This document appears to be a list of genes or genetic markers from the Medicago truncatula genome arranged in order with their physical position on chromosomes indicated in the left column. There are over 500 entries included in the list with various identifiers like "cp", "OG", "CaM", and others potentially representing different genes or markers. The document provides a dense listing of genetic data from M. truncatula but does not include any additional context or explanation.
Comparative Genomics with GMOD and BioPerlJason Stajich
BioPerl is an open source toolkit for bioinformatics data manipulation written in Perl. It contains modules for reading and writing sequence data in common formats, manipulating sequences, parsing BLAST reports and multiple sequence alignments. BioPerl objects represent sequences, features, annotations and search results in a flexible and extensible way. The toolkit is widely used for tasks like sequence analysis, parsing bioinformatics software output, and accessing biological databases.
This document summarizes the achievements, lessons learned, challenges, and gaps from Phase II of the Tropical Legumes II Project. Key achievements include the release of 129 new varieties of six legume crops, training of scientists, and production of over 250,000 tons of seed. Lessons highlight the importance of partnerships, seed systems approaches like community seed banks, and policies supporting the seed industry. Remaining challenges include strengthening national breeding programs and seed production capacity. Gaps include improving variety adoption, linking seed systems to markets, and ensuring continuous seed supply during droughts.
Comparative genomics is the study of genome structure and function across species. Sequencing entire genomes is non-optimal due to vast numbers of species and large genome sizes, and individuals within a species have genetically distinct genomes. Comparative genomics addresses these issues using approaches like gene prediction algorithms that use features of protein-coding regions and tools that find putative genes or syntenic regions between genomes.
This document discusses key concepts in comparative genomics including orthologs, paralogs, speciation, and clusters of orthologous genes (COGs). It defines orthologs as genes evolved from a common ancestor through speciation that retain the same function, while paralogs are related through duplication and may evolve new functions. COGs are groups of orthologous genes from different species that are more similar to each other than to other genes within individual genomes. The document notes that COGs can be used to predict gene function and track evolutionary divergence. It provides an example of the NCBI COG database containing over 136,000 proteins from 50 bacteria, 13 archaea and 3 eukaryotes classified into CO
This document provides an overview of comparative genomics. It begins by defining genomics and its subfields, including comparative genomics which compares complete genome sequences across species. Tools for comparative genomics like BLAST and synteny are discussed. The history of comparative genomics from early virus comparisons to current eukaryote analyses is summarized. Methods for comparative analysis include examining genome structure, coding regions, protein content, and non-coding regions. General databases useful for comparative genomics are also listed.
This document discusses comparative genomics and the evolution of genes using Drosophila species as a model. It summarizes that 12 Drosophila genomes were compared phylogenetically and this comparison revealed patterns of gene family expansion and contraction over time as well as structural changes and rearrangements like in the Hox cluster. The multi-species comparisons provided strong evidence for gene models and functions are conserved despite sequence divergence across the Drosophila phylogeny.
Comparative genomics involves comparing the genetic material and genome sequences of different species to understand evolution, gene function, and disease. For cereals like wheat, rice, maize, and barley, comparative genomics has revealed conserved gene order and colinearity between species, helping to map genes. While rice has a small genome and was fully sequenced first, studies compare features across cereal genomes of different sizes to understand genome structure and evolution in grasses. Comparative genomics is improving our ability to predict gene function and location in related species.
1) Agri-startups are small companies developing new products or services in agriculture to address unmet needs. They go through various phases from ideation to scaling up.
2) IARI is supporting agri-startups through its Arise program which provides incubation support like training, mentoring and helping startups pitch to investors.
3) A PhD thesis identified the top characteristics of successful agri-entrepreneurs as innovativeness, social networking, risk-taking and resiliency.
Comparative Genomics with GMOD and BioPerlJason Stajich
BioPerl is an open source toolkit for bioinformatics data manipulation written in Perl. It contains modules for reading and writing sequence data in common formats, manipulating sequences, parsing BLAST reports and multiple sequence alignments. BioPerl objects represent sequences, features, annotations and search results in a flexible and extensible way. The toolkit is widely used for tasks like sequence analysis, parsing bioinformatics software output, and accessing biological databases.
