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Novel simplified bioinformatics for NGS data analysis

Marc Noguera-Julian, PhD

IrsiCaixa AIDS Research Institute

Badalona, Catalonia

Bringing the next generation

sequencing to the clinic

Outline

1. Intro:

• The role of bioinformatics

• How is bioinformatics used?

• Challenges & potential for bioinformatics in clinical use

• How should a bioinformatic tool be like

• Simplified Bioinformatic tools

1. Geno2Pheno-[NGS]

2. Exatype

3. MiCall

4. Hydra

5. PASeq.org

• Standardization efforts

Skurtur (http://skurtur.com)

NGS data deluge

Poster #86


• All NGS platforms produce [tens/hundreds] Thousands of sequences

• Bioinformatics skills need to be acquired (hired/learned) when adopting

NGS technologies.

• Bioinformatics needed to:

• Ensure data quality

• Design & Ensure adequate data analysis

• Ensure result data structure for further exploitation (when possible)

NGS data deluge


How is NGS being used for HIV-DR testing?

• Mostly Illumina [H,M]iSeq platform used in research, but

• Many [in-house] Experimental protocols in place for sample preparation

and library preparation

• Poll Data on how NGS data is generated and processed for HIV-DR

testing


How is NGS currently data being analyzed?

• Most research sites use in-house pipelines and have bioinformatics

support

• Poll Data from INTEGRATE project on how NGS data is generated and

processed for HIV-DR testing

NGS Data deluge

HIV-DR testing challenges


• Computational (storage & processing) resources scale-up

• Many experimental designs exist that may affect data analysis

efficacy/validity.

• Need for automated, robust and reproducible analysis

• Multiple sequencing platforms with different sequencing chemistries and

intrinsic/specific error models or data processing need.

• Heterogeneous analysis strategies and output formats render results

difficult to compare.

• Data format and interoperability is an important ISSUE. Need for

standardization.

• Data regulatory aspects need to be embedded in software plaftorms:

• Storage time

• Access policies

However,

• Individual Report and

DR interpretation• Individual Report and

DR interpretation

Health

CenterHealth

CenterHealth

Center

Central

Diagnostics

Lab

Central

Diagnostics

Lab

Central Diagnostics

Lab/NGS Sequencing

• High Throughput

• Low Cost

• Validated Assay

Automated

Bioinformatics

• One-click use by Lab

Tech

• Secure

• Highly Scalable

• Automated QA/QC

• Automated integration

with HIVDR interpretation

systems (HIVDB-

Stanford)

Quality-Curated

HIVDR Data

Structured Database

Individual Report and

HIVDR interpretation

Program

Officer Real Time Surveillance:

•HIVDR Epidemiology

Actionable ReportCloud Computing:

Centralized Data Analysis

HIVDR test

required

Training & Quality Improvement

• Web Interface

• Pre-built queries

• Embedded Statistics

Sample Shipment

•QA/QC Monitoring

Great Potential from NGS in HIV DR clinics

Noguera-Julian. et al. J. Infect. Dis. 2017

NGS Data Deluge

HIV-NGS-DR Testing Needs

Several key features for bioinformatics to move into clinical practice:

• Usually no bioinformatics support on site in the clinical setting

• Usually no or minimal computational infraestructure available

• Be remotely usable by users with no bioinformatics skills through a

user-friendly web interface accessible from simple computers or

smartphones

• Provide robust, reproducible, and easy-to-interpret results using

standard and well-established HIV resistance interpretation rules (eg,

Stanford HIVdb or equivalent);

• Incorporate built-in quality standards

• Avoid unnecessary transfer of large data volumes

• Provide clinically actionable results that can be downloadable with

limited network access

• Demand minimal or no local computational infrastructure

• Seamlessly respond to varying number of samples in highly scalable

manner without an impact in time to results

• Minimal(No) cost to enable their sustainable adoption by LMICs


Available Software

Multiple Software packages/platforms exist:

Hezhao. et al. JIAS 2018, submitted

Which usually means bioinformatics support

Available software for HIV-NGS-DR Testing

Noguera-Julian. et al. J. Infect. Dis. 2017

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PASeq PASeq PASeq

MiCall Hydra MiCall Hydra MiCall Hydra

15% 5% 1%

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MiCall

Hydra

Good agreement between analysis pipelines

Simplified

Data

Analysis

Common Concepts Data Analysis for NGS HIV-DR Testing

FastQ file: Similar to FastA file but containing sequences with per-nucleobase quality

values. This is the raw material produced by most(if not all) of the sequencers.

