Decoding encode
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Decoding ENCODE. Jim Kent University of California Santa Cruz. ENCODE Timeline. ENCyclopedia of Dna Elements. Attempt to catalog as many functional elements in human genome as possible using current technologies. Pilot project - finished 2007, covered 1% of genome.

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Decoding ENCODE

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Decoding encode

Decoding ENCODE

Jim Kent

University of California Santa Cruz

Encode timeline

ENCODE Timeline

  • ENCyclopedia of Dna Elements.

    • Attempt to catalog as many functional elements in human genome as possible using current technologies.

    • Pilot project - finished 2007, covered 1% of genome.

    • Production project - ramping up now. Genome-wide. Should have major amounts of data in 6 months.

Encode experiments

ENCODE Experiments

  • Chromatin state:

    • DNA Hypersensitivity assays

    • Chromatin Immunoprecipitation (ChIP)

      • Histones in various methylation states

      • Sequence-specific transcription factors

    • DNA methylation

    • Chromatin conformation capture (5C)

  • Functional RNA discovery

    • Nuclear & cytoplasmic, short & long

    • RNA Immunoprecipitation

  • Comparative Genomics

  • Human curated gene annotation

Role of ucsc

Role of UCSC

  • Display data in context of what else is known on the UCSC Genome Browser and in other tools.

  • Facilitate analysis of the data with both Web-based and command line tools.

A peek at the pilot project

A Peek at the Pilot Project

Encode pilot data at genome ucsc edu

ENCODE pilot data at

Correlation at gene starts in enr221

Correlation at gene starts in enr221

Transcription at enm221

Transcription at enm221

Encode chromatin immunoprecipitation

ENCODE Chromatin Immunoprecipitation

Scientific highlights of pilot

Scientific Highlights of Pilot

  • Transcription:

    • Lots of transcription outside of known genes.

    • Outside of known genes transcribed areas not very well conserved across species.

    • Lots of rare splice variants, also poorly conserved.

  • DNA/Protein Interactions

    • Good correlation between histone markers, gene starts, and _active_ transcription.

    • Lots of “occupied transcription factor binding sites” not conserved, near promoters etc.

  • Biological noise?

    • Main controversy was whether to explain much of the data as “biological noise” that was tolerated but not necessary for function.

From pilot to production phase

From Pilot to Production Phase

Encode production phase

ENCODE Production Phase

  • Moving from microarray based assays to assays based on next-generation sequencing. (ChIP-chip to ChIP-seq)

  • Genome-wide rather than regional.

  • Broader set of cell lines used more consistently between labs.

  • Broader set of antibodies.

  • Some new technology development continues.

Encode cell lines

ENCODE Cell Lines

  • Tier 1 - used in ALL experiments

    • GM12878 (lymphoblastoid cell line)

    • K562 (chronic myeloid leukemia)

  • Tier 2 - used in most experiments

    • HepG2 (hepatocellular carcinoma)

    • Hela-S3 (cervical carcinoma)

    • HUVEC (umbilical vein endothelial cells)

    • Keratinocyte (normal epidermal cells)

    • Likely will do an embryonic stem cell too.

  • Tier 3 - used in one or two experiments

    • Many of these for assays such as DNAse hypersensitivity, RNA measurements where don’t have to do separate experiment for each antibody.

Simple model of eukaryotic transcription regulation

Simple Model of Eukaryotic Transcription Regulation

  • Initially chromatin “opened” to allow transcription factors to access DNA

  • Multiple transcription factors bind to DNA in combination.

    • Most factors have such small DNA binding sites that by themselves they are not specific or the binding even stable

  • The right combination of factors in open chromatin leads to active transcription starting at the initiation complex.

  • With the ENCODE experiments we can directly test most aspects of this model.

Chromatin experiments

Chromatin Experiments

  • In general applied across a large number of cell lines.

  • DNAseI hypersensitivity

  • Formaldehyde Assisted Isolation of Regulatory Elements

  • Methylation of CpG Islands

  • ChIP-seq of relevant factors

    • H3K4me1,2,3 H3K9me3 H4K20me3, H3K27me3, H3K36me3, RPol-II, etc.

Transcription factor chip

Transcription Factor ChIP

  • Many antibodies in modest number of cell lines.

  • Limited by good antibodies, hope for 100 or more.

  • Current good antibodies include

    • E2F1, E2F4, E2F6, KAP1, L3MBTL2, STAT1, CtBP1, CtBