Basic processing of next-generation sequencing (NGS) data

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NGS/Array expression analysis course , Feb 2011 Henrik Hornshøj

Basic processing of next-generation

sequencing (NGS) data

Getting from raw sequence data to expression analysis!

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Reminder: we are measuring expression

of protein coding genes by transcript

abundance

Chromosome

Gene

Transcript

Protein

mRNA abundance

(transcript sequence copies)

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A typical experimental setup

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Condition 1

Condition 2

Tissue sample 1

Tissue sample 2

.

Tissue sample n

Tissue sample 1

Tissue sample 2

.

Tissue sample n

mRNA sequencing

(RNA-seq)

Data processing

Expression analysis

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NGS/Array expression analysis course , Feb 2011 Henrik Hornshøj Raw sequence generation Sequence filtration Mapping Mapping filtration Gene expression analysis Computing gene expression values

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Sequence file formats

› Output files from sequencing equipment

› Can be MANY and HUGE data files!

A common file format is Fastq (36 bp reads example):

@OBAN:8:1:2:902#0/1 AAAGCTTGTTTTTTCCCTACANCTGTATCCTTTCTT +OBAN:8:1:2:902#0/1 aaaVa]aY]aaba_RYZ``X[DN_[X_]PU_aaZ_a @OBAN:8:1:2:1718#0/1 TAAATATAACATTCTTTCCACNACACTTTCTAGGAC +OBAN:8:1:2:1718#0/1 aaaaaaaaaàaa]aaa[`_XD\\`\à^[^]PFXW . . . @OBAN:8:1:1114:370#0/1 GGAAGGCAGCGAACATCTGTTCAATCTCCTCCTTGG +OBAN:8:1:1114:370#0/1 aâba\]_[_]Wa[[Z]__M^YNX``]]â`_XTXB 5 Sequence number 1 Sequence number 2 Sequence number n DNA sequence Quality sequence

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Sequence format conversions

If specific sequence file format is required for downstream analysis/processing, conversion can be done with programs like maq or fq_all2std.pl

Program: maq (Mapping and Assembly with Qualities) Version: 0.7.1

Contact: Heng Li <[email protected]> Usage: maq <command> [options] Format converting:

sol2sanger convert Solexa FASTQ to standard/Sanger FASTQ mapass2maq convert mapass2's map format to maq's map format bfq2fastq convert BFQ to FASTQ format

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Filtering on raw sequences

› Quality

› Read count per tissue/sample

› Uniqueness

› Trimming

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Mapping sequences

Choice of suitable program

Depend on your purpose of analysis. There are many alignment software programs, here are some commonly used examples

› Bowtie: Basic mapping to reference sequence

› Maq/BWA: Mapping and SNP/indel detection

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Mapping sequences

Choice of reference sequence database – what to map against?

Genome reference: common if you want to do transcript assembly, study alternative splicing and detect SNPs/indels

Transcript reference: common if you want to do ‘simple’ read mapping, count transcript copies and analyze expression levels

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Mapping sequences

Common issues

Reference database not available.

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Mapping sequences

Common issues

Lack of unique mapping will typically lead to random mapping

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ATCATCGGGCCATCGATTAGCTGATCGGACGCTA

TTTTCCTCTTTATCGATTAGCTGGGGGT

ATCGATTAGCTG

Gene A

Gene B

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Mapping sequences

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Example: map reads to reference

database with Bowtie

Build reference database index:

bowtie-build NC_002127.fna e_coli_O157_H7

Test build:

bowtie -c e_coli_O157_H7 GCGTGAGCTATGAGAAAGCGCCACGCTTCC

Map/align reads to references:

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Sequence Alignment MAP format

(SAM: Bowtie output example)

Standard alignment file format:

OBAN:8:1:3:1366#0/1 16 ENSSSCG00000004803|ENSSSCT00000005302|ACTC1 1250 255 36M * 0 0 GTCTACTTTACGTTCAGGATGACAGGTTAATGCTTC VXG^Z^`_ZVUYZ]T`ZQ\_U\W[X_^aaa`[R\\R XA:i:0 MD:Z:36 NM:i:0

OBAN:8:1:3:285#0/1 4 * 0 0 * * 0 0 AGGTATTGGGTTTGGGGGCCTTACACACCAGGTGGA `VOW^b`RVRS`aUQMT[Z^_a_`_Y_]Y]\RNTVW XM:i:0 OBAN:8:1:3:672#0/1 4 * 0 0 * * 0 0 TGGGTATACAGTTCATCCAGTACCCGCTCCGGCTTC a`^\Y_^``]``aQ]a^^_VP`[[\^[SY[YQJWUB XM:i:0

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Filtering sequence mapping

› Low percent reads mapped

› Low number of genes covered

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Computing absolute expression

Counting transcript copies

Mapping output – Sample 1 Count – Sample 1

GeneA ATCGATTAGAC GeneA 2

GeneA ATGGGCTGCAG GeneB 1

GeneB ATTTCGGCTGC GeneC 3

GeneC ATCCCTCCCTA GeneC GGGCTGGCTGC GeneC GCCGGCGGCAA

Count copies

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Creating a gene expression matrix

› Concat and Pivot

› Column IDs defined by tissue samples

› Rows IDs defined by gene/transcript IDs

Gene Sample1 Sample2 . SampleN

GeneA 2 4 GeneB 1 0 GeneC 3 14 . . . 17

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Filtering genes by absolute expression

› Number of reads per gene per sample (alternatively per total samples)

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Transformation of expression values

Adjust for technical differences like total read count per sample

› Depending in the downstream analysis tool

› Some tools read absolute count and does transformation/normalization

› Relative abundance (RA)

› Log transformation of RA

› Reads per Kilobase per Million Reads (RPKM; Mortazavi et al, 2008)

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Carry on with gene expression analysis!

› Differential expression

› Clustering

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Some general considerations

› Linux is the best environment for handling and analyzing huge data files

› Learning some of the Linux commands can be helpful (grep, sed, cut, awk)

› Learning Perl/R programming can also help data text file processing

› Use batch files to build data processing pipelines (documentation and re-use)

› Get use to shift between various tools for processing, analyzing and visualization

› Check input/output files, you are responsible, not the software/script authors!

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Resources for NGS data

› Forum for discussing NGS data analysis: http://www.seqanswers.com

› Galaxy online tools: http://main.g2.bx.psu.edu

› NCBI Short Read Archive (SRA): http://trace.ncbi.nlm.nih.gov/Traces/sra

› Bioconductor packages for NGS:

http://www.bioconductor.org/help/workflows/high-throughput-sequencing ShortRead, Biostrings, edgeR, Rsamtools, biomaRt