Author Archives: afolarinbrc

CompBio-Docker Symposium 2015

Bio In Docker Conference

Event Page [Bio In Docker]

Kings College London and the Biomedical Research Centre will be running a 2 day Docker in Bioinformatics event towards the end of summer 2015.

We are opening a call for speakers using Docker in bioinformatics to come and talk about their work.

Docker is now establishing itself as the de facto solution for containerization across a wide range of domains. The advantages are attractive, from reproducible research to simplifying deployment of complex code. Several bioinformatics groups are now utilizing this for various purposes, we would like to bring together some notable cases to discuss how advantage of this new technology can best be achieved.

At KCL, our group are presently using Docker to encapsulate our Next Generation Sequencing pipeline tools, our aim is to provide up-to-date containers for the most commonly used tools, benchmarking data and provide a framework to string these containerized tools into pipelines which can easily be deployed anywhere. The project is called NGSeasy

We propose the 2 day event to include:

  • A day of talks from selected speakers
    • Feature talks
    • Lightning talks
  • We would also like to identify where common goals exist in the bioinformatics arena where efforts in containerized solutions could be aligned by establishing a community of Docker users and resources (with a similar function to that of Bioconductor for R). This could include:
    • Communal repositories
    • Documentation and tutorials
    • Forums
  • Running a mini-hackday to introduce, demonstrate, and invite participation using Docker on some interesting and well scoped problems.

Post Doctoral Researcher in the application of Digital Technology

Post Doctoral Researcher in the application of Digital Technology

King’s College London

We are looking for an experienced Post Doctoral Researcher in the application of Digital Technology to Mental Health.

In the digital era we generate more data every 2 days than we did in the time up until 2003. This big data is being used by advertising, media, retail, finance and travel with medicine and healthcare lagging behind. The data held in electronic patient records and generated through modern digital devices are examples of the big biomedical data that we could use to develop more targeted treatment strategies. So, the challenge in e- and m- health is no longer data generation, the problem has shifted to data handling and the separation of signal from the noise to produce clear indicators of whether a clinical intervention is necessary. This is especially an issue in mental health as we work with less clear signals.

The exciting opportunity is now to use new technology to support more sophisticated models which might detect signals early enough to provide opportunities for effective interventions that keep people well.

This post will be based in our established BRC informatics group and work with the Patient and Carer Theme to develop shared informatics tools and infrastructure to enable data harmonization and analytics for use in discovering the personal signatures for needs for care using machine learning for example.

The role will contribute to the growing initiatives within the BRC clinical informatics, bioinformatics and biostatistics groups ( whose others areas of focus include integrating genomics with electronic hospital patient records and cloud based patient owned records such as MyHealthLocker.

The post-holder will have an established track record in programming on the Linux OS, and will ideally also have quantitative research skills and/or previous experience of working with digital health data although this is not essential.

The post is funded for 18 months and the salary is grade 6 on King’s salary scale, currently £31,644 -£37,756 plus London allowance of £2323 per annum.

For an informal chat pleas contact Dr Richard Dobson,; Prof Til Wykes,

To apply, please go to

Artificial Intelligence in Mental Health

NIHR Biomedical Research Centre for Mental Health, and NIHR Biomedical Research Unit for Dementia

Institute of Psychiatry – Institute of Psychiatry

Location: London
Salary: Not specified
Hours: Full Time
Contract: Contract / Temporary
Placed on: 15th May 2014
Closes: 19th June 2014

King’s College London

To start: October 2014

We are offering a PhD studentship to applicants with background in Computer Science, particularly Artificial Intelligence or Multi-agent systems. The award is funded by the National Institute for Health Research (NIHR) through the Biomedical Research Centre for Mental Health (BRC-MH) and Biomedical Research Unit for Dementia (BRU-D) at South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry, King’s College London (KCL).

The BRC-MH and BRU-D are pioneering multidisciplinary translational research and experimental medicine in the areas of mental health and neuroscience. They bring together researchers, clinicians and allied health professionals from the UK’s largest NHS mental health service provider (SLaM), and the Institute of Psychiatry (IOP), the largest academic community in Europe dedicated to the study, treatment and prevention of mental health problems and neurodegenerative disease. The IOP offers excellent opportunities for research training in basic and clinical science across the mental health spectrum on one campus, with further strong links with our BRC partner at Guy’s, King’s and St Thomas’ hospitals.