This document summarizes the achievements, lessons learned, challenges, and gaps from Phase II of the Tropical Legumes II Project. Key achievements include the release of 129 new varieties of six legume crops, training of scientists, and production of over 250,000 tons of seed. Lessons highlight the importance of partnerships, seed systems approaches like community seed banks, and policies supporting the seed industry. Remaining challenges include strengthening national breeding programs and seed production capacity. Gaps include improving variety adoption, linking seed systems to markets, and ensuring continuous seed supply during droughts.
Comparative genomics is the study of genome structure and function across species. Sequencing entire genomes is non-optimal due to vast numbers of species and large genome sizes, and individuals within a species have genetically distinct genomes. Comparative genomics addresses these issues using approaches like gene prediction algorithms that use features of protein-coding regions and tools that find putative genes or syntenic regions between genomes.
This document discusses key concepts in comparative genomics including orthologs, paralogs, speciation, and clusters of orthologous genes (COGs). It defines orthologs as genes evolved from a common ancestor through speciation that retain the same function, while paralogs are related through duplication and may evolve new functions. COGs are groups of orthologous genes from different species that are more similar to each other than to other genes within individual genomes. The document notes that COGs can be used to predict gene function and track evolutionary divergence. It provides an example of the NCBI COG database containing over 136,000 proteins from 50 bacteria, 13 archaea and 3 eukaryotes classified into CO
This document provides an overview of comparative genomics. It begins by defining genomics and its subfields, including comparative genomics which compares complete genome sequences across species. Tools for comparative genomics like BLAST and synteny are discussed. The history of comparative genomics from early virus comparisons to current eukaryote analyses is summarized. Methods for comparative analysis include examining genome structure, coding regions, protein content, and non-coding regions. General databases useful for comparative genomics are also listed.
This document discusses comparative genomics and the evolution of genes using Drosophila species as a model. It summarizes that 12 Drosophila genomes were compared phylogenetically and this comparison revealed patterns of gene family expansion and contraction over time as well as structural changes and rearrangements like in the Hox cluster. The multi-species comparisons provided strong evidence for gene models and functions are conserved despite sequence divergence across the Drosophila phylogeny.
Comparative genomics involves comparing the genetic material and genome sequences of different species to understand evolution, gene function, and disease. For cereals like wheat, rice, maize, and barley, comparative genomics has revealed conserved gene order and colinearity between species, helping to map genes. While rice has a small genome and was fully sequenced first, studies compare features across cereal genomes of different sizes to understand genome structure and evolution in grasses. Comparative genomics is improving our ability to predict gene function and location in related species.
1) Agri-startups are small companies developing new products or services in agriculture to address unmet needs. They go through various phases from ideation to scaling up.
2) IARI is supporting agri-startups through its Arise program which provides incubation support like training, mentoring and helping startups pitch to investors.
3) A PhD thesis identified the top characteristics of successful agri-entrepreneurs as innovativeness, social networking, risk-taking and resiliency.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
Development of Agriculture Sector in Malaysiasuraya izad
The agricultural sector was previously the backbone of Malaysia's economy, contributing 39.3% to GDP at independence. It was dominated by rubber and palm oil plantations and provided the majority of export earnings. Since then, the sector has diversified and the government has intervened to improve productivity and incomes, especially for smallholders. Key programs include in-situ development projects, land consolidation schemes, and R&D to promote crops and boost self-sufficiency in food. While the contribution of agriculture has declined, it remains important for rural development, economic stability, and food security.
The document discusses different types of agriculture practiced in India. It describes primitive subsistence farming, intensive subsistence farming, commercial farming, and plantation farming. It also discusses major crops grown in India like rice, millets, cotton, and coffee. Agricultural development aims to increase farm production to meet population growth through expanding cropped area, irrigation, use of fertilizers and high-yielding seeds, and farm mechanization. The ultimate goal is increased food security. Farming in India has a long history and India ranks second worldwide in agricultural output. In the US, agriculture is a major industry and the country exports food, with over 2 million farms covering over 900 million acres.
Powerpoint Search Engine has collection of slides related to specific topics. Write the required keyword in the search box and it fetches you the related results.
This document summarizes research from various institutions on dissecting and utilizing disease QTL for rice and maize. It describes QTL identified for different diseases in rice and maize, near-isogenic lines developed to characterize QTL, and advanced breeding lines with improved disease resistance being tested in Kenya, India, and Indonesia. Results are presented on QTL effects, candidate genes associated with resistance, and histopathological analysis of fungal infection in maize. Deliverables over the project include understanding and dissecting disease traits through QTL mapping, developing resistant lines, and characterizing QTL through near-isogenic lines and histopathology.
1. The document discusses several projects aimed at dissecting and utilizing quantitative trait loci (QTL) that confer disease resistance in rice and maize.
2. Multiple strategies are being used across different research groups, including linkage mapping, association mapping, analysis of near-isogenic lines and mutants, to map QTL and isolate the underlying genes.
3. Preliminary results from some projects show correlations between increased expression of germin-like protein genes and enhanced disease resistance in rice, as well as identification of QTL associated with resistance to different diseases like blast and drought tolerance.
Presentation made by the GCP Director during the CGIAR Fund Council (FC) visit to CIMMYT (GCP's host), on the sidelines of the FC meeting in Mexico in May 2014.
The document describes an Integrated Breeding Platform (IBP) that aims to provide a user-friendly platform and tools to support plant breeders in Africa. The IBP will provide access to high-throughput breeding services, data management tools, analysis pipelines, and decision support tools. It will also offer support and communities of practice. The core is a Breeding Management System that will integrate various tools to support the entire breeding workflow from crossing to variety release. The IBP will be implemented through regional hubs and with support from agricultural universities to train new breeders and increase adoption of integrated breeding approaches.
This document summarizes work done by CIAT to improve common bean productivity in marginal environments in sub-Saharan Africa. It describes the use of markers for biotic stress resistance, including for diseases like BCMV, bruchid resistance, and common bacterial blight. It also discusses ongoing work on drought tolerance using MARS and MAGIC populations. Data management practices are being improved and capacity building activities with African partners are highlighted. The overall aim is to develop improved germplasm with biotic and abiotic stress resistance to increase yields for smallholder farmers in Africa.
The document outlines objectives and activities to improve groundnut productivity in marginal environments in Sub-Saharan Africa through genetic resources and genomic resources. Key activities include confirming disease and drought resistance sources, developing SNP markers, mapping disease resistance QTLs, using MABC to introgress resistance, and strengthening NARS capacity. Milestones include confirming resistance in 10 new genotypes, producing new synthetics, phenotyping CSSLs, developing a SNP assay, anchoring QTL regions to a physical map, and empowering 2 NARS partners to breed varieties with multiple traits.
This document summarizes activities from Phase II of the Tropical Legumes 1 project, which aims to improve cowpea productivity in marginal environments in sub-Saharan Africa. Key activities included developing a multi-parent advanced generation intercross (MAGIC) population, generating genomic resources like an improved consensus map, and employing marker-assisted recurrent selection (MARS) and marker-assisted backcrossing (MABC) to develop improved cowpea lines. The project involved multiple institutions across Africa and generated important products like a MAGIC population, genomic resources, quantitative trait loci (QTL) discoveries, and preliminary advanced breeding lines.
The document summarizes objectives and activities from Tropical Legumes I and II projects. The projects aimed to improve productivity of tropical legumes through developing genomic resources, identifying molecular markers and genes for biotic and drought stress resistance. Key outputs included genomic resources, genetic stocks with traits introgressed, molecular markers, improved germplasm, trained scientists, and data management strategies. The projects collaborated with partners in Africa and South Asia to build breeding capacity and validate approaches in drought-prone environments.
This document discusses challenges and opportunities for adoption of modern breeding tools in developing countries. It covers several topics: human expertise and training needs, infrastructure requirements, access to information and technology, data management challenges, availability of analytical tools and support services, importance of effective partnerships, and changing mindsets. Supporting adoption will require a coordinated, multi-pronged approach including training, infrastructure investments, improved data management practices, access to analytical tools and genotyping services, and strong support networks.
This document provides an overview of the Integrated Breeding Platform (IBP) and its Breeding Management System (BMS). The BMS aims to provide breeders with a simple, integrated suite of informatics tools to facilitate breeding workflows from cross-making to variety release. It will include modules for list management, breeding logistics, nursery and trial management, phenotyping, genotyping, statistical analysis, and decision support tools. The system is being designed for use by both public and small-scale breeders in developing countries.
The Crop Ontology is a controlled vocabulary for plant breeding data that aims to standardize terminology and enable data sharing and interoperability. It provides definitions and relationships for traits, phenotypes, experimental factors, and other relevant concepts. The ontology is being developed collaboratively by various crop centers and is accessible online. It is aligned with other related ontologies and being converted to semantic web formats to integrate with other plant data resources and enable linked open data.
This document summarizes a project that aims to improve cowpea productivity in marginal environments in sub-Saharan Africa through marker-assisted breeding. The project is applying genomic resources and marker-assisted selection to introgress genes for drought tolerance, heat tolerance, and resistance to pests and diseases from donor parents into popular local varieties. Researchers are developing cowpea consensus genetic maps, identifying quantitative trait loci (QTLs) for important traits, and employing both marker-assisted backcrossing and marker-assisted recurrent selection in breeding programs in several countries to develop improved cowpea varieties with locally adapted traits.
The GCP is a 10-year program launched in 2003 with a $15 million annual budget to improve crops in harsh drought-prone environments in Africa, Asia, and Latin America. It partners with over 200 institutes including the CGIAR. The GCP aims to use genetic diversity and plant science to develop improved crop varieties for food security. It focuses on cereals, legumes, roots, and tubers through research themes, initiatives, and an integrated breeding platform. Major achievements include developing genetic resources, genomic tools, and stress-resistant markers. Moving forward, the GCP faces challenges in monitoring and evaluating impact, ensuring product delivery, and strengthening partnerships to complete its work by 2013.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
Comparative genomics involves comparing genomes to discover similarities and differences. It can provide insights into evolutionary relationships, help predict gene function, and aid in drug discovery. The first step is often aligning genome sequences using tools like BLAST or MUMmer. Genomes can then be compared at various levels, such as overall nucleotide statistics, genome structure, and coding/non-coding regions. Comparing gene and protein content across genomes helps predict functions. Conserved genomic features across species also aid prediction. Insights into genome evolution come from studying molecular events like inversions and duplications. Comparative genomics has impacted phylogenetics and drug target identification.
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
Development of Agriculture Sector in Malaysiasuraya izad
The agricultural sector was previously the backbone of Malaysia's economy, contributing 39.3% to GDP at independence. It was dominated by rubber and palm oil plantations and provided the majority of export earnings. Since then, the sector has diversified and the government has intervened to improve productivity and incomes, especially for smallholders. Key programs include in-situ development projects, land consolidation schemes, and R&D to promote crops and boost self-sufficiency in food. While the contribution of agriculture has declined, it remains important for rural development, economic stability, and food security.
The document discusses different types of agriculture practiced in India. It describes primitive subsistence farming, intensive subsistence farming, commercial farming, and plantation farming. It also discusses major crops grown in India like rice, millets, cotton, and coffee. Agricultural development aims to increase farm production to meet population growth through expanding cropped area, irrigation, use of fertilizers and high-yielding seeds, and farm mechanization. The ultimate goal is increased food security. Farming in India has a long history and India ranks second worldwide in agricultural output. In the US, agriculture is a major industry and the country exports food, with over 2 million farms covering over 900 million acres.
Powerpoint Search Engine has collection of slides related to specific topics. Write the required keyword in the search box and it fetches you the related results.
This document summarizes research from various institutions on dissecting and utilizing disease QTL for rice and maize. It describes QTL identified for different diseases in rice and maize, near-isogenic lines developed to characterize QTL, and advanced breeding lines with improved disease resistance being tested in Kenya, India, and Indonesia. Results are presented on QTL effects, candidate genes associated with resistance, and histopathological analysis of fungal infection in maize. Deliverables over the project include understanding and dissecting disease traits through QTL mapping, developing resistant lines, and characterizing QTL through near-isogenic lines and histopathology.
1. The document discusses several projects aimed at dissecting and utilizing quantitative trait loci (QTL) that confer disease resistance in rice and maize.
2. Multiple strategies are being used across different research groups, including linkage mapping, association mapping, analysis of near-isogenic lines and mutants, to map QTL and isolate the underlying genes.
3. Preliminary results from some projects show correlations between increased expression of germin-like protein genes and enhanced disease resistance in rice, as well as identification of QTL associated with resistance to different diseases like blast and drought tolerance.
Presentation made by the GCP Director during the CGIAR Fund Council (FC) visit to CIMMYT (GCP's host), on the sidelines of the FC meeting in Mexico in May 2014.
The document describes an Integrated Breeding Platform (IBP) that aims to provide a user-friendly platform and tools to support plant breeders in Africa. The IBP will provide access to high-throughput breeding services, data management tools, analysis pipelines, and decision support tools. It will also offer support and communities of practice. The core is a Breeding Management System that will integrate various tools to support the entire breeding workflow from crossing to variety release. The IBP will be implemented through regional hubs and with support from agricultural universities to train new breeders and increase adoption of integrated breeding approaches.
