SNIC Science Cloud (SSC): A National-Scale Cloud Infrastructure for Swedish Academia

Toor, Salman; Lindberg, Mathias; Falman, Ingemar; Vallin, Andreas; Mohill, Olof; Freyhult, Pontus; Nilsson, Linus; Agback, Martin; Viklund, Lars; Zazzik, Henric; Spjuth, Ola; Capuccini, Marco; Möller, Joakim; Murtagh, Donal; Hellander, Andreas

doi:10.1109/escience.2017.35

Cited by 29 publications

(18 citation statements)

References 5 publications

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“…As service fees for assembly may be as high as the expenses for data generation, academic researchers can double the number accessions included in a pan-genomics project if they perform sequence assembly on their own. Alternatives on-site computing infrastructures are national computing infrastructures such as CyVerse [43], de.NBI [44] or SNIC [45].…”

Section: Discussionmentioning

confidence: 99%

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Monat

Padmarasu

Lux

et al. 2019

Preprint

160

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Chromosome-scale genome sequence assemblies underpin pan-genomic studies. Recent genome assembly efforts in the large-genome Triticeae crops wheat and barley have relied on the commercial closed-source assembly algorithm DeNovoMagic. We have developed TRITEX, an open-source computational workflow that combines paired-end, mate-pair, 10X Genomics linked-read with chromosome conformation capture sequencing data to construct sequence scaffolds with megabase-scale contiguity ordered into chromosomal pseudomolecules. We evaluated the performance of TRITEX on publicly available sequence data of tetraploid wild emmer and hexaploid bread wheat, and constructed an improved annotated reference genome sequence assembly of the barley cultivar Morex as a community resource.

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Section: Discussionmentioning

confidence: 99%

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Monat

Padmarasu

Lux

et al. 2019

Preprint

160

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“…Cloud resources were provided by SNIC Science Cloud (SSC) [49] UPPMAX region under the projects SNIC 2018/10-5 and SNIC 2019/10-8.…”

Section: Acknowledgementsmentioning

confidence: 99%

Predicting Target Profiles with Confidence as a Service using Docking Scores

Ahmed

Suardi

Arvidsson

et al. 2020

Preprint

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Background Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.

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“…Thanks to Polina Georgiev for providing the images used in the evaluation of Case Study 1. Resources from The Swedish National Infrastructure for Computing (SNIC) [31] were used for Case Study 2.…”

Section: Acknowledgementsmentioning

confidence: 99%

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Blamey

Toor

Dahlö

et al. 2020

Preprint

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This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is online and based on data content rather than a priori metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope.

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SNIC Science Cloud (SSC): A National-Scale Cloud Infrastructure for Swedish Academia

Cited by 29 publications

References 5 publications

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools

Predicting Target Profiles with Confidence as a Service using Docking Scores

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

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