1. Konstantin Okonechnikov Qualimap v2: advanced quality control of
high throughput sequencing data
Max Planck Institute For Infection Biology
Molecular Biology department
BOSC 2015 Dublin 1 1

2. Quality Control of HTS data
High-throughput sequencing : high speed, deep coverage, various applications
However there are platform specific, protocol-based and analysis errors (duplicates, PCR, algorithm induced biases, etc…)
There are tools that allow to perform Quality Control task:
FastQC
Samtools
Picard tools
RSeQC …
Qualimap
There are various solutions. Also Qualimap. Previous year second version of Qualimap.

3. Qualimap
A Java application, computes statistics and graphs for the evaluation and quality control of HTS alignment data. Both GUI and command line interfaces available.
Input: BAM/SAM, GTF/GFF/BED
Output: HTML /PDF, simple text format
Analysis modes: BAM QC, RNA-seq QC, Counts QC
So we provided our solution. Since we are here in the visualization Qualimap

4. Qualimap 2
A Java application, computes statistics and graphs for the evaluation and quality control of HTS alignment data. Both GUI and command line interfaces available.
Input: BAM/SAM, GTF/GFF/BED
Output: HTML /PDF, simple text format
Analysis modes: BAM QC, Multi-sample BAM QC, RNA-seq QC + Counts QC v2
So we provided our solution. Since we are here in the visualization Qualimap

5. BAM QC mode
BAM file global statistics: alignment types, coverage analysis, insert size, duplication rate, etc.
Improvements and novelties:
Additional metrics for mapping quality, coverage, insert size
Region/out-of-region analysis
Duplicate alignments exclusion
… many more …
Global data (reference size, number of reads), coverage (mapped, paired, per chromosome) , reads info (insert size, quality, homopolymers, duplication rate)

6. Novel mode: multisample BAM QC
Common analysis of generated BAM QC results
Detailed plots: coverage, GC-content, insert size, etc.
Principal component analysis based on selected parameters
A new feature, available from the snapshots

7. Novel mode: multisample BAM QC
Common analysis of generated BAM QC results:
Detailed plots: coverage, GC-content, insert size, etc.
Principal component analysis based on selected parameters
A new feature, available from the snapshots

8. Redesigned RNA-seq quality control: RNA-seq QC + Counts QC
Analysis types: transcript coverage and proportion, GC bias, 5‘-3‘ bias, counts analysis (expression level, gene types, etc.)
Novel :multisample analysis in Counts QC
Main properties are comparable to RSeQC and RNASeQC. Novelty multisample analysis, example saturation, expression analysis

9. Becoming more “open-source”
Source code repository: bitbucket
Discussion forum: google-groups
User activity ( – ):
15 bug reports
9 novel issue suggestions
3 bug-fixes from users
Supporters are mentioned in commits, news and history log
Each user mentioned in the log

10. Thank you for your attention!
Useful links:
Web-site:
Bitbucket:
Reference: García-Alcalde F, Okonechnikov K, et al “Qualimap: evaluating next-generation sequencing alignment data." Bioinformatics 28, no. 20 (2012):
This is it. If you are working with NGS alignment data, give Qualimap a try. Hope you will find it as useful as we find it for our research. Thanks a lot.