The Bioinformatics Team.

• Tom Carroll (ex-Head of Bioinformatics)
• Gopuraja Dharmalingam (Epigenetics)
• Sanjay Khadayate (Genes and Metabolism)
• Yi-Fang Wang (Integrative Biology)
• Marion Dore (Genomics)
• TBD (Proteomics)

Websites

http://mrccsc.github.io/

Computing and Bioinformatics

http://bioinformatics.csc.mrc.ac.uk/

Where to find the team.

• ICTEM
• 2nd floor, MRC.
• Central aisle,
• Behind the printers.

Role

• Experimental design.
• Analysis
• Bioinformatics Infrastructure.
• Training.

Text

Experimental Design

“To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.”

Fisher RA, 1938

• Work closely with Genomics Team to help with design questions
  • Replicate number.
  • Sequencing depth.
  • Sequencing strategy.

Nice example experimental design

• RNA-seq experiment (2014)
• Graph shows major sources of variation.
• Samples from same groups close together.
• Samples from different experimental conditions separate. 

Nice example of experimental design

• Smaller sources of variance relating to other metadata.
• Samples group according to the day that RNA was extracted on.
• Known effects can be removed from analysis.

Analysis

• Initial data processing and QC.
• Advice and support as needed.
• Support throughout project.​

Increased demand for long term support.

Authorships in > 30 publication since 2014

Analysis support

• Increased use of high throughput techniques in projects.
• Greater use for bioinformatics in projects.
• Analysis across project lifetime or individual elements.
• Requires reproducible research.

Reproducible research

• Reproducible results from computational methods should be straight forward.
• Common problems.
  • Version and software changes.
  • Lack of analysis documentation.

rMarkdown

• rMarkdown converted R (or python/many languages') code to dynamic reports.
• Code, results and versions are reported within the same page.
• HTML allows for inclusion of dynamic elements.

Project tracking

• Use Redmine software.
• Multiple user interface to record project information.
• Repository to version control scripts (SVN).
• Wiki for internal documentation.

Infrastructure

• Analysis pipelines.
• Data delivery.
• Software development.

ChIP-seq and RNA-seq

pipelines.

• Common analysis steps can be automated.
• Optimised for local resources.
• Reproducible and comparable.
• ChIP-seq and RNA-seq pipeline to automate alignment and quality control.​
• Freely available for use or customisation on github

http://mrccsc.github.io/

Genomics Pipeline

• Joined forces with Genomics to better automate some processes of basecalling/demultiplexing data. 
  • Basecalling - Convert optical data to sequence information.
  • Demultiplexing - Split sequence data by a sequence tag (index) which is unique per sample
• Process is 
  • ​Users submit indexed samples and a sample sheet containing sample/index information.
  • Genomics provide a set of QCed sample sequence files for further analysis.

Why?

• Reduce time consuming steps.
  • Sample sheet templates often misinterpreted by users or incorrectly filled out.
  • Thankfully not very originally and common errors can be automatically caught.
  • Manual creation of basemasks for index/read layout.
  • Remove dependence on Illumina's Windows' point and click software - Sequencing Analysis Viewer (SAV).
  • Reduce copy and paste.
• Remove sources of potential error.
  • An automated process will be more consistent (in both accuracy and error).
  • Reduce copy and paste errors.

The MRC-LMS Genomics Pipeline

• Automate samplesheet cleaning and updating.
• Create basemasks or index/read description.
• Parse results from basecalling/demultiplexing
  • Binary files from Real Time Analysis software.
  • XML files from BCL2fastq version 2.17 or above.
• Summarise results to provide to user alongside resulting sequence files in fastq format.
• Written in R, moved to Bioconductor in May 2017.

Others in the pipeline

• Internal RNA-seq/ChIP-seq pipeline
  • Written in R.
  • Easily installed, maintained.
  • Allows Core to move between systems easily (both install and parallelisation).
  • Released soon.
• Basecalling to ChIP/RNA-seq QC/initial analysis all in one reproducible, version controlled report.
• Proteomics data pipeline?

UCSC genome browser

• UCSC allows for visualisation of a range of genomics data types.
• Public instances can be very slow.
• CSC public instance maintained by Bioinformatics team.

• web: http://ucsc

  FTP: ftp://ucsc

Software

• Develop and maintain software relevant to our work.
• R packages and javascript toolsets.
• Release software to public (peer-reviewed) repositories.
  • Reviews improves code quality
  • Collaborative feedback.
  • Automated build reports and checking.

• IGV is an popular alternate to UCSC.
• Allows for inclusion of per sample metadata and complex sample display types.
• Tracktables creates standalone and rMarkdown compliant tables.

Tracktables

• Visualising genomics data over regions of the genome.
• Allows for rapid generation of profiles and subsetting by IDs or other regions.
• Arithmetic operations between and within profiles allows for rapid, iterative investigation of hypotheses.

Soggi

• Peak calling in R is convenient.
• Many peak callers in R have unwieldy input and far from optimised.
• triform contains a reliable peak calling algorithm in need of optimisation for speed and long marks.
• MRC CSC took over maintenance of triform in 2015

triform

basecallQC

• Automate basecalling and demultiplexing.
• Less manual intervention.
• No dependence on proprietary software.
• Customised for MRC-LMS use

Material Repository

• 2014 (R Basics)
  • Intro to R course.
  • Reproducible R.
• 2015 (Basics of Bioinformatics)
  • ChIP-seq.
  • RNA-seq.
  • Bioconductor.
  • Genome Browsers and File Formats.
• 2016 (Intermediate R and Bioinformatics)
  • Intermediate R.
  • Alignments and Counting.
  • Visualising High Throughput Sequeuncing Data
  • Introduction to Python

CSC Bioinformatics Course

• Current and upcoming Bioinformatics training material can be found at our site

http://mrccsc.github.io/training.html

Github/Github.io/Travis/Appveyor and Training in 2017

• All training is hosted on Github.
• All pages automatically synced to Github.io
• Training courses automatically tested on windows/mac/linux after any changes.

Training Collaborations

• All material is open source (CC BY-NC-ND 4.0).
  • Free to distribute or collaborate.
  • No commercial use.
  • No remixes.
• Bioinformatics Core Shared Training (with Mark Dunning)
  • Genomic File Formats
  • Intermediate R
  • ChIP-seq ->
  • IGV ->
  • -> Experimental Design

Training on the cloud.

• Awarded grant from Amazon Web Services.
• Use virtual linux servers to host  R and RStudio pre-loaded with course material.
• Allow for larger, real world analysis tasks during training.
• No need for dedicated classroom - train from anywhere.

Future!

• Release and link RNA-seq and ChIP-seq pipelines.
  • Submit samples and get QCed, aligned data back.
• Support proteomics and analysis of other MRC high throughput technologies.
• Training updates
  • More python!
  • More non-programmer courses (e.g. Deeptools)!
  • Julia and other languages?!
  • Interactive online training!!
• More analysis!!!

Have a great week!

Bioinformatics Team

Tom - thomas.carroll@imperial.ac.uk

Gopu - gopuraja.dharmalingam@imperial.ac.uk

Sanjay -  sanjay.khadayate@imperial.ac.uk

Yi-Fang - yifang.wang@imperial.ac.uk

Marion - marion.dore@imperial.ac.uk