Sequence data analysis/machine learning workstation (general-purpose configuration) | TEGAKARI, an information dissemination media for research and development

Case No. PC-25000268A customer who visited our website inquired about a workstation that could handle both nanopore sequencing data analysis and machine learning.
We plan to perform epigenetic analysis, de novo assembly, RNA-Seq analysis, metagenomic analysis, and other processing, and will use the following software:

General-purpose analysis and ML infrastructure: Samtools, XGBoost, LightGBM
Genome assembly and evaluation: SPAdes, BUSCO, Prodigal, FastANI, GTDB-Tk
Nanopore base calling: Guppy, Dorado, Remora, DeepMod2

Additionally, the OS would be Ubuntu, and we would like a proposal within a budget of around 350 million yen.

Based on the information you provided, we proposed the following configuration.

CPU	Intel Xeon W7-3565X 2.50GHz (up to 3.0GHz at TB4.8) 32C/64T
memory	Total 512GB DDR5 5600 REG ECC 64GB x 8
Storage 1	4TB SSD M.2 NVMe Gen5
Storage 2	24TB HDD S-ATA
Video	NVIDIA RTX PRO5000 48GB
network	on board (1GbE x1 /10GbE x1)
Housing + power supply	Mid-tower chassis 1500W 80PLUS PLATINUM
OS	Ubuntu 24.04

A balanced and strengthened configuration with machine learning in mind

This configuration is "Case No. PC-25000268This proposal emphasizes "versatility that can handle machine learning while upgrading the system." The combination of a Xeon W processor and 512GB of memory achieves both the throughput required for sequence analysis and the computing performance required for machine learning processing.

The optimized configuration based on the system requirements isCase No.PC-TE1J254011 .

About CPU

The Intel Xeon W7-3565X (32 cores) has a high clock speed and excellent single-task performance, making it an ideal choice for both genome analysis and machine learning processing, where the load characteristics vary depending on the tool. It is also suited to software environments where part of the calculation processing depends on CPU performance.

GPU selection

This configuration is equipped with a GPU in anticipation of utilizing machine learning.
While the workflow for sequence analysis involves a lot of CPU-driven processing, the presence of a GPU is extremely effective in research that incorporates machine learning, so we decided to add a GPU in order to broaden the scope of our research.

Dorado is an example of a high-end GPU that assumes double-precision calculations when using GPU acceleration.
Therefore, rather than simply configuring a system that meets Dorado's high-precision GPU requirements, we decided to use the RTX series as a realistic and effective option that can flexibly accommodate a wide range of research applications, particularly machine learning.

The RTX series is well-suited for deep learning frameworks and is easily adaptable to a wide range of research tasks. Its configuration allows for flexible adaptation to future expansion and workflow evolution.

Reference: Dorado GPU requirements

Reference: Benchmarking the Oxford Nanopore Technologies basecallers on AWS

Scalability

The large mid-tower chassis allows for flexible support for future storage expansion and the addition of GPUs, making it easy to expand the scope of use to suit changing research needs.

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.

Keyword

・What is Samtools?
Samtools is a set of standard tools widely used for processing and analyzing next-generation sequencing (NGS) data. It enables high-speed processing of alignment data such as BAM and CRAM, including sorting, indexing, filtering, and visualization, and is widely used as the foundation of genome analysis workflows.

Reference: GitHub – samtools/samtools: Tools (written in C using htslib) for manipulating next-generation sequencing data

・What is XGBoost?
XGBoost is a machine learning framework that implements the gradient boosting decision tree (GBDT) algorithm with high speed and accuracy. It has a wide range of applications including classification, regression, and ranking, and is also widely used in the bioinformatics field, including genome analysis, disease risk prediction, and gene expression analysis.

https://xgboost.ai/Reference: XGBoost Official Site

・What is LightGBM?
LightGBM is a fast and memory-efficient gradient boosting framework developed by Microsoft. It is suitable for analyzing large amounts of data and is used for SNP analysis, methylation data classification, disease prediction models, and more.

Reference: LightGBM Documentation

What is SPAdes?
SPAdes is a de novo assembler widely used for microbial genome assembly. It supports hybrid assembly integrating multiple platforms, including Illumina, Nanopore, and PacBio, and is capable of constructing high-quality contigs.

Reference: SPAdes – Genome Assembly Toolkit

・What is BUSCO?
BUSCO is a representative tool for assessing the completeness of genome assemblies. It uses a highly conserved set of single-copy genes to objectively assess assembly quality.

References: Release · ezlab / busco · GitLab

・What is Prodigal?
Prodigal is a gene prediction tool specialized for microbial genomes. It is capable of extracting prokaryotic gene regions with high accuracy and also performs well in metagenomic analysis.