanding of the tissues and organisms that cells compose.
In biology, a mutation is an alteration in the nucleotide sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA.
In biology, cell signaling or cell-cell communication, governs the basic activities of cells and coordinates multiple-cell actions. A signal is an entity that codes or conveys information. Biological processes are complex molecular interactions that involve a lot of signals.
Necrosis is the death of body tissue. It occurs when too little blood flows to the tissue. This can be from injury, radiation, or chemicals. Necrosis cannot be reversed. When large areas of tissue die due to a lack of blood supply, the condition is called gangrene
Thermodynamics is the branch of physics that deals with the relationships between heat and other forms of energy. In particular, it describes how thermal energy is converted to and from other forms of energy and how it affects matter.
Translation is the process of translating the sequence of a messenger RNA (mRNA) molecule to a sequence of amino acids during protein synthesis. The genetic code describes the relationship between the sequence of base pairs in a gene and the corresponding amino acid sequence that it encodes.
Database administration refers to the whole set of activities performed by a database administrator to ensure that a database is always available as needed. Other closely related tasks and roles are database security, database monitoring and troubleshooting, and planning for future growth
These organs synthesize and secrete specific biochemical messengers, known as hormones, into the blood in a synchronized collaboration with the central nervous system (CNS) and the immune system to regulate metabolism, growth, development, and reproduction (Figure 15-1).
Apoptosis is an orderly process in which the cell's contents are packaged into small packets of membrane for “garbage collection” by immune cells. Apoptosis removes cells during development, eliminates potentially cancerous and virus-infected cells, and maintains balance in the body.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
3. Prokaryotes
• Advantages
– Simple gene structure
– Small genomes (0.5 to 10 million bp)
– No introns
– Genes are called Open Reading Frames (ORFs)
– High coding density (>90%)
• Disadvantages
– Some genes overlap (nested)
– Some genes are quite short (<60 bp)
5. Simple rule-based gene finding
• Look for putative start codon (ATG)
• Staying in same frame, scan in groups of three
until a stop codon is found
• If # of codons >=50, assume it’s a gene
• If # of codons <50, go back to last start codon,
increment by 1 & start again
• At end of chromosome, repeat process for reverse
complement
7. Content based gene prediction method
• RNA polymerase promoter site (-10, -30
site or TATA box)
• Shine-Dalgarno sequence (+10, Ribosome
Binding Site) to initiate protein translation
• Codon biases
• High GC content
8. Similarity-based gene finding
• Take all known genes from a related genome and
compare them to the query genome via BLAST
• Disadvantages:
– Orthologs/paralogs sometimes lose function and
become pseudogenes
– Not all genes will always be known in the comparison
genome (big circularity problem)
– The best species for comparison isn’t always obvious
• Summary: Similarity comparisons are good
supporting evidence for prediction validity
10. Ab-initio Methods
• Fast Fourier Transform based methods
• Poor performance
• Able to identify new genes
• FTG method
• http://www.imtech.res.in/raghava/ftg/
12. Eukaryotes
• Complex gene structure
• Large genomes (0.1 to 3 billion bases)
• Exons and Introns (interrupted)
• Low coding density (<30%)
– 3% in humans, 25% in Fugu, 60% in yeast
• Alternate splicing (40-60% of all genes)
• Considerable number of pseudogenes
13. Finding Eukaryotic Genes
Computationally
• Rule-based
– Not as applicable – too many false positives
• Content-based Methods
– CpG islands, GC content, hexamer repeats, composition statistics,
codon frequencies
• Feature-based Methods
– donor sites, acceptor sites, promoter sites, start/stop codons,
polyA signals, feature lengths
• Similarity-based Methods
– sequence homology, EST searches
• Pattern-based
– HMMs, Artificial Neural Networks
• Most effective is a combination of all the above
14. Gene prediction programs
• Rule-based programs
– Use explicit set of rules to make decisions.
– Example: GeneFinder
• Neural Network-based programs
– Use data set to build rules.
– Examples: Grail, GrailEXP
• Hidden Markov Model-based programs
– Use probabilities of states and transitions between
these states to predict features.
– Examples: Genscan, GenomeScan
16. Egpred: Prediction of Eukaryotic Genes
• Similarity Search
– First BLASTX against RefSeq datbase
– Second BLASTX against sequences from first BLAST
– Detection of significant exons from BLASTX output
– BLASTN against Introns to filter exons
• Prediction using ab-initio programs
– NNSPLICE used to compute splice sites
• Combined method
http://www.imtech.res.in/raghava/ (Genome Research 14:1756-66)