1660247400
XSim is a fast and user-friendly tool to simulate sequence data and complicated pedigree structures
# Load XSim
using XSim
# Simulate genome with 10 chromosomes, and 100 markers are located on each chromosome.
build_genome(n_chr=10, n_loci=100)
# Simulate two independent traits controlled by 3 and 8 QTLs, respectively.
build_phenome([3, 8])
# Initialize founders
n_sires = 3
n_dams = 20
sires = Founders(n_sires)
dams = Founders(n_dams)
# Define parameters
args = Dict(# mating
:nA => 3,
:nB_per_A => 5,
:n_per_mate => 2,
:ratio_malefemale => 1.0,
# selection
:h2 => [.8, .5],
:weights => [.6, .4],
# breeding
:n_gens => 5,
:n_select_A => 3,
:n_select_B => 20)
# Breeding program
sires_new, dams_new = breed(sires, dams; args...)
# Inspect the results
summary(sires + dams)
summary(sires_new + dams_new)
Bibliography
Chen, C.J., D. Garrick, R. Fernando, E. Karaman, C. Stricker, M. Keehan, and H. Cheng. 2022. XSim version 2: simulation of modern breeding programs. G3 Genes|Genomes|Genetics 12:jkac032. doi:10.1093/g3journal/jkac032.
BibTeX
@article{chen_xsim_2022,
title = {{XSim} version 2: simulation of modern breeding programs},
volume = {12},
issn = {2160-1836},
url = {<https://doi.org/10.1093/g3journal/jkac032>},
doi = {10.1093/g3journal/jkac032},
number = {4},
urldate = {2022-05-26},
journal = {G3 Genes{\textbar}Genomes{\textbar}Genetics},
author = {Chen, Chunpeng James and Garrick, Dorian and Fernando, Rohan and Karaman, Emre and Stricker, Chris and Keehan, Michael and Cheng, Hao},
month = apr,
year = {2022},
}
Old users may install the old version of XSim as using Pkg; Pkg.add(name="XSim", version="0.5")
Cheng H, Garrick D, and Fernando R (2015) XSim: Simulation of descendants from ancestors with sequence data. G3: Genes-Genomes-Genetics, 5(7):1415-1417.
using Pkg; Pkg.add("XSim")
Author: Reworkhow
Source Code: https://github.com/reworkhow/XSim.jl
License: GPL-2.0 license
1620466520
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1620629020
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
1617959340
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company
1660247400
XSim is a fast and user-friendly tool to simulate sequence data and complicated pedigree structures
# Load XSim
using XSim
# Simulate genome with 10 chromosomes, and 100 markers are located on each chromosome.
build_genome(n_chr=10, n_loci=100)
# Simulate two independent traits controlled by 3 and 8 QTLs, respectively.
build_phenome([3, 8])
# Initialize founders
n_sires = 3
n_dams = 20
sires = Founders(n_sires)
dams = Founders(n_dams)
# Define parameters
args = Dict(# mating
:nA => 3,
:nB_per_A => 5,
:n_per_mate => 2,
:ratio_malefemale => 1.0,
# selection
:h2 => [.8, .5],
:weights => [.6, .4],
# breeding
:n_gens => 5,
:n_select_A => 3,
:n_select_B => 20)
# Breeding program
sires_new, dams_new = breed(sires, dams; args...)
# Inspect the results
summary(sires + dams)
summary(sires_new + dams_new)
Bibliography
Chen, C.J., D. Garrick, R. Fernando, E. Karaman, C. Stricker, M. Keehan, and H. Cheng. 2022. XSim version 2: simulation of modern breeding programs. G3 Genes|Genomes|Genetics 12:jkac032. doi:10.1093/g3journal/jkac032.
BibTeX
@article{chen_xsim_2022,
title = {{XSim} version 2: simulation of modern breeding programs},
volume = {12},
issn = {2160-1836},
url = {<https://doi.org/10.1093/g3journal/jkac032>},
doi = {10.1093/g3journal/jkac032},
number = {4},
urldate = {2022-05-26},
journal = {G3 Genes{\textbar}Genomes{\textbar}Genetics},
author = {Chen, Chunpeng James and Garrick, Dorian and Fernando, Rohan and Karaman, Emre and Stricker, Chris and Keehan, Michael and Cheng, Hao},
month = apr,
year = {2022},
}
Old users may install the old version of XSim as using Pkg; Pkg.add(name="XSim", version="0.5")
Cheng H, Garrick D, and Fernando R (2015) XSim: Simulation of descendants from ancestors with sequence data. G3: Genes-Genomes-Genetics, 5(7):1415-1417.
using Pkg; Pkg.add("XSim")
Author: Reworkhow
Source Code: https://github.com/reworkhow/XSim.jl
License: GPL-2.0 license
1618039260
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt