Alexander Fillbrunn, Julianus Pfeuffer, Jeanette Prinz

The Center for Integrative Bioinformatics (CIBI) and KNIME

Analysis of Mass Spectrometry and Sequence

Data with KNIME

Schedule

• About us

• Generic KNIME Nodes

• Mass-spectrometry data analysis in KNIME with OpenMS

• Introduction and theory of label-free quantification

• Demo of a label-free quantification workflow

• Analysis of high-throughput sequencing data with KNIME and SeqAn

• Introduction and theory of variant calling

• Demo of a variant calling workflow

German Network for Bioinformatics Infrastructure

de.NBI Mission Statement

• The 'German Network for Bioinformatics Infrastructure' provides comprehensivefirst-class bioinformatics services to users in life sciences research, industry andmedicine. The de.NBI program coordinates bioinformatics training andeducation and the cooperation of the German bioinformatics community withinternational bioinformatics network structures.

Center for Integrative Bioinformatics (CIBI)

• … provides cutting-edge and integrative tools for proteomics, metabolomics, NGS and image data analysis as well as a workflow engine to integrate tools into coherent solutions for reproducible analysis of large-scale biological data.

Generic KNIME Nodes

• Wrapping of command line tools (OpenMS, SeqAn,…) in KNIME via GenericKNIMENodes (GKN)

• Every OpenMS and SeqAn tool writes its Common Tool Description (CTD) via its command line parser

• GKN generates Java source code (static) or an XML representation (dynamic) for nodes to show up in KNIME

• Wraps C++ (&more) executables and provides additional file handling nodes

Julianus Pfeuffer, Alexander Fillbrunn

Mass-spectrometry data analysis in KNIME

OpenMS• OpenMS – an open-source C++ framework for computational mass

spectrometry

• Jointly developed at ETH Zürich, FU Berlin, University of Tübingen

• Open source: BSD 3-clause license

• Portable: available on Windows, OSX, Linux

• Vendor-independent: supports all standard formats and vendor-formats through proteowizard

• OpenMS TOPP tools – The OpenMS Proteomics Pipeline tools

– Building blocks: One application for each analysis step

– All applications share identical user interfaces

– Uses PSI standard formats

• Can be integrated in various workflow systems

– Galaxy

– WS-PGRADE/gUSE

– KNIME

Kohlbacher et al., Bioinformatics (2007), 23:e191

Installation of the OpenMS plugin

• Community-contributions update site (stable & trunk)– Bioinformatics & NGS

• Provides > 180 OpenMS TOPP tools as Community nodes – SILAC, iTRAQ, TMT, label-free, SWATH, SIP, …

– Search engines: OMSSA, MASCOT, X!TANDEM, MSGFplus, …

– Protein inference: FIDO

A Mass Spectrum

Peak

DataMaps

Annotated

Maps

Data Flow in Shotgun Proteomics

HPLC/MSSample

Sig.

Proc.

Data Reduction

Diff.

Quant.

Identification

Differentially

Expressed

Proteins

100 GB

1 GB50 MB

50 MB 50 kB

Raw

Data

Quantification StrategiesQuantitative Proteomics

Relative Quantification

Labeled

In vivo

14N/15N SILAC

In vitro

iTRAQ TMT 16O/18O

Label-Free

SpectralCounting MRM Feature-Based

Absolute Quantification

AQUA SISCAPA

After: Lau et al., Proteomics, 2007, 7, 2787

Quantitative Data – LC-MS Maps

• Spectra are acquired with rates up to dozens per second

• Stacking the spectra yields maps

• Resolution:

– Up to millions of points per spectrum

– Tens of thousands of spectra per LC run

• Huge 2D datasets of up to hundreds of GB per sample

• MS intensity follows the chromatographic concentration

LC-MS Data (Map)

13

Quantification(15 nmol/µl, 3x over-expressed, …)

Label-Free Quantification (LFQ)

• Label-free quantification is probably the most natural way of quantifying – No labeling required, removing further sources of

error, no restriction on sample generation, cheap

– Data on different samples acquired in different measurements – higher reproducibility needed

– Manual analysis difficult

– Scales very well with the number of samples, basically no limit, no difference in the analysis between 2 or 100 samples

LFQ – Analysis Strategy

1. Find features in all maps

1. Find features in all maps

2. Align maps

LFQ – Analysis Strategy

1. Find features in all maps

2. Align maps

3. Link corresponding features

LFQ – Analysis Strategy

1. Find features in all maps

2. Align maps

3. Link corresponding features

4. Identify features

GDAFFGMSCK

LFQ – Analysis Strategy

1. Find features in all maps

2. Align maps

3. Link corresponding features

4. Identify features

5. Quantify

GDAFFGMSCK

1.0 : 1.2 : 0.5

LFQ – Analysis Strategy

Feature Finding

• Identify all peaks belonging to one peptide

• Key idea:

– Identify suspicious regions (e.g. highest peaks)

