Supplementary Files for thesis titled "Visual-analytics-driven bioinformatics methods for the analysis of biomolecular data"

doi:10.26190/unsworks/24500

Supplementary Files for thesis titled "Visual-analytics-driven bioinformatics methods for the analysis of biomolecular data"

Download files

Access & Terms of Use

open access
Copyright: UNSW

GPL

Abstract

This data set provides Supplementary files referenced in the thesis titled "Visual-analytics-driven bioinformatics methods for the analysis of biomolecular data". In particular, this data set consists of the following files (Details are also provided in an included README.txt file): Description of files in this data set: 1. Supplementary File 4.1. Supplementary File 4.1 - URL and variants schema.pdf. Graphical Backus-Naur schema of the variant syntax recognized by Aquaria. 2. Supplementary File 4.2. Supplementary File 4.2 - Schema.json. Aquaria feature set schema. This schema can be utilized in conjunction with user-specified JSON files for validation in online tools such as https://www.jsonschemavalidator.net/ (see Section 4.5.5). 3. Supplementary File 6.1. Supplementary File 6.1 - Illumina and complete genome IDs.xlsx. NCBI SRA accession identifiers of 673 Illumina (short-read length) and 673 PacBio sequenced genomes (long-read length), corresponding to 673 isolates sequenced using two technologies. 4. Supplementary File 6.2. Supplementary File 6.2 - Distribution of IS in complete genomes.xlsx. ISs in complete genomes. 5. Supplementary File 6.3. Supplementary File 6.3 - QUAST analysis of assemblies.xlsx. Summary of SPAdes and SKESA assembly quality statistics, generated using QUAST. 6. Supplementary File 6.4. Supplementary File 6.4 - WiIS performance metrics.xlsx. WiIS performance metrics for each genome. 7. Supplementary File 6.5. Supplementary File 6.5 - Correlation of performance metrics and assembly statistics.xlsx. Correlation of WiIS performance metrics with SPAdes and SKESA assembly quality statistics. 8. Supplementary File 6.6. Supplementary File 6.6 - IS insertions found by all tools.xlsx. IS insertions found by all tools for each of the 673 short-read sequenced genome. 9. Supplementary File 6.7. Supplementary File 6.7 - IS insertions found by all tools (20 base pair distance threshold).xlsx. IS insertions found by all tools, with a buffer length of 20 base pairs, for each of the 673 short-read sequenced genome. 10. Supplementary File 6.8. Supplementary File 6.8 - WiIS SPAdes IS insertions found with respect.xlsx. Summary of IS insertions found by WiIS (SPAdes) with respect to Tohama I (including the counts of insertions identified by WiIS, but not in Tohama I). 11. Supplementary File 6.9. Wiis.zip. WiIS code.

Publication Year

2022

Resource Type

Dataset

Keyword(s)

Schema
Bordetella pertussis
Performance metrics
Tool comparisons
Insertion sequences

UNSW Faculty

Files

Supplementary File 4.1 - URL and variants schema.pdf	201.83 KB	Adobe Portable Document Format
Supplementary File 4.2 - Schema.json	1.75 KB	Unknown data format
Supplementary File 4.2 - Schema.json.zip	1.29 KB	ZIP archive
Supplementary File 6.1 - Illumina and complete genome IDs.xlsx	56.68 KB	Microsoft Excel XML
Supplementary File 6.2 - Distribution of IS in complete genomes.xlsx	41.37 KB	Microsoft Excel XML

Related publication(s)

Visual-analytics-driven bioinformatics methods for the analysis of biomolecular data

(2022) Kaur, Sandeep

Thesis

Advances in molecular biology data collection, leading to the accumulation of large amounts of diverse data, call for novel computational approaches to enable their effective analysis. This thesis explored the application of visual-analytics-driven bioinformatics approaches to four biomolecular data-driven challenges. For analysing time-series omic and multiomic data, a novel method, Minardo-Model, was developed. Minardo-Model can identify key events (e.g. phosphorylation) from such time-series data and temporally order them. To visualise the inferred order of events, two novel visualisation approaches, event maps and event sparklines, were developed. Minardo-Model was tested using two time-series datasets and in both cases, the event orderings derived by this method correlated with prior knowledge. To streamline the use of experimental 3D protein structures for analysing sequence variants, a novel method was developed and integrated into Aquaria. For variants specified in the HGVS notation, the method identifies and displays a best matching structure. Additionally, for each variant specified, all structures spanning the variant, and containing the exact variant (missense only), along with sequence features retrieved from external resources, are summarised. The developed approach was used to analyse variants in human ACE2, and SARS-CoV-2 spike, revealing novel insights. For pathogenic bacterial isolates characterised using multilevel genome typing (MGT), the MGTdb web service was developed. MGTdb, enables upload of isolates as sequence reads or extracted alleles, which are processed and assigned the MGT-identifiers. The features of MGTdb, such as interactive visualisation tools, data download and export to external software, enable epidemiological exploration in the context of the local or global database of isolates. The usability of MGTdb was successfully demonstrated through three case studies. For identifying insertion sequences (IS) from short-read sequencing data, a novel method, WiIS, was developed. WiIS was tested on Bordetella pertussis isolates, for which both short-read (test data) and long-read sequences (ground truth) were available - WiIS was found to have high precision and recall. It also outperformed other published tools in identifying IS in B. pertussis genomes. The novel bioinformatics methods developed in this thesis enable novel analysis of a wide variety of data thus providing insight into various biomolecular processes.

View full record Show statistics

Library