Visualizing your experimental results with the Integrative Genomics Browser (IGV)

BaRC Hot Topics – April 25, 2017

Bioinformatics and Research Computing Whitehead Institute

http://barc.wi.mit.edu/hot_topics/

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Getting IGV http://www.broadinstitute.org/igv

Igv*.jnlp: max-heap-size

Why IGV?

• Faster navigation or browsing • Run it locally on your desktop • Special Datasets:

– ENCODE: human(hg19), mouse (mm9) – 1000 Genomes: human (hg19) – Body Map 2.0: human (hg19) – The Cancer Genomics Atlas: human (hg19)

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UCSC genome browser vs IGV

• Some tracks are unique to one of them • IGV navigates faster because it runs locally • IGV allows you to create a new genome • It’s easier to jump to view the same region in

another species with the UCSC browser

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IGV Interface

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1. Tool Bar 2. Chr. Ideogram 3. Ruler 4. Track Data 5. Features 6. Track Names 7. Attributes

Viewing NGS Data

• BAM format recommended

• At low resolution only coverage is shown

• At higher resolution, reads are shown including where bases differ

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sam/bam format

Col Field Description 1 QNAME Query template/pair NAME

2 FLAG bitwise FLAG 3 RNAME Reference sequence NAME

4 POS 1-based leftmost POSition/coordinate of clipped sequence

5 MAPQ MAPping Quality (Phred-scaled)

6 CIAGR extended CIGAR string 7 MRNM Mate Reference sequence NaMe (‘=’ if same as RNAME)

8 MPOS 1-based Mate POSistion 9 TLEN inferred Template LENgth (insert size)

10 SEQ query SEQuence on the same strand as the reference

11 QUAL query QUALity (ASCII-33 gives the Phred base quality)

12+ OPT variable OPTional fields in the format TAG:VTYPE:VALUE

Header: begins with @ Alignment:

Example: NA18502_YRI.sra.736765 272 chr1 14641 0 46M * 0 0 ATGTCAGAGCAATGGCCCAAGTCTGGGTCTGGGGGGGAAGGTGTCA :3;.9AB@A?<7-?????:@AAA9=BBB?7ABBCBBCBBBCABBBB AS:i:-3 XN:i:0 XM:i:1 XO:i:0 XG:i:0NM:i:1 MD:Z:12C33 YT:Z:UU XS:A:- NH:i:5 CC:Z:chr12 CP:i:90939 HI:i:0

Example: @SQ SN:chr1 LN:197195432

IGV: also needs to have indexed bam file (.bam.bai)

ChIP-seq Demo (hg 19) • Data: File > Load from ENCODE

• ENCODE GATA1 K562 SC-266 • Peaks (bed format): about 7k peaks

• Find Motif: • GATA (Tools > Find Motif)

• View multiple regions: – Top 50 peaks by enrichment score from Encode K562

Gata1 chip-seq data • wgEncodeSydhTfbsK562Gata1bIggmusPk.narrowPeak_top50.bed • Regions > Import Regions

• Save session • Find overlapped regions with IGV bedtools on tak UNIX server

• Tools > BEDTools

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RNA-seq Demo (hg19) • Data:

• breast, brain from Human body Map • Pair end reads File > Load From Server > Body Map > Alignments > Merged 50bp and 75bp (hiSeq)

• View pair end reads: eg: EIF4A1 • Control (command) + click while not in “view as Pairs”

• View multiple genes side-by-side • Regions > Gene Lists > Microglia marker or your list

• View splice junctions with Sashimi plot: eg: PTPRC • View Preferences: (view > Preferences )

• Coverage mismatch threshold • Visibility range threshold • Downsample reads

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Others

• View variant mutation files (not covered) • View methylation data: (not covered)

– BS-Seq, bisulfite sequencing – RRBS-Seq, reduced representation bisulfite

sequencing

• Create .genome file – Eg: Toxoplasma gondii:

• ToxoDB-10.0_TgondiiGT1_Genome_Chr_Only.fasta

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Covered in this session

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tak

• Right-click

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View -> Preferences…

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More Information

• IGV User Guide: – http://www.broadinstitute.org/igv/book/export/h

tml/6 – http://www.broadinstitute.org/software/igv/dow

nload – Insertion/deletion/invertion/duplication

• ftp://ftp.broadinstitute.org/distribution/igv/COUNTWAY/IGV_Countway_2011_no_demos.pdf

• Video: – https://www.youtube.com/watch?v=IILfC3Uc6Vo

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