A customer involved in plant breeding research contacted us to inquire about introducing PCs.
I would like a PC to perform genetic polymorphism analysis on plant genome sequence data.
The budget is around 50 yen, and in the future we are also considering short-read and long-read sequencers.
| CPU | Intel Core Ultra 9 285K 3.70GHz(8C/8T) + 3.20GHz(16C/16T) |
| memory | Total 128GB DDR5 6400 64GB x 2 |
| storage | 2TB SSD M.2 NVMe Gen5 |
| Video | NVIDIA RTX A400 4GB (MiniDisplayPort x4) |
| network | on board(2.5GBase-T x1) Wi-Fi, Bluetooth |
| Housing + power supply | Mid-tower chassis + 1000W 80PLUS PLATINUM |
| OS | Ubuntu 24.04 |
About memory
In whole-genome analysis of plants, the amount of data to be handled becomes extremely large, so the speed of computation is more important than the data itself.Memory capacitybecomes important.
In particular, in processes such as mapping, assembly, and genetic polymorphism analysis, it is necessary to load and store large amounts of sequence data at once while performing calculations.
Therefore, by ensuring sufficient memory, it becomes possible to analyze larger amounts of data and improve processing efficiency.
This configuration comes with 128GB of memory, and there is an available slot for expansion up to 256GB.
This will also enable future long-read sequence analysis.
About storage
Anticipating that the input data and intermediate files would be very large, we selected a 2TB NVMe Gen5 SSD.
High-speed read/write performanceReduce I/O bottlenecksThis prevents interruptions in processing and provides a stable analysis environment.
The configuration allows for the addition of external drives or HDDs as needed, allowing for flexible data storage and management.
If you are planning to add more storage yourself, we will deliver the unit with an internal layout that takes into consideration ease of installation.
If you are considering changing or adding parts, please consult us in advance so that we can propose and respond to the most appropriate configuration.
About GPU
In recent years, with the emergence of variant analysis using deep learning and GPU-compatible assembly tools,Increasing opportunities for GPU utilizationdoing.
However, since processes such as mapping and SNP analysis are CPU-based, it is important to select a configuration that suits the analysis content and application.
In this configurationGPU-independent analysisSince this will be the main focus, we selected the NVIDIA RTX A400 for drawing processing and basic graphics output.
If you are planning to perform analysis that actively utilizes GPUs, we can also consult with you about considering higher performance GPUs and configuration designs with an eye toward expansion.
For those who are active in these fields
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Tegara's custom-made PC production service not only caters to initial use, but also supports system expansion in anticipation of future expansion of research scale.
We not only propose configurations that meet various software requirements, but also accept consultations regarding the construction of an entire research environment.
Please feel free to contact us and we will provide the best solution to suit your needs.
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