@HWI-D00283:145:C6BEJANXX:5:1101:18971:2203

AGGCCTTGAATGAGATTCCAAAAATCTATCGACTACAATCCCCCAAAAATCTATCGACTAC+EBB=B>FEGGGEGGFEFC@G:CDD>FGGGCBCAGGGGGGEEBB=B>FEGGGEGGFEFC

Sequence/base Quality:

Numeric value (1-40

range) indicating the

probability that the

sequence/base readout is

wrong. Higher value

means higher quality.

Paired-end/Single-end:

Refers to experimental

design in how every DNA

fragment has been read:

from both ends or from a

single end.

Common Concepts Data Analysis for NGS HIV-DR Testing

Sequence Alignment: usually called SAM or BAM file. Contains all information

regarding how every one of the fastQ sequences has aligned against a reference

Depth of coverage or, simply, “coverage”, refers to the number of times each

genomic position has been read by an independent sequence read.

Common steps in Data Analysis for NGS HIV-DR Testing

Quality Filter/control

Sequence alignment: reference wise

Variant Calling

Resistance Interpretation

Structured Data Storage

Contamination

Control

Alignment

Quality Control

Alignment filesCoverage Plots

Codon/AA TablesCodon/AA Freqs

Consensus / Variant Calling

Nucleotide tableConsensus Seq

Resistance Reports

Queryable Database

Real-time Algorithms[Real time] surveillance

Sequence Quality & Contamination Control

Quality Control: Essentially removing all sequences that either have low

quality values (higher probability of missed nucleobase-calls or that are

too short to obtain a reliable alignment

Contamination Control: Remove all

sequences that are not legit, not from the

sample being studied

- External contamination: Usually human

DNA, does not interfere with HIV

alignments but can be a reason to

have a very low throughput

- Cross-Contamination: DNA

contamination from one sample to

another during either library

preparation or sequencing. I can

interfere with DR testing, specially at

low thresholds

Available Software

Multiple Software packages/platforms exist:

Hezhao. et al. JIAS 2018, submitted

Geno2Pheno-[NGS]

https://ngs.geno2pheno.org/

Döring M, et al. Nucleic Acids Res. gky349.

Poster #25

Geno2Pheno-[NGS]

geno2pheno[ngs-freq] Report

1/3© ngs.geno2pheno.org (Version 1.0.453)

Sample: 1_HIV Case Study

Patient:

Date of birth:

Sample received:

Sample type:

Physician:

Study:

Viral load:

Sample collected:

Date of report: May 28, 2018

Treatment:

Resistance Interpretation: HIV-1, Subtype B (100%)

Consensus sequence at prevalence >= 10% Consensus sequence at prevalence >= 2%

Drug SIR Z Mutations >= 10% SIR Z Mutations < 10% and >= 2%

ABC 0.2 No resistance mutations found. 3.8 M184V (2%)

ddI 0.3 R211E (73%) 2.7 M184V (2%)

3TC 0.1 No resistance mutations found. 7.8 M184V (2%)

d4T 0.6 No resistance mutations found. 0.6 No further resistance mutations found.

TDF -0.6 No resistance mutations found. 0 E138K (7%)

ZDV 0.5 No resistance mutations found. 0.6 No further resistance mutations found.

EFV -0.4 No resistance mutations found. -0.1 No further resistance mutations found.

ETR 0.7 No resistance mutations found. 3.3 E138K (7%)

NVP -0.1 No resistance mutations found. 0.2 No further resistance mutations found.

RPV -0.2 No resistance mutations found. 1.8 E138K (7%)

APV 1 L63P (84%) 2.1 No further resistance mutations found.

ATV 3.3 I93L (99%) 4.4 No further resistance mutations found.

DRV 0 No resistance mutations found. 0.7 No further resistance mutations found.

IDV 3.1 No resistance mutations found. 3.1 No further resistance mutations found.

LPV 1.9 L63P (84%) 2.1 No further resistance mutations found.

NFV 1.9 No resistance mutations found. 1.9 No further resistance mutations found.

SQV 1.4 No resistance mutations found. 1.7 No further resistance mutations found.

TPV 0.7 No resistance mutations found. 1.4 No further resistance mutations found.

NR

TI

NN

RT

IP

I

The clinical relevance of resistant variants below 10% is still unclear. Since their presence may reduce treatment success, these mutations should be considered in unison with other f actors such as the viral load.The Z-column indicates the change of the outcome estimate (e .g. reistance factor) in terms of standard deviations relative to the mean outcome for treatment-naive persons.

Legend

susceptible intermediate resistant

FEATURES:

• User downloadable reports

• Automatic Coverage alerts

• Detects APOBEC mutations

• Codon table as input requires pre-

processing but allows flexibility in

sequence analysis

• User-definable detection thresholds

• Uses g2p-resistance for mutation

interpretation

• https://ngs.geno2pheno.org/

• No registration required

• Accepts HCV Data

https://ngs.geno2pheno.org/

Hyrax Exatype

https://exatype.com

Hyrax Exatype: Quality Control

Hyrax Exatype: Resistance Interpretation

Hyrax Exatype

FEATURES

• Commercial platform for cloud-based DR testing in HIV

• Embedded all-in-one quality control, sequence alignment,

variant calling and Stanford HIVDB based Resistance

interpretation

• Cloud Based, highly multiplexed and scalable

• Processes data from different NGS platforms.

• Codon-aware aligner strategy (RAMICS/Examap)*

• Free analysis for 50 samples.

• https://www.exatype.com

*Wright et al, NAR, 2014

MiCall for NGS-based data analysis

MiCall for NGS-based data analysis

FEATURES:

• User downloadable resistance reports

• Raw Data used as input

• Embedded error model building

• RawData directly used from Illumina

basespace

• Can download all intermediate files

• Registration/Authorization required

• Free to use(?)

• Cloud Based, highly multiplexed and

scalable

Hydra Web for HIV NGS data analysis


Sequence length threshold

Score Cutoff

Error rate (platform-specific)

Quality for variants

Depth of coverage

Minimum mutation count

Frequency threshold

Hydra Web : Quality Control

Hydra Web : Results

FEATURES:

- Highly customizable analysis parameters

- Multi-sample upload & results download for high scale analysis

- User registration and access-controlled data storage

- No drug-level resistance interpretation.

- Can download intermediate files

- No pdf report generated.

- https://hydra.canada.ca/

PASeq : Polymorphism Analysis by Sequencing

www.paseq.org

• Minimal User intervention – Drag & Drop raw files

• User can input sample metadata (optional)

HIV-NGS-DR Testing in PASeq

HIV-NGS-DR Testing

2017-11-06 07:27:25. Sample created

2017-11-06 07:28:10. Allocating Resources

2017-11-06 07:28:47. Server assigned. Launching instance

2017-11-06 07:29:40. Initiating process

2017-11-06 07:29:40. Downloading Fastq R1 file

2017-11-06 07:29:40. Downloading Fastq R2 file

2017-11-06 07:29:40. This is a Paired-end Analysis

2017-11-06 07:29:40. Creating WorkSpace

2017-11-06 07:29:41. Going Through Quality Control Using Trimmomatic

2017-11-06 07:31:01. Quality Filtering and Adapter Trimming: Trimmomatic

2017-11-06 07:31:01. MinLen=75 minQual=30 SlidingWindow=20

2017-11-06 07:32:53. 17937 of 21643 survived

2017-11-06 07:32:54. Checking for External Contamination

2017-11-06 07:34:32. Found 17016 HIV sequences and 921 non-HIV sequences

2017-11-06 07:34:40. Indentifying potential contamination source

2017-11-06 07:35:44. Compressing Files and moving on

2017-11-06 07:36:00. Creating Coverage plots

2017-11-06 07:36:53. Calling Deep Variants

2017-11-06 07:39:59. Querying HIVDB using deep Variant Data at different thresholds

2017-11-06 07:40:49. Querying HIVDB-Stanford with consensus sequence for resistance interpretation

2017-11-06 07:40:50. Storing Consensus sequences for surveillance

2017-11-06 07:41:05. Uploading Results

2017-11-06 07:41:42. Job Finished

~30 min Analysis Time

Data Download & setup

Quality Control

Contamination Control

Sequence Alignment &

Variant Calling

Resistance Interpretation

HIV-NGS-DR Testing – PASeq Output – Embedded Quality

Control

HIV-NGS-DR Testing – PASeq Output – User-defined

threshold

···

···

5%

···

···

1%

Mutation Protein Frequency (%)

Q148R INT 3.177

N155H INT 99.877

S147G INT 99.838

M46I PR 1.825

E138A RT 99.624

IrsiCaixaReport date: 2017-11-06 18:17:11 CETStanford HIVDB Version:8.2 PASEQ Version:

Resistance interpretation information obtained from Stanford HIVDB (https://hivdb.stanford.edu/)This report has been generated by PASeq Web Service. All data interpretations are for research use only, not for diagnostic or clinical purposes, and are provided as is with NO guarantee of any class.



Quality Control

HIVdb mutation comments

E138A is a common polymorphic accessory mutation weakly selected in patients receiving ETR and RPV. It reduces ETR and RPVsusceptibility ~2-fold. It has a weight of 1.5 in the Tibotec ETR genotypic susceptibility score.

H51Y is a rare non-polymorphic accessory mutation selected in patients receiving RAL and EVG and in vitro by DTG. H51Y reduces EVGsusceptibility 2 to 3-fold. It does not reduce RAL or DTG susceptibility.

M46I/L are relatively non-polymorphic PI-selected mutations. In combination with other PI-resistance mutations, they are associated withreduced susceptibility to each of the PIs except DRV.

N155H is a non-polymorphic mutation selected in patients receiving RAL and EVG. Alone, it reduces RAL and EVG susceptibility ~15-fold and30-fold, respectively. N155H has been selected by DTG in RAL-experienced patients but alone does not reduce DTG susceptibility.

Q148H/K/R are non-polymorphic mutations selected by RAL and EVG. Alone, Q148H reduces RAL and EVG susceptibility ~5 to 10-fold. Alone, Q148R/K reduce RAL and EVG susceptibility ~30 to 100-fold. In combination with G140S/A or E138K/A, they reduce RAL and EVGsusceptibility >100-fold. Alone, Q148H/K/R have minimal effects on DTG susceptibility. In combination with G140S/A/C and/or E138K/A, theyreduce DTG susceptibility up to 10-fold.

S147G is a non-polymorphic mutation selected in patients receiving EVG. It reduces EVG susceptibility 5 to 10-fold. It does not reduce RAL orDTG susceptibility.



Mutation Protein Frequency (%)

H51Y INT 3.256



PASeq.org

FEATURES:

• User downloadable resistance reports

• Configurable report

• Automatic Low coverage alerts

• Detects contamination

• Detects APOBEC mutations

• Raw Data used as input

• Can download all intermediate files

• User-definable detection thresholds

• Use real-time updated HIVdb-Stanford

• Registration required

• Free to use

• Cloud Based, highly multiplexed and

scalable

• Shareable Outputswww.paseq.org

Ongoing Bioinformatics Standardization Efforts

aHezhao et al, JIAS, 2018, submitted

Winnipeg Consensusa:

- From International symposium from NGS HIV-DR testing and pipeline

developers

- Contains recommendations/consideration at each of the analysis steps

when developing new pipelines.

VQA Dry Panel:

- Set of real samples distributed & sequences among 13 different centers

- NGS data from 6 centers & synthetic data used to evaluate different

pipelines

Variant Data Format(AVF Format)b:

- Intent to find a common data exchange format for amino acid variants.

Based on Hydra HMCF format by Eric Enns (NML/PHAC).

- Applicable to HIV but also to other genomic populations, particularly

viral

- Overcome limitations/artifacts of consensus-like sequence for this kind

of data.

bAVF format, https://github.com/winhiv/aavf-spec

THANKS!

Toti Herms @Microbial Genomics @

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