I spoke on "Big Data in Biology". The talk basically concentrates on how biology has affected big data and how big data has become a key player in biology. I have also covered how DNA storage can address long term archival storage.
Transcriptomics is the study of RNA, single-stranded nucleic acid, which was not separated from the DNA world until the central dogma was formulated by Francis Crick in 1958, i.e., the idea that genetic information is transcribed from DNA to RNA and then translated from RNA into protein.
Systems biology is the computational and mathematical modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.
PAM and BLOSUM are the widely used substitution matrices in the sequence alignment. The mathematical modeling of PAM matrices is explained in these slides.
this presentation is about bioinformatics. the contents of bioinformatics are as under:
1.Introduction to bioinformatics.
2.Why bioinformatics is necessary?
3.Goals of bioinformatics
4.Field of bioinformatics
5.Where bioinformatics help?
6.Applications of bioinformatics
7.Software and tools of bioinformatics
8.References
Transcriptomics is the study of RNA, single-stranded nucleic acid, which was not separated from the DNA world until the central dogma was formulated by Francis Crick in 1958, i.e., the idea that genetic information is transcribed from DNA to RNA and then translated from RNA into protein.
Systems biology is the computational and mathematical modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach (holism instead of the more traditional reductionism) to biological research.
PAM and BLOSUM are the widely used substitution matrices in the sequence alignment. The mathematical modeling of PAM matrices is explained in these slides.
this presentation is about bioinformatics. the contents of bioinformatics are as under:
1.Introduction to bioinformatics.
2.Why bioinformatics is necessary?
3.Goals of bioinformatics
4.Field of bioinformatics
5.Where bioinformatics help?
6.Applications of bioinformatics
7.Software and tools of bioinformatics
8.References
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble and analyze the function and structure of genomes
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
INTRODUCTION
WHAT IS DATA AND DATABASE?
WHAT IS BIOLOGICAL DATABASE?
TYPES OF BIOLOGICAL DATABASE
PRIMARY DATABASE
Nucleic acid sequence database
Protein sequence database
SECONDARY DATABASE
COMPOSITE DATABASE
TERTIARY DATABASE
WHY NEED?
CONCLUSION
REFRENCES
A 45min presentation given at the 'Getting published in Nature's Scientific Data journal', hosted by the University of Cambridge Research Data Management team (www.data.cam.ac.uk). Presented on Monday 11th January 2016.
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble and analyze the function and structure of genomes
Module 2 Sequence similarity.
Part of bioinformatics training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
INTRODUCTION
WHAT IS DATA AND DATABASE?
WHAT IS BIOLOGICAL DATABASE?
TYPES OF BIOLOGICAL DATABASE
PRIMARY DATABASE
Nucleic acid sequence database
Protein sequence database
SECONDARY DATABASE
COMPOSITE DATABASE
TERTIARY DATABASE
WHY NEED?
CONCLUSION
REFRENCES
A 45min presentation given at the 'Getting published in Nature's Scientific Data journal', hosted by the University of Cambridge Research Data Management team (www.data.cam.ac.uk). Presented on Monday 11th January 2016.
Synthetic Biology Behind the Creation of Body Parts by Daniel BednarikDaniel Bednarik
Daniel Bednarik is a biopharmaceutical researcher who lives and works in Germantown, Maryland. He is the Vice President of the Molecular Engineering Unit Operations at Intrexon Corporation in Germantown, Maryland.
Daniel Bednarik is a prominent biopharmaceutical researcher who has had experience in a number of roles within the world of biopharmaceutical research, giving him a very diverse background in his field. He currently works at Intrexon Corporation in Germantown, Maryland. He is the Vice President of the Molecular Engineering Unit Operations at Intrexon, and greatly enjoys his rewarding career.
Why the world needs phenopacketeers, and how to be onemhaendel
Keynote presented at the the Ninth International Biocuration Conference Geneva, Switzerland, April 10-14, 2016
The health of an individual organism results from complex interplay between its genes and environment. Although great strides have been made in standardizing the representation of genetic information for exchange, there are no comparable standards to represent phenotypes (e.g. patient disease features, variation across biodiversity) or environmental factors that may influence such phenotypic outcomes. Phenotypic features of individual organisms are currently described in diverse places and in diverse formats: publications, databases, health records, registries, clinical trials, museum collections, and even social media. In these contexts, biocuration has been pivotal to obtaining a computable representation, but is still deeply challenged by the lack of standardization, accessibility, persistence, and computability among these contexts. How can we help all phenotype data creators contribute to this biocuration effort when the data is so distributed across so many communities, sources, and scales? How can we track contributions and provide proper attribution? How can we leverage phenotypic data from the model organism or biodiversity communities to help diagnose disease or determine evolutionary relatedness? Biocurators unite in a new community effort to address these challenges.