The aim of the project is to evaluate the use of multi-agent computer systems in guiding the clinical decision making process for improved patient care. The project involves designing and implementing a fully workable multi-agent environment responsible for complementing the decision making process. The proposed system will actively monitor entries made to the patient electronic health records through the Electronic Patient Journey System (EPJS) in the aim of anticipating and minimising the occurrence of adverse events, providing evidence-based clinical advice, enforcing adherence to clinical guidelines and capturing and reporting data entry mistakes and clinical errors. The outcome of the project is an active monitoring and guiding system to be incorporated into EPJS, which results less inconsistencies and enforces prompt response to critical events.

How to apply

Click here (  for entry requirements, award details and eligibility, further information about the IOP, BRC-MH and BRU-D, and how to apply.

Alzheimers Disease Big Data DREAM Challenge

Ranit Schmelzer. USAgainstAlzheimer’s. Tel: 202-538-1065. Email:
Thea Norman. Sage Bionetworks. Tel: 206-667-3192. Email:

Washington, DC – The Global CEO Initiative on Alzheimer’s Disease (CEOi), Sage Bionetworks and DREAM Project, today launched the Alzheimer’s Disease Big Data DREAM Challenge #1 in an effort to advance diagnostic innovation and identify new Alzheimer’s disease biomarkers through the use of open source data.

The goal of the Challenge (AD#1) is to apply an open science approach to rapidly identify accurate predictive Alzheimer’s disease biomarkers that can be used by the scientific, industry and regulatory communities to improve Alzheimer’s diagnosis and treatment.  AD#1 will be the first in a series of Alzheimer’s Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with Alzheimer’s.

“Alzheimer’s is more costly to society than cancer, yet there is currently no cure, treatment, or means of prevention” said George Vradenburg, Convener of CEOi and Chairman of USAgainstAlzheimer’s.  “This unprecedented and innovative challenge will showcase the use of open science using 21st century tools, leading to a potential breakthrough for the Alzheimer’s research community.”

The AD#1 Challenge is hosted on Synapse, Sage Bionetworks’ open computational platform, an integrated knowledge environment where data (e.g. human sequence and image data) and models (e.g. prediction and the underlying model source code) can be shared and worked on collaboratively by teams of teams.  The Challenge will be objectively judged against data that has been hidden from participants.  Information about the three AD#1 Challenge questions and the scientific rationale can be found here.

“This challenge will showcase the power of open science in breaking down barriers that slow innovation in the race to cure Alzheimer’s,” said Stephen Friend, President and Co-Founder of Sage Bionetworks.  “Through this series of big challenges, we hope to move closer to solving this intractable problem of Alzheimer’s.”

The open source data from Alzheimer’s patients is provided by the North American Alzheimer’s Disease Neuroimaging Initiative (ADNI), Rush University Medical Center, and the United Kingdom’s AddNeuroMed Study, and will include results from imaging, clinical, whole genome sequencing, and multiple cognitive tests that were conducted on a cohort of individuals who have aged normally, suffer from mild-cognitive impairment or have Alzheimer’s disease.  More than two hundred bioinformatics experts from around the world have already signed up to participate in the Challenge.

While there has been huge growth in scientific data due to declining costs and advances in technology, there remains very little crowd sourcing of findings among researchers.  In recent years, however, pharmaceutical companies have shown an increased willingness to share pre-competitive data, as research and development has declined.  This development has occurred alongside recent efforts by regulatory agencies to encourage data standardization, disclosure, and sharing.

More than 40 million people globally suffer from Alzheimer’s disease or dementia.  Today, the global cost of caring for people with Alzheimer’s is more than 1% of global economic output, or $600 billion annually.  In coming years, as more and more baby boomers reach the age of risk for the disease, those numbers are projected to skyrocket without a treatment to slow the progression of the disease.

