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At INFN-T1 we recently acquired some nodes with ARM and RISC-V CPUs to understand the experiment level of readiness on new hardware solutions and to test our production pipelines. After some initial testing, ARM resources entered the standard farm, since the stability both of the nodes and of the software was production quality ready. On the contrary RISC-V solutions are still to be considered only as testbed, since the software is not ready for general production. In this article we will describe all the activities that were necessary to enable users to run on ARM and RISC-V and will give some figures on performance, compared to x86_64 counterpart. In the end we will try to describe our point of view for the possible mass adoption of this architecture in Tier1 data centers.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment. Several bottlenecks exist in metabolomics data analysis. Here, the authors present MetaboAnalystR 4.0 as a unified workflow for LC-MS untargeted metabolomics. It highlights significant improvements in LC-MS2 spectral processing and functional analysis, providing an end-to-end computational pipeline.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.A protocol for generating chromatin accessibility profiles from a broad variety of cell and tissue types, including a step-by-step workflow for library preparation and guidelines for data processing and downstream analysis.

Average nucleotide identity (ANI) is a category of computational analysis that can be used to define species boundaries of Archaea and Bacteria. Calculating ANI usually involves the fragmentation of genome sequences, followed by nucleotide sequence search, alignment, and identity calculation. The original algorithm to calculate ANI used the BLAST program as its search engine. An improved ANI algorithm, called OrthoANI, was developed to accommodate the concept of orthology. Here, we compared four algorithms to compute ANI, namely ANIb (ANI algorithm using BLAST), ANIm (ANI using MUMmer), OrthoANIb (OrthoANI using BLAST) and OrthoANIu (OrthoANI using USEARCH) using >100,000 pairs of genomes with various genome sizes. By comparing values to the ANIb that is considered a standard, OrthoANIb and OrthoANIu exhibited good correlation in the whole range of ANI values. ANIm showed poor correlation for ANI of <90%. ANIm and OrthoANIu runs faster than ANIb by an order of magnitude. When genomes that are larger than 7 Mbp were analysed, the run-times of ANIm and OrthoANIu were shorter than that of ANIb by 53- and 22-fold, respectively. In conclusion, ANI calculation can be greatly sped up by the OrthoANIu method without losing accuracy. A web-service that can be used to calculate OrthoANIu between a pair of genome sequences is available at http://www.ezbiocloud.net/tools/ani . For large-scale calculation and integration in bioinformatics pipelines, a standalone JAVA program is available for download at http://www.ezbiocloud.net/tools/orthoaniu .

This work focuses on implementation/designing the RISC-V Processor with optimized pipeline throughput, cache hit rate, and dynamic instruction scheduling to enhance the processing speed and energy efficiency. RISC-V extension used to support the tasks in AI, signal processing and cryptography. Design of processor will be implemented by using Verilog/VHDL and simulation tools later it will be tested on FPGA hardware. This project in designing to improve the performance mainly used for high-performance application.

Background Biomedical researchers use alignments produced by BLAST (Basic Local Alignment Search Tool) to categorize their query sequences. Producing such alignments is an essential bioinformatics task that is well suited for the cloud. The cloud can perform many calculations quickly as well as store and access large volumes of data. Bioinformaticians can also use it to collaborate with other researchers, sharing their results, datasets and even their pipelines on a common platform. Results We present ElasticBLAST, a cloud native application to perform BLAST alignments in the cloud. ElasticBLAST can handle anywhere from a few to many thousands of queries and run the searches on thousands of virtual CPUs (if desired), deleting resources when it is done. It uses cloud native tools for orchestration and can request discounted instances, lowering cloud costs for users. It is supported on Amazon Web Services and Google Cloud Platform. It can search BLAST databases that are user provided or from the National Center for Biotechnology Information. Conclusion We show that ElasticBLAST is a useful application that can efficiently perform BLAST searches for the user in the cloud, demonstrating that with two examples. At the same time, it hides much of the complexity of working in the cloud, lowering the threshold to move work to the cloud.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis. Key points MZmine is a program designed to process data from untargeted mass spectrometry (MS) experiments acquired in data-dependent acquisition mode; specifically, collision-induced dissociation and higher-energy collisional dissociation. This protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by instrumental setups: liquid chromatography–(ion mobility spectrometry–)MS, gas chromatography–MS and (ion mobility spectrometry–)MS imaging. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.

Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus—a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies. Cumulus is a cloud-based framework enabling large-scale single-cell and single-nucleus RNA sequencing data analysis.

Metameric object matching is an intrinsic characteristic of trichromatic color measurement where spectrally distinct object reflectances produce identical color signals under a given viewing condition. However, when the viewing conditions change, the previously identical color signals may diverge in a phenomenon known as metamer mismatching. In this paper we review the conceptual foundations, evolving computational methods, and practical applications of Metamer Mismatch Bodies (MMBs), which characterize the range of color signals produced by a metamer set with a change in viewing conditions. We outline advancements in models of metamer mismatching from early statistical estimates and linear programming to modern algorithms capable of computing precise mismatch boundaries without assumptions about reflectance smoothness or transition count. We review the use of MMBs in practice for light source design and evaluation, digital camera sensor design and color appearance modeling. And finally, we present an optimized MATLAB implementation of the Logvinenko et al. five-transition approximation algorithm enabling large-scale spectral analysis and broader integration into imaging pipelines. By consolidating theoretical developments and practical advances, this survey positions MMBs as a foundational tool for understanding and quantifying color variation across changing conditions.