This document summarizes work done by CIAT to improve common bean productivity in marginal environments in sub-Saharan Africa. It describes the use of markers for biotic stress resistance, including for diseases like BCMV, bruchid resistance, and common bacterial blight. It also discusses ongoing work on drought tolerance using MARS and MAGIC populations. Data management practices are being improved and capacity building activities with African partners are highlighted. The overall aim is to develop improved germplasm with biotic and abiotic stress resistance to increase yields for smallholder farmers in Africa.
The document outlines objectives and activities to improve groundnut productivity in marginal environments in Sub-Saharan Africa through genetic resources and genomic resources. Key activities include confirming disease and drought resistance sources, developing SNP markers, mapping disease resistance QTLs, using MABC to introgress resistance, and strengthening NARS capacity. Milestones include confirming resistance in 10 new genotypes, producing new synthetics, phenotyping CSSLs, developing a SNP assay, anchoring QTL regions to a physical map, and empowering 2 NARS partners to breed varieties with multiple traits.
This document summarizes activities from Phase II of the Tropical Legumes 1 project, which aims to improve cowpea productivity in marginal environments in sub-Saharan Africa. Key activities included developing a multi-parent advanced generation intercross (MAGIC) population, generating genomic resources like an improved consensus map, and employing marker-assisted recurrent selection (MARS) and marker-assisted backcrossing (MABC) to develop improved cowpea lines. The project involved multiple institutions across Africa and generated important products like a MAGIC population, genomic resources, quantitative trait loci (QTL) discoveries, and preliminary advanced breeding lines.
The document summarizes objectives and activities from Tropical Legumes I and II projects. The projects aimed to improve productivity of tropical legumes through developing genomic resources, identifying molecular markers and genes for biotic and drought stress resistance. Key outputs included genomic resources, genetic stocks with traits introgressed, molecular markers, improved germplasm, trained scientists, and data management strategies. The projects collaborated with partners in Africa and South Asia to build breeding capacity and validate approaches in drought-prone environments.
This document discusses challenges and opportunities for adoption of modern breeding tools in developing countries. It covers several topics: human expertise and training needs, infrastructure requirements, access to information and technology, data management challenges, availability of analytical tools and support services, importance of effective partnerships, and changing mindsets. Supporting adoption will require a coordinated, multi-pronged approach including training, infrastructure investments, improved data management practices, access to analytical tools and genotyping services, and strong support networks.
This document provides an overview of the Integrated Breeding Platform (IBP) and its Breeding Management System (BMS). The BMS aims to provide breeders with a simple, integrated suite of informatics tools to facilitate breeding workflows from cross-making to variety release. It will include modules for list management, breeding logistics, nursery and trial management, phenotyping, genotyping, statistical analysis, and decision support tools. The system is being designed for use by both public and small-scale breeders in developing countries.
The Crop Ontology is a controlled vocabulary for plant breeding data that aims to standardize terminology and enable data sharing and interoperability. It provides definitions and relationships for traits, phenotypes, experimental factors, and other relevant concepts. The ontology is being developed collaboratively by various crop centers and is accessible online. It is aligned with other related ontologies and being converted to semantic web formats to integrate with other plant data resources and enable linked open data.
This document summarizes a project that aims to improve cowpea productivity in marginal environments in sub-Saharan Africa through marker-assisted breeding. The project is applying genomic resources and marker-assisted selection to introgress genes for drought tolerance, heat tolerance, and resistance to pests and diseases from donor parents into popular local varieties. Researchers are developing cowpea consensus genetic maps, identifying quantitative trait loci (QTLs) for important traits, and employing both marker-assisted backcrossing and marker-assisted recurrent selection in breeding programs in several countries to develop improved cowpea varieties with locally adapted traits.
The GCP is a 10-year program launched in 2003 with a $15 million annual budget to improve crops in harsh drought-prone environments in Africa, Asia, and Latin America. It partners with over 200 institutes including the CGIAR. The GCP aims to use genetic diversity and plant science to develop improved crop varieties for food security. It focuses on cereals, legumes, roots, and tubers through research themes, initiatives, and an integrated breeding platform. Major achievements include developing genetic resources, genomic tools, and stress-resistant markers. Moving forward, the GCP faces challenges in monitoring and evaluating impact, ensuring product delivery, and strengthening partnerships to complete its work by 2013.
This project aims to improve rice productivity in West Africa through marker-assisted recurrent selection for drought tolerance and yield potential. Highlights include establishing drought evaluation sites, characterizing the target environments, and developing three MARS populations. The populations were phenotyped in multiple locations for drought tolerance and yield. Promising lines with higher yields under stress were identified. The project also built capacity through training, technology exchange, and developing phenotyping databases to manage data.
This document summarizes a project to improve sorghum productivity in Mali through an integrated Marker-Assisted Recurrent Selection (MARS) approach. The project phenotyped multiple traits across multiple environments to identify favorable alleles and define ideal genotypes. It conducted several breeding cycles and genotyped populations to detect QTLs. The main products were improved sorghum material and capacity building activities. Key lessons included validating MARS for African sorghum breeding and considering different trait combinations for varying environments.
1) Several wheat projects presented products and activities that may require support beyond 2014, including developing new wheat lines and varieties with improved drought and heat tolerance.
2) The wheat product catalogue was updated with additions and deletions.
3) Detailed costing of post-2014 activities will be provided in October to inform future planning.
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Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
3. - Assembly of genomics and germplasm
resources
- Development of comparative maps and
framework genetic markers for target
crops
- Assignment of genes and pathways to
phenotypes
- Validation of genes and pathways
Four objectives
4. Large-scale molecular markers including SSR, SNP
and DArT markers for less-studied crops
Genetic maps with moderate marker density available
Cost-effective SNP genotyping platform i.e.
GoldenGate assays, KASPar assays for a number of
crops
Transcriptomic resources for chickpea, pigeonpea,
sweetpotato, cowpea, cassava
Physical maps for cassava, cowpea
Mutant collections in common bean, potatoes
Utilization of mutant collections in rice for function
analysis
Progress updates- i
5. Cloning of AltSB (SbMATE1) and Pup1 in sorghum and
rice and diagnostic markers
Molecular markers associated with traits of interest to
breeders in cowpea, chickpea
Gene expression analysis for drought tolerance in rice
Bioinformatic tools for comparative genomics
analysis
Gene expression analysis for selected transcription
factors in wheat for drought tolerance
Allele discovery for drought tolerance in sorghum and
rice
Bioinformatic tools for gene expression analysis
ISMU pipeline for analysis of NGS data…
Progress updates- ii
6. Comparative Genomics Challenge Initiative
(PDC: Leon Kochian USDA/ARS & Cornell Uni)
• This CI takes advantage of previous GCP projects
where two genes AltSB (SbMATE1) and Pup1 have
either already been cloned.
Rice PUP1 Sorghum AltSB
Clone
homologs
Clone
homologs
Maize Sorghum Maize Rice
Verify role in
P efficiency
Verify role in Al
tolerance
P Efficient
Maize
P Efficient
Sorghum
Products for breeding programs
in developing countries
Al Tolerant
Maize
Al Tolerant
Rice
Products for breeding programs
in developing countries
Pyramid P Eff & Al Tol
7. • Rice:
QTL mapping and GWAS for Al-tolerance (G7009.07, Susan McCouch);
cloning and characterization of Pup1 and dissemination of breeding
linesto NARS partners (G7010.03.04, Sigrid Heuer)
• Maize:
Cloning and characterization of Al-tolerance
(G7010.03.02, Claudia Guimaraes);
Cloning and characterization of Pup1 (G7010.03.01, Leon Kochian);
breeding for Al-tolerance and P-efficiency (G7010.03.05, Sam Gudu)
• Sorghum:
Cloning and characterization of P-efficiency
(G7009.03, Jura Magalhaes)
breeding for Al-tolerance and P-efficiency (G7010.03.03, Eva
Weltzein)
Comparative genomics RI
8. • Pup1 major tolerance gene is a constitutive enhancer of root growth
• Acts upstream of genes with key function in root growth and stress response
• Final set of Pup1 gene/allele specific markers available
• Development of Pup1-breeding lines by MABC completed
• First field data confirm beneficial effect of Pup1
• Seed increase ongoing at IRRI and ICABIOGRAD
• Mapping of Al-tolerance in rice is underway
Sigrid Heuer, IRRI
9. Genotyping and Phenotyping of
Sorghum Association Panel
• Initially genotyped with Illumina 1536 SNP chip by M Hamblin
• Currently being genotyped by sequencing by Ed Buckler and
Sharon Mitchell as part of their NSF BREAD grant .