Relational databases are perhaps the most commonly used data management systems. In relational databases, data is modeled as a collection of disparate tables. In order to unify the data within these tables, a join operation is used. This operation is expensive as the amount of data grows. For information retrieval operations that do not make use of extensive joins, relational databases are an excellent tool. However, when an excessive amount of joins are required, the relational database model breaks down. In contrast, graph databases maintain one single data structure---a graph. A graph contains a set of vertices (i.e. nodes, dots) and a set of edges (i.e. links, lines). These elements make direct reference to one another, and as such, there is no notion of a join operation. The direct references between graph elements make the joining of data explicit within the structure of the graph. The benefit of this model is that traversing (i.e. moving between the elements of a graph in an intelligent, direct manner) is very efficient and yields a style of problem-solving called the graph traversal pattern. This session will discuss graph databases, the graph traversal programming pattern, and their use in solving real-world problems.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
The future always feels like it’s running late. Human imagination works harder than human enterprise, but at any given moment, scientists and engineers are redesigning future technology and the world around us in big and small ways
The age of gene editing - Workshop on innovations in food and agriculture sys...OECD Environment
The workshop took place in Paris on 25-26 February 2016. Its central aim was to discuss with experts how scientific, technological, and farm practice innovation can improve productivity and sustainability in the food and agricultural sector, with a focus on international collaboration on gene editing techniques. It was introduced in the form of a presentation entitled ‘The Age of Gene editing’, produced by Steffi Friedrichs (STI), which played a pivotal role during the expert discussions.
Introduction to Gene Mining Part A: BLASTn-off!adcobb
In this lesson, students will learn to use bioinformatics portals and tools to mine plant versions of human genes. Student handout and teacher resource materials are available at www.Araport.org, Teaching Resources (Community tab). Suitable for grades 9-12 or first year undergraduate students.
A huge revolution has taken place in the area of Genomic science. Sequencing of millions of DNA strands in parallel and also getting a higher throughput reduces the need to implement fragment cloning methods, where extra copies of genes are produced. The methodology of sequencing a large number of DNA strands in parallel is known as Next Generation Sequencing technique. An overview of how different sequencing methods work is described. Selection of two sequencing methods, Sanger Sequencing method and Next generation sequencing method and analysis of the parameters used in both these techniques. A Comparative study of these two methods is carried out accordingly. An overview of when to use Sanger sequencing and when to use Next generation sequencing is described. Increase in the amount of genomic data has given rise to challenges like sharing, integrating and analyzing the genetic data. Therefore, application of one of the big data techniques known as Map Reduce model is used to sequence the genetic data. A flow chart of how genetic is processed using MapReduce model is also present. Next Generation Sequencing for analysis of huge amount of genetic data is very useful but it has few limitations such as scaling and efficiency. Fortunately recent researches have proven that these demerits of Next Generation Sequencing can be easily overcome by implementing big data methodologies. Chinmayee C | Amrita Nischal | C R Manjunath | Soumya K N"Next Generation Sequencing in Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12975.pdf http://www.ijtsrd.com/computer-science/bioinformatics/12975/next-generation-sequencing-in-big-data/chinmayee-c
20 years of evolution in data production in health and life sciencesslecrom
I share feedbacks as a genomics core facility scientific head and as a facilities manager over the last 20 years. I go trough evolution in the high throughput sequencing field and discuss about data storage and sharing.
Next generation genomics: Petascale data in the life sciencesGuy Coates
Keynote presentation at OGF 28.
The year 2000 saw the release of "The" human genome, the product of a the combined sequencing effort of the whole planet. In 2010, single institutions are sequencing thousands of genomes a year, producing petabytes of data. Furthermore, many of the large scale sequencing projects are based around international collaboration and consortia. The talk will explore how Grid and Cloud technologies are being used to share genomics data around the planet, revolutionizing life science research.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
3. So,howisthisdataproduced??
● The data produced by the social
media in a single minute is
astounding!
● All this data is stored and
analyzed for many obvious
reasons.
5. humangenomeproject
● It is an international scientific research project.
● The goal of the project is to determine the sequence of chemical base
pairs that make up human DNA.
● The project was successfully completed in 2003 and 90% of the human
genome was sequenced.
● This was just a start of a new era of sequencing.
9. Bigdataparking
Clouds are a solution, but they also throw up fresh challenges. Ironically, their proliferation
can cause a bottleneck if data end up parked on several clouds and thus still need to
be moved to be shared.
And clouds means entrusting valuable data to a distant service provider who may be
subject to power changes or other disruptions.
Scientists experiment with different constellations to suit their needs and trust levels.
Clouds can be used for both data storage and computing.
This reduces the overhead of transferring the data into a local machine and computing it on
12. ● The information necessary to build and control any living organism is written in its
genome and it took 13 years to decipher.
● A single decade later sequencing a genome takes a few hours on a machine that fits on
a tabletop.
● The tsunami of biological data generates new problems, it needs to be analysed
properly to unearth and retrieve the exciting knowledge it contains.
● Getting the most from the data requires interpreting them in light of all the relevant prior
knowledge.
● That means scientists have to store a large data sets, and analyse, compare and share
them - not simple tasks.
Whatareweconcernedabout??
13. It is estimated that by 2025 , exabytes(1018) of genomics data will be produced globally
and will far exceed from twitter and facebook.
Moreover, the genomics data being produced roughly doubles every year and will
require new solutions in precision and accuracy for storage, analysis and sharing.
The European Bioinformatics Institute(EBI), UK, part of the European Molecular Biology
Laboratory and one of the world’s largest biology-data repositories, currently stores
20 petabytes(20*1015) of data and back-ups about genes, proteins and small
molecules.
Genomic data accounts for 2 petabytes of that, a number that more than doubles every
year.
DataExplosion
18. Microsoft has already started storing some of its data using DNA.
The first phase of demonstration was successfully completed.
Microsoft partnered with the startup “Twist Bioscience” which produced oligonucleotides for
them and arranged them in the sequence specified.
One of the drawbacks of this storage is it cannot be commercialised.