Tag Archives: Bioinformatics

iLearn on R@CMon

An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data : the impact of making machine learning good practice readily available to the community.

Associate Professor Jiangning Song is a long-standing user of the Monash Research Cloud (R@CMon). He is the lead of the Song Lab within the Monash Biomedicine Discovery Institute. Jiangning’s journey began with the deployment of the Protease Specificity Prediction Server – PROSPER app in 2014. Since then the lab has launched more than 30 bioinformatics web services, all of which are made available to research communities worldwide.

Their latest contribution, iLearn, addresses key obstacles to the adoption of machine learning applied to sequencing data. Well-annotated DNA, RNA and protein sequence data is increasingly accessible to all biological researchers. However, at the scale of this data it is challenging if not impossible for an individual to manually investigate. Similarly, another obstacle to broad scale access is that investigation and validation through wet laboratory experiments is time consuming and expensive. Hence when presented appropriately, machine learning can play an import role making higher-level biological data accessible to many researchers in the biosciences.

Many of the previous works and tools only focus on a specific step within a data-processing pipeline. The user is then responsible for chaining these tools together, which in most cases is challenging due to incompatibilities between tools and data formats. iLearn has been designed to address these limitations, using common patterns informed by the lab and its collaborators.

An emerging breakdown of the pipeline steps is:

Feature extraction
Clustering
Normalization
Selection
Dimensionality reduction
Predictor extraction
Performance evaluation
Ensemble training
Results visualisation

iLearn packages these steps for use in two ways. Users can use iLearn through an online environment (web server) or as a stand-alone python toolkit. Whether your interest is in DNA, RNA or protein analysis, iLearn provides a common workflow pattern for all three cases. Users input their sequence data (normally in FASTA format), and then enters various descriptors and parameters for the analysis.

The results page shows the various output, once again informed by the Lab’s good-practices. They can be downloaded from the web server in various formats (e.g CSV, TSV). High quality diagrams and visualisations are also generated by iLearn within the web server:

Since iLearn’s release, more than 5K unique users have used the web server worldwide. The user community and resultant impact continues to grow, with 60 citations since the tool’s seminal publication.

iLearn has been used as an efficient and powerful complementary tool for orchestrating machine-learning-based modelling which in turn improves the speed in biomedical discoveries through genomics and data analysis. As new descriptors get developed and optimised, iLearn aims to incorporate these into future releases to further improve its performance with the R@CMon team providing support to tackle the potential increase in computational and storage complexities.

This article can also be found, published created commons here ¹.

GlycoMine on R@CMon

Glycosylation is an ubiquitous type of protein post-translational modification (PTM) in eukaryotic cells, which plays vital roles in various biological processes such as cellular communication, ligand recognition and subcellular recognition. It is estimated that greater than 50% of the entire human proteome is glycosylated. However, it is still a significant challenge to identify glycosylation sites, which requires expensive and laborious experimental research. Thus, bioinformatics approaches that can predict the glycan occupancy at specific sequons in protein sequences would be useful for understanding and utilising this very important PTM.

Predicted N-linked glycosylation sites from two case-study proteins using GlycoMine-Struct

Dr. Jiangning Song from the Department of Biochemistry and Molecular Biology at Monash University and his collaborators have designed and developed a bioinformatics tool – GlycoMine-Struct for predicting glycosylation sites. GlycoMine-Struct is a comprehensive tool for the systematic in-silico identification of N-linked and O-linked glycosylation sites in the human proteome. Through R@CMon, a dedicated cloud project with computational and storage resources has been provisioned to develop and host the GlycoMine-Struct tool. The flexible and scalable R@CMon-powered development environment enabled rapid prototyping, testing and re-deployment of the tool.

GlycoMine-Struct Main Page (http://glycomine.erc.monash.edu/Lab/GlycoMine_Struct/index.jsp#Introduction)

GlycoMine-Struct is now a publicly accessible web service, available to the wider research community. Users can now easily submit protein structure input files in PDB (Protein Data Bank) format to perform sites prediction on GlycoMine-Struct. Since it went public, GlycoMine-Struct has been accessed and used by thousands of local and international users, and still growing. A scientific reports paper has been published, highlighting the collaborative work done to develop GlycoMine-Struct, as an essential bioinformatics tool for improving the prediction of human glycosylation sites. The R@CMon team is actively supporting the GlycoMine-Struct project as it continues to serve the research community and develop performance improvements.