Through its diverse partnerships, the CEOi is seeking to work closely with governments and global institutions to advance meaningful reforms to the Alzheimer’s drug marketplace.  The CEOi members include AC Immune, Bank of America, Banner Health, General Electric, Home Instead, Janssen, Lilly, Merck, Nestle Health Science, Pfizer, Sanofi, and Takeda.


Global CEO Initiative on Alzheimer’s Disease (CEOi) is an organization of private-sector leaders who have joined together to provide business leadership in the fight against Alzheimer’s. The CEO Initiative seeks to partner with public leaders to transform the disease from a social, health, and economic crisis into an opportunity for healthy aging and innovation in research and care. The CEO Initiative believes that, during this era of aging populations, it will take visionary, coordinated, goal-oriented leadership of public and private leaders working together to solve our greatest challenges.  Learn more at:

Sage Bionetworks is a nonprofit biomedical research organization, founded in 2009, with a vision to promote innovations in personalized medicine by enabling a community-based approach to scientific inquiries and discoveries.  Sage Bionetworks strives to activate patients and to incentivize scientists, funders and researchers to work in fundamentally new ways in order to shape research, accelerate access to knowledge and transform human health.  It is located on the campus of the Fred Hutchinson Cancer Research Center in Seattle, Washington and is supported through a portfolio of philanthropic donations, competitive research grants, and commercial partnerships. More information is available at



The goal of the Alzheimer’s Disease Big Data DREAM Challenge #1 (AD#1) is to apply an open science approach to rapidly identify accurate predictive AD biomarkers that can be used by the scientific, industrial and regulatory communities to improve AD diagnosis and treatment. AD#1 will be the first in a series of AD Data Challenges to leverage genetics and brain imaging in combination with cognitive assessments, biomarkers and demographic information from cohorts ranging from cognitively normal to mild cognitively impaired to individuals with AD.

We invite you to participate in this Challenge by considering any of the following questions:

Subchallenge 1: Predict the change in cognitive scores 24 months after initial assessment.
Scientific Rationale: Answers to this question will help predict cognitive trajectory and potentially provide new approaches for early diagnosis of AD. This earlier identification would allow for more efficient selection of samples for clinical trials and possibilities for earlier disease treatment.


Subchallenge 2: Predict the set of cognitively normal individuals whose biomarkers are suggestive of amyloid perturbation.
Scientific Rationale: Answers to this question will help us understand how some people maintain normal cognitive function in the presence of amyloid pathology. The biological basis of this resilience to pathology will provide important insights into the development of prevention and therapy.


Subchallenge 3: Classify individuals into diagnostic groups using MR imaging.
Scientific Rationale: If a single MR image could be used to differentiate AD patients from people with mild cognitive impairment or from healthy individuals, research can focus on the specific anatomical structures that are different between the groups. Currently, MRI data are acquired routinely in hospitals: thus a winning algorithm could potentially be retrospectively applied to existing archives of clinical data as well as to future scans without requiring additional resources or expertise.

For questions about the Challenge design please contact the Challenge Organizers through the Challenge Forum

Exome Chip Rare Caller Pipeline

A pipeline for running Zcall and Opticall on Illumina Exome Chip Datasets. This workflow will handle all the file parsing and run both callers and compare the output bed files.


Author: Amos Folarin
Organisation: KCL/SLaM



1) Sun Grid Engine
2) Zcall
3) Opticall


1) Read notes (GenomeStudio.SOP.v1.2.docx – the most up-to-date SOP are on the confluence page ) on processing the data in GenomeStudio
2) Generate the GenomeStudio Report file (as required for by Zcall, parses this as input for both Zcall and Opticall rare callers):

i) In GenomeStudio select ‘Full Data Table’ tab.
ii) Click on ‘Column Chooser’ icon.
iii) In Displayed Columns select ‘Name’, ‘Chr’,’Position’, and all your samples.
iv) In Displayed Subcolumns select ‘GType’, ‘X’ and ‘Y’.
v) Hit OK then click on ‘Export displayed data to a file’ icon.

3) Copy the into a working directory
4) Edit the paths as indicated in this script for your installations of Zcall and Opticall etc.
5) Specify the datapath and basename variables for the GenomeStudio Report generated in step (2)
6) Execute the pipeline bash script (