Laura Rodriguez

Proteomics research encompasses a wide array of experimental designs, resulting in diverse datasets varying in structure and properties. This diversity has led to a considerable variety of software solutions for data analysis, each of them using multiple tools with different algorithms for operations like peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. Computational workflows combine these algorithms to facilitate end-to-end analysis, spanning from raw data to detecting differentially regulated proteins. We introduce WOMBAT-P, a versatile platform designed for the automatic benchmarking and comparison of bottom-up label-free proteomics workflows. By standardizing software parameterization and workflow outputs, WOMBAT-P empowers an objective comparison of four commonly utilized data analysis workflows. Furthermore, WOMBAT-P streamlines the processing of public data based on the provided metadata, with an optional specification of 30 parameters. Wombat-P can use Sample and Data Relationship Format for Proteomics (SDRF-Proteomics) as the file input to simply process annotated local or ProteomeXchange deposited datasets. This feature offers a shortcut for data analysis and facilitates comparisons among diverse outputs. Through an examination of experimental ground truth data and a realistic biological dataset, we unveil significant disparities and a low overlap between identified and quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of four workflows (on the same dataset) using a wide range of benchmarking metrics but also provides insights into the capabilities of different software solutions. These metrics support researchers in selecting the most suitable workflow for their specific dataset. The modular architecture of WOMBAT-P promotes extensibility and customization, making it an ideal platform for testing newly developed software tools within a realistic data analysis context.

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Summary: We demonstrate how graph decomposition techniques can be employed for the visualization of hierarchical co‐occurrence patterns between medical data items. Our research is based on Gaifman graphs (a mathematical concept introduced in Logic), on specific variants of this concept, and on existing graph decomposition notions, specifically, graph modules and the clan decomposition of so‐called 2‐structures. The construction of the Gaifman graphs from a dataset is based on co‐occurrence, or lack of it, of items in the dataset. We may select a discretization on the edge labels to aim at one among several Gaifman graph variants. Then, the decomposition of the graph may provide us with visual information about the data co‐occurrences, after which one can proceed to more traditional statistical analysis.

Software-defined networking (SDN) and disaggregation are key technologies to fulfill the requirements of 5G and beyond optical metro networks. Indeed, open networking solutions will reduce vendor lock-in, achieving increased flexibility and programmability of the network. SDN-enabled sliceable bandwidth/bitrate variable transceivers (S-BVTs) are proposed to be adopted in metro networks in order to provide sustainable capacity growth and hyper-scalability, while enabling disaggregation and network flexibility/reconfigurability. We propose a transceiver modular approach following a pay-as-you grow model, which allows the S-BVT to be adapted to the traffic demand and network requirements. In fact, the S-BVT solution can be seen as a set of bandwidth/bitrate variable transceivers (BVTs) that can be pluggable and multi-vendor. Hence, SDN agents have been developed based on an OpenConfig vendor-neutral data model in order to efficiently reconfigure the S-BVTs fostering network agility and adaptability. Specifically, an optimized agent implementation is experimentally validated in order to increase the network efficiency and reduce the setup configuration time of an optical channel. Two slices/BVTs of 50 Gb/s each are successfully enabled and programmed to establish a high capacity flow of 100 Gb/s. The cost/power consumption of the proposed solution, based on direct detection (DD), is analyzed showing significant savings compared to 100 Gb/s coherent (CO) technology.

Space division multiplexing (SDM) super-channels are the only way to guarantee a sustainable scaling of the optical line interface rate. High-capacity terabit interfaces can be deployed by logically associating several optical sub-channels transmitted in parallel in different spatial cores or modes in order to create an (logical) optical channel with the desired interface rate. This paper is focused on the use of spatial modes for SDM super-channels. First, we present a software defined networking (SDN)-controlled disaggregated SDM network architecture that deploys the proposed sliceable-mode transceivers with multiple-input multiple-output equalization. Then, we present the adaptive SDN workflow and heuristics for the provisioning, scaling up/down, and soft-failure restoration of SDM super-channels using spatial modes. Two proofs of concept are deployed in the joint testbed between KDDI Research and CTTC.

We design and implement programmable (SDN-enabled) sliceable bandwidth/bitrate variable transceivers (S-BVTs) based on multicarrier modulation (MCM) with direct detection (DD), tailored for disaggregated metro networks. A first level of disaggregation is assessed by considering an S-BVT architecture composed of a set of white box bandwidth/bit rate variable transceivers (BVTs) from multiple manufaturers/providers. An OpenConfig vendor-neutral model is adopted for the implementation of the SDN agents that configure the S-BVTs according to the network requirements/targets. Additionally, a fully disaggregated metro network is envisioned by considering a fixed/flexi-grid dense wavelength division multiplexing (DWDM) network with white box ROADM/OXC nodes. The impact of the filter narrowing effect is assessed considering different network node architectures based on either flexible wavelength selective switches (WSSes) or arrayed waveguide gratings (AWGs). Thanks to the transceiver inherent modularity, flexibility and bit rate variability, up to 8 network nodes can be traversed ensuring a target capacity of 50 Gb/s per slice at a 4.62 · 10 ^-3 BER.

The authors have recently proposed the usage of modular decompositions of Gaifman graphs as an exploratory data analysis tool. We describe how these techniques allow for a compact, hierarchical visualization of the patterns of cooccurrence between data items, in the context of medical data corresponding to simultaneous diagnostics of patients.

We propose a heuristic and experimentally demonstrate dynamic scaling up/down of SDM super-channels to increase/decrease the capacity by exploiting the spatial modes. We deploy mode-adaptive MIMO equalization and modulation format assignment controlled by SDN.

This paper presents the developed extensions to the NFV MANO framework for the control of SDM/WDM-enabled fronthaul and packet-based backhaul transport networks to deploy NFV network services. We develop a novel hierarchical Transport SDN solution that extends the Transport API.