– Fit a model to that region and identify peaks explained by it

Feature Finding

• Extension: collect all data points close to the seed

• Refinement: remove peaks that are not consistent with the model

• Fit an optimal model for the reduced set of peaks

• Iterate this until no further improvement can be achieved

Feature-Based Alignment

• LC-MS maps can contain millions of peaks

• Retention time of peptides (or metabolites) can shift between

experiments

• In label-free quantification, maps thus need to be aligned in

order to identify corresponding features

• Alignment can be done on the raw maps (where it is usually

called ‘dewarping’) or on already identified features

• The latter is simpler, as it does not require the alignment of

millions of peaks, but just of tens of thousands of features

• Disadvantage: it replies on an accurate feature finding

Feature-Based Alignment

~350,000 peaks

~ 700 features

Map 1

Map 2

Map k

…

rt

m/z

T1

T2

Tk

Consensus map

• Dewarp k maps onto a comparable coordinate system

• Choose one map (usually the one with the largest number of features) as reference map (here: map 2 -> T2 = 1)

Multiple Alignment

…

rt

Peptide Identification

Sven Nahnsen

LC-MS/MS experiment Fragment m/z values

Sequence database

Theoretical fragment m/z values from suitable peptides

Compare

Q9NSC5|HOME3_HUMAN Homer protein homolog 3 -Homo sapiens (Human)MSTAREQPIFSTRAHVFQIDPATKRNWIPAGKHALTVSYFYDATRNVYRIISIGGAKAIINSTVTPNMTFTKTSQKFGQWDSRANTVYGLGFASEQHLTQFAEKFQEVKEAARLAREKSQDGGELTSPALGLASHQVPPSPLVSANGPGEEKLFRSQSADAPGPTERERLKKMLSEGSVGEVQWEAEFFALQDSNNKLAGALREANAAAAQWRQQLEAQRAEAERLRQRVAELEAQAASEVTPTGEKEGLGQGQSLEQLEALVQTKDQEIQTLKSQTGGPREALEAAEREETQQKVQDLETRNAELEHQLRAMERSLEEARAERERARAEVGRAAQLLDVSLFELSELREGLARLAEAAP

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24574.83580.70580.92579.99603.92 611.14616.74

570.84571.72580.40591.18579.35607.25 611.42614.45

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

569.24572.33580.30581.46582.63606.32 610.24616.14

1 QRESTATDILQK 18.77

2 EIEEDSLEGLKK 14.78

3 GIEDDLMDLIKK 12.63

Score hits

Theoretical spectra

m/z

[%]

m/z

[%]

m/z

[%]

m/z

[%]

Experimental spectra

m/z

RT

© 2019 KNIME AG. All rights reserved.

Variant Calling/Annotation with SeqAn and KNIME

Jeanette Prinz, Julianus Pfeuffer, Alexander Fillbrunn, René Rahn

© 2019 KNIME AG. All rights reserved. 32

Next Generation Sequencing (NGS)

• DNA sequencing: process of determining the nucleic acid sequence – the order of nucleotides (A,T,G,C) in DNA

• NGS platforms perform sequencing of millions of small fragments (reads) of DNA in parallel => fast, cheap

• Bioinformatics used to map the individual reads to reference genome

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841808/

© 2019 KNIME AG. All rights reserved. 33

NGS Application Areas

Taken from: http://www.nfcr.org/sites/default/files/images/GenomicProfiling2.jpghttp://ecx.images-amazon.com/images/I/51tztcMqIRL._SS500_.jpg

Cancer

Hereditary Diseases

Metagenomics

Agriculture

© 2019 KNIME AG. All rights reserved. 34

SeqAn - a Bioinformatics Resource

© 2019 KNIME AG. All rights reserved. 35

SeqAn Library Features

• Efficient index data structures• Efficient algorithms• Fully parallelized and vectorized pairwise alignment

algorithms• Search schemes for index searches

• Fast I/O• SAM/BAM, FastA/FastQ, VCF, …

© 2019 KNIME AG. All rights reserved. 36

SeqAn: NGS Tools

Installation: KNIME Community Contributions-> Bioinformatics & NGS-> SeqAn NGS ToolBox

© 2019 KNIME AG. All rights reserved. 37

Variant Calling

https://www.ebi.ac.uk/training/online/course/human-genetic-variation-i-introduction/variant-identification-and-analysis/what-variant

© 2019 KNIME AG. All rights reserved. 38

Variant Annotation

© 2019 KNIME AG. All rights reserved. 39

Variant Consequences

http://www.ensembl.info/2012/08/06/variation-consequences/

© 2019 KNIME AG. All rights reserved. 40

Variant Calling/Annotation with KNIME

Demo

© 2019 KNIME AG. All rights reserved. 41

Summary

• SeqAn KNIME nodes for mapping to reference genome and variant calling

• KNIME nodes for variant annotation and visualization of results

• Workflow can be easily extended and adjusted to your own needs, e.g. to include quality control

42© 2019 KNIME AG. All rights reserved.

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KNIME® is also registered in Germany.