-Developed multiplexing approach to sequence multiple
samples in one lane of Illumina High-Seq.
- Developed a bioinformatics pipeline for SNP ID
- Hope to add 100,000 to 200,000 SNPs to each member of
association panel.
• Have phenotyped entire panel for Al tolerance – waiting for
genotyping to be completed to conduct GWAS on Al tolerance.
• Have phenotyped the IGD part of the panel (converted lines) for
P efficiency at Embrapa.
•Will soon phenotype panel for P efficiency and root architecture in
low P soils in greenhosse at Cornell
Leon Kochian, USDA/ARS & Cornell Uni
10. GWAS of Rice 3D Root Architecture Traits
• Have completed phenotyping rice for 3-D RSA traits under
control conditions in gel-based media. Phenotyped the
McCouch’s NSF-TV rice diversity panel (500 lines) and also
bi-parental mapping population (168 lines).
• That involved phenotyping approximately 2000 individual
plants in gellan gum cylinders.
• Roots imaged at 3, 6, 9, & 12 days after
planting to include dynamic
growth parameters.
• Randy is in Taiwan for the summer
where he as nearly completes 3D
reconstructions and quantification
of his 20 RSA traits.
• GWAS analysis will be completed
in Fall with 950k SNP chip.
Leon Kochian, USDA/ARS & Cornell Uni
11. Breeding value of AltSB
0.00
1.00
2.00
3.00
Control (-Al) TT tt
Yield(tons/ha)Allelic substitution effect:
100 RILs BR007 x SC283
r=0.28 (P=0.0047):
nutrient sol. vs. field
Drought x Al
tt TT 0.5 - 1 ton/ha
Chr 3
Map position (cM)
-log10(p)
0 50 100 150 200 250
024681012
Gy
Gy_flo
Rnrg
Alt
SB
• Background SNP markers
• Association pipeline for Al
tolerance
• Assessment of AltSB on
acid soils (grain yield
advantage)
Jura Magalhaes, EMBRAPA
12. Establish network for genomics community for
enhanced discussions in the area of
development and application of genomic tools
Develop user friendly portal that will present
information on tools, resources developed by
GCP or available in public domain to offer one-
stop shop solution to the breeding community
Broker-access to economically priced
genotyping and sequencing services
Agricultural Genomics Network
(AGN)
13. Consensus:
AGN is a GREAT initiative, it would encourage new
breeders also to have access and provide all help to
implement molecular breeding.
AGN will keep the existing GCP community vibrant and
active in post-GCP era!
Markers of choice for breeding applications:
SNPs (also SSRs in some cases!)
Genotyping through outsourcing is preferred and
accepted solution by NARS breeders
Discussions on AGN
14. Suggestions:
A broader survey about requirements of tools,
resources on portals may be conducted
Some basic information about molecular breeding
together with tutorials may be kept on portal
Helpdesk to respond on genomics related questions in
time is URGENTLY required.
Portal can be in wiki style
Sustainability is an important issue in post-GCP era.
Discussions on AGN
15. Comparative genomics
vs
species specific genomics research
Excellent for understanding the genome
evolution, gene function and trait mechanism
Applied aspects- few examples?
Cloning of one gene in one species- how useful
this is for the other species? Pup1 and Al-
tolerance in cereals?
Development of diploid genome physical map
for applications in 4x groundnuts?
Species specific genomic resource
development is no more an expensive and time
consuming task?
16. Challenges/ Opportunities
Data management and sharing (continuous discussions
Analysis of large scale datasets especially NGS data
(working with international players/Theme 3)
Outsourcing vs. in-house work to generate data in cost-
effective and timely fashion (BGI collaboration)
Conversion of genomics research platform in breeding
application platforms (KASar assays in collaboration
with Theme 2/ IBP)
Capacity building – an important component of
Genomics Integrated Breeding Services
17. Utilization of developed genomic resources
Assemble large scale informative SNPs for breeding
applications
Accelerated activities of Comparative Genomics CI
towards identifying diagnostic markers for Al-tolerance
and P-uptake in targeted cereals
Publication of large scale datasets
Engage CGIAR community by implementing AGN under
GIBS
Perspectives
Thank you !