Introduction to genome biology

Transcription

1 Introduction to genome biology Lisa Stubbs Deep transcritpomes for traditional model species from ENCODE (and modencode) Deep RNA-seq and chromatin analysis on 147 human cell types, as well as tissues, developmental stages from model species Good coverage of isoforms, genes, noncoding RNAs, micrornas Most (75%) of the genomes of all species studied are transcribed Significant overlap in transcript expression different cells Only 7% are cell-type specific, although RNA levels can differ widely The vast majority of transcripts are non-coding functional or not? Most metazoan genes are alternatively spliced, multiple ways transcripts per human gene on average alternative promoters, 3 ends and alternative coding sequences 1

2 We ve found most genes; but what about the rest of the genome? Genome size* 12 Mb 95 Mb 170 Mb 1500 Mb 2700 Mb 3200 Mb #coding genes ~7000 ~20000 ~14000 ~26000 ~23000 ~21000 # transcripts ~7000 ~50000 ~29000 ~53000 ~93000 ~ Kb/gene 1714 bp 4750 bp bp 57,692 bp bp bp *data taken from ENSEMBL genome browser Most notably: Coding gene number is relatively constant in metazoans, BUT Number of alternative transcripts per gene and Gene density are not Each gene gives rise to many more isoforms: protein sequence diversity Much more non-coding DNA, including gene regulatory DNA Most traditional studies have focused on promoters and nearby (proximal) enhancers Promoter regions are most likely to be involved in recruiting RNA polymerase and related proteins TATA binding proteins (TAFs) General transcription factors (GTFs) Mediator complexes Some transcription factors (TF) are also more likely to be found at promoter sites SP1, E2F family are classical examples BUT, most other metazoan TFs are found preferentially at distant sites Introns, intergenic regions Some may be 100s or 1000s of bp from the target promoter, or even embedded within neighboring genes 2

3 Transcription factors and their binding sites Most known TFs have short, and variable binding sites, e.g. YY1 SP1 Mzf1 BUT The probability of finding a string such as the Yy1 core (even as a simple string, rather than a matrix) is (1/4) 4 = 1/256 bp! Most TFBS are not much more specific than this! So, how to raise the probability that the site you find is functional? 1. Interspecies conservation: sites that are found in similar locations in diverse species are more likely to be functional 2. Site clustering: most TFBS form homo- or heterodimers that significantly stabilize binding and influence function 3. Location within regions that are known to be in an open state in the cell type and conditions of interest How to find the regulatory needles in the haystack? Vertebrate genomes are mostly non-coding ~2% coding; ~5% noncoding and evolutionarily conserved (at the DNA sequence alignment level) Websites to view pre-aligned sequence conservation levels abound; e.g. the ECR browser zpicture provides a do it yourself tool for alignments of up to 1Mb; Both tools allow detection of conserved TFBS from Transfac, Jaspar, and other databases 3

4 Many regulatory elements interact with distant promoters (and each other) through long-range chromatin loops Regulatory elements are essentially docking sites for specific types of DNA-binding proteins These proteins and co-factors mediate protein: protein interactions across the loops Most gene regulatory activities are coordinated by gangs of interacting and competing proteins all vying for access to the same chromatin sites May or may not regulate the nearest genes Not all these sites are overtly sequence-conserved How to find active elements? Chromatin immunoprecipitation with TF and histone-modification antibodies Chromatin and attendant proteins are chemically crosslinked (lightly) using formaldehyde Crosslinking will also attach proteins to each other, so that detection of long range and secondary chromatin interactions is inevitable Cross-linked chromatin is randomly sheared by sonication (average fragment size bp) + Sonicated fragments in solution are exposed to a protein-specific antibody Antibody is retrieved with DNA still attached DNA is released with salt and heat (reverses the crosslinks) Library is created for sequencing : ligation of tags and light PCR amplification ATGGCCTTAACGA.. Sequenced directly e.g. illumina sequencing 4

5 Sequence-based ChIP approaches Harness ChIP, DNAse sensitivity, and other assays, to Illumina sequencing ChIP enriched DNA is ligated to Illumina linkers and sequenced directly If you experiment works, you ve enriched a very small fraction of the genome: Requires a lot of input chromatin! Traditional methods need ~10^7 cells per experiment!! Critical step is an efficient, selective antibody (and very few exist) ChIP computational issues Sequence is read from randomly position ends of multiple, overlapping randomly sheared fragments Reads will be scattered around a distance ~2X shear fragment length; ChIP seq reads surround but may not contain the DNA binding site Computational tools (like MACS) need to join adjacent sets of read peaks and define a shift distance between read peaks to determine a summit Seq reads ChIP fragments Binding site 5

6 Analytical considerations Genomic neighborhoods Shear efficiency is not really random Some genomic regions are fragile and sensitive; some are protected Chromatin-matched, co-sheared controls are essential Peak width Transcription factors typically yield sharp peaks; chromatin marks are sometimes broader and more diffuse User-friendly tools Zinba Rashid et al. Genome Biology 2011 PMID: (Jason Lieb lab) Best for diffuse or varied peaks Requires R programming language, but there are excellent help files MACS: Zhang et al, Genome Biology 2008, Feng et al. 2012, Nat Procols PMID: (Xiaole Liu lab); MACS1 is best for sharp peaks (TFs); will break diffuse peaks into smaller regions MACS2 is designed to allow broad- or sharp-peak detection Available in Galaxy and simple to use on command line MACS 2 allows settings optimized to detect broad or sharp peaks MACS creates wiggle files to view your sequencing reads and background in the UCSC browser environment 6

7 Data from ChIP with TFs, modified Histones, and other proteins are available as Tables in the UCSC genome browser ( From Hoffman et al, Nucl Acid Res 41:827, 2013 Genome Biology Topic overview Lectures Ross Hardison epigenomic features, assays and data integration David Hawkins Chromatin states, biological applications James Taylor Higher dimension chromatin structure Lisa Stubbs Integrating data for biological inference: Basics of Expression correlation Workshops Bowtie and ChIPseq on Galaxy Bowtie and ChIPseq/Tophat and Cufflinks on the command line Peaks to features in Galaxy How to for ECR browser and Z-picture (optional) Simple methods for expression correlation: Cluster and Cytoscape 7