XCMSplus Metabolomics Analysis on R@CMon

At the start of 2017, the R@CMon team had its first user consultation with Dr. Sri Ramarathinam, a research fellow from the Immunproteomics Laboratory (Purcell Laboratory) at the School of Biomedical Sciences in Monash University. Sri and his group at the lab studies metabolomics compounds in various samples by conducting a “search” and “identification” process using a pipeline of analysis and visualisation tools. The lab has acquired the license to use the commercial XCMSPlus metabolomics platform from SCIEX on their workflow. XCMSPlus provides a powerful solution for analysis of untargeted metabolomics data in a stand-alone configuration, which will greatly increase the lab’s capacity to analyse more samples, with faster and easeful results generation and interpretation.

XCMSPlus main login Page, entry point of the complete metabolomics platform

During the first engagement meeting with Sri and the lab, it’s been highlighted that a specialised hosting platform (with appropriate storage and computational capacity) would be required for XCMSPlus. XCMSPlus is distributed as stand-alone appliance (personal cloud) from the vendor. As an appliance, XCMSPlus has been optimised and packaged to be deployed on a single, multi-core and high-memory machine. An added minor complication is that this appliance was distributed in VMWare’s appliance format, which need to be translated into an OpenStack-friendly format. The R@CMon team provided the hosting platform required for XCMSPlus through the Monash node of the Nectar Research Cloud.

Analysis results and visualisation in XCMSPlus

A dedicated Nectar project has been provisioned for the lab, which is now being used for hosting XCMSPlus. This project also has enough capacity for future expansion and new analysis platform deployments. The now R@CMon-hosted (and supported) XCMSPlus platform for the Immunproteomics Laboratory is the first custom XCMSPlus deployment in Australia. Due to being the first in Australia, there were some early minor issues encountered during its first test runs. These technical issues were eventually sorted out due to collaborative troubleshooting efforts from the R@CM team, the lab and the vendor. And after several months of usage, hundred of jobs submitted and processed by XCMSPlus, and counting, the lab is continuing to fully integrate it as part of their analysis workflow. The R@CMon team is actively engaging with the lab for supporting its adaption of XCMSPlus and planning for future analysis workflow expansions.

Disruptive change in the clinical treatment of pancreatic cancer

Professor Jenkins’ research focuses on pancreatic cancer, an inflammation-associated cancer and the fourth most common cause of cancer death worldwide, with an extremely low 5% five-year survival rate. Typically studies look at gene expression patterns between normal pancreas and cancerous pancreas in order to identify unique signatures, which can be indicative of sensitivity or resistance to specific chemotherapeutic treatments.

“Using next generation gene sequencing, involving big instruments, big data and big computing – allows near-term disruptive change in the clinical treatment of pancreatic cancer.” Prof. Jenkins, Monash Health..

To date, gene expression studies have largely focused on samples taken from open surgical biopsy; a procedure known to be very invasive and only possible in 20% of pancreatic cancers. Prof Jenkins’ group, in collaboration with Dr Daniel Croagh from the Department of Upper Gastrointestinal and Hepatobiliary Surgery at Monash Medical Centre, recently trialled an alternative less invasive process available to nearly all pancreatic cancer patients known as endoscopic ultrasound-guided fine-needle aspirate (EUS-FNA) which uses a thin, hollow needle to collect the samples of cells from which genetic material can be extracted and analysed. The challenge then becomes to ensure gene sequencing from EUS-FNA samples is comparable to open surgical biopsy such that established analysis and treatment can be used.

Twenty-four EUS-FNA-derived genetic samples from normal and cancerous pancreas were sequenced at the MHTP Medical Genomics Facility producing a total amount of 40Gb of raw data. Those data were securely transferred onto R@CMon by the Monash Bioinformatics Platform for processing, statistical analysis and computational exploration using state-of-the-art Bioinformatics methods.

super_computer

Results thus far from this study show that data from EUS-FNA-derived samples were of high quality and also allowed the identification of gene expression signatures between normal and cancerous pancreas. Professor Jenkins’ group is now confident that EUS-FNA-derived material not only has the potential to capture nearly all of pancreatic cancer patients (compared to ~20% by surgery), but to also improve patient management and their treatment in the clinic.

“The current clinical genomics research space requires specialized high performance computational and storage infrastructure to support the processing and long term storage of those so-called “big data”. Thus R@CMon plays a major role in the discovery and development of new therapies and the improvement of Human health care in general.” Roxane Legaie, Senior Bioinformatician, Monash Bioinformatics Platform

The Ramialison Group Analysis Workflow on R@CMon