Monitoring the conformance of planning decisions to urban land use policies using Information Extraction and GeoVisualisation

In this thesis two existing computer science techniques are used to solve a specific problem in the field of ‘spatial planning’. The problem to be addressed is monitoring the conformance of planning decisions to urban land use policies. ‘Monitoring conformance’ refers to adherence to development plans and must be distinguished from monitoring performance, which looks at whether or not the plan met its objectives. The two computing techniques applied to the problem are Information Extraction (IE) and GeoVisualisation (GV).IE is an approach to the automated processing of text. This thesis demonstrates that the restricted subset of language used in the short texts present in planning applications makes them ideally suited to IE methods.GV is an approach to the interactive analysis of geographical data. Its use was motivated by two factors. Firstly, it is necessary to avoid the assumption of a simple relationship between policy and implementation – many different policies may apply to a particular decision. These may be weighted differently and are open to interpretation. Hence, statistical conclusions, such as ‘there is 80% conformance to policy’, are never drawn. Instead the visualisation leaves the interpretation of the results open to the user. It is through the details-on-demand functionality of visualisation tools that this link to the user’s own background knowledge is made. Secondly, the prototype user interface developed exemplifies the use of GV to explore geo-temporal patterns in the data. This was motivated by the knowledge that policies change over time.Evaluation work is conducted which shows that policy-makers can see reflections of the conformance of decision making to urban land use policies in the GV tool. The computational techniques used have been brought together and applied to the domain in a novel way, which assists in addressing the problem identified. A number of more theoretical questions are also considered along the way

ComplexViewer: visualization of curated macromolecular complexes.

SUMMARY: Proteins frequently function as parts of complexes, assemblages of multiple proteins and other biomolecules, yet network visualizations usually only show proteins as parts of binary interactions. ComplexViewer visualizes interactions with more than two participants and thereby avoids the need to first expand these into multiple binary interactions. Furthermore, if binding regions between molecules are known then these can be displayed in the context of the larger complex. AVAILABILITY AND IMPLEMENTATION: freely available under Apache version 2 license; EMBL-EBI Complex Portal: http://www.ebi.ac.uk/complexportal; Source code: https://github.com/MICommunity/ComplexViewer; Package: https://www.npmjs.com/package/complexviewer; http://biojs.io/d/complexviewer. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js. CONTACT: [email protected] or [email protected]

ComplexViewer: visualization of curated macromolecular complexes.

SUMMARY: Proteins frequently function as parts of complexes, assemblages of multiple proteins and other biomolecules, yet network visualizations usually only show proteins as parts of binary interactions. ComplexViewer visualizes interactions with more than two participants and thereby avoids the need to first expand these into multiple binary interactions. Furthermore, if binding regions between molecules are known then these can be displayed in the context of the larger complex. AVAILABILITY AND IMPLEMENTATION: freely available under Apache version 2 license; EMBL-EBI Complex Portal: http://www.ebi.ac.uk/complexportal; Source code: https://github.com/MICommunity/ComplexViewer; Package: https://www.npmjs.com/package/complexviewer; http://biojs.io/d/complexviewer. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js. CONTACT: [email protected] or [email protected]

mzIdentML 1.3.0 – Essential progress on the support of crosslinking and other identifications based on multiple spectra

ABSTRACT The mzIdentML data format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra has been introduced. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds

Complex Portal 2022:New curation frontiers

International audienceThe Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the ‘Support’ link