Sequencing Informatics
Next Generation DNA Sequencing Resources
Stuart M. Brown
NYU Center for Health Informatics
& Bioinformatics
NYU Center for Health Informatics
& Bioinformatics
-
FACT: Next-Gen Seq data lives forever
- Every file from every lane of every run will be reanalyzed in the future, in ways that you have not yet thought of
- Data is aggregated across multiple runs and re-analyzed (differential expression, SNP calling, etc)
- You will need to submit original ‘raw’ data files to NCBI archive
- Mange it well centrally, or mange it poorly (and differently) in each of 20-50 different Investigator’s labs
- Plan to double your data storage every year.
- Two words: OFFSITE BACKUP (!)
- “Hurricane Sandy”
- Bioinformatics generally must take the lead role in NGS quality control.
- QC software is part of the data processing pipeline.
- You don’t really know if an experiment has worked until the reads are aligned.
- Most NGS problems are diagnosed through data analysis
- Pre-Alignment: Vendor software, FASTQC
- # raw clusters
- % filtered by vendor software
- % error and per-base quality scores
- % duplication (PCR artifacts)
- After Alignment
- % alignment to reference genome
- average depth of genome or transcriptome coverage
- over-represented sequences
- for ChIP-seq:
- clustering of reads into peaks
- distance from peaks to promoters (TSS)
- for RNA-seq
- % rDNA
- genomic DNA contamination (reads map to non-coding regions)
- 3’ (or 5’) bias
- Simon Andrews, Babraham Bioinformatics
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ - Java app, runs on any computer
- per-base sequence quality
- average whole read quality scores
- per-base GC content
- sequence length (all same for Illumina)
- sequence duplications
- overrepresented sequences
- With the Illumina HiSeq producing >200 million reads per sample, what depth of coverage is needed for RNA-seq?
- Can we multiplex several samples per lane and save $$ on sequencing?
- For expression profiling (and detection of differentially expressed genes), probably yes, 2-4 samples per lane is practical
Depth of Coverage, cont’d
100 million reads, 81% of genes FPKM ≥ 0.05
Each additional 100 million reads detects ~3% more genes
http://picard.sourceforge.net/command-line-overview.shtml
Huge collection of tools to work on SAM/BAM files
- find barcodes and demultiplex
- GC bias
- insert size
- mark (and remove) duplicates
- quality score distribution, mean quality by cycle
- RNA-seq metrics (3’/5’ bias; %rRNA; percent of bases of aligned reads in coding, intron, UTR, intergenic regions)
Picard tools: CollectRnaSeqMetrics
| 5’ bias (strand oriented protocol) |
no bias (low coverage at ends of transcript) |
3’ bias (poly-A selection) |
 |
 |
 |
Stuart Brown’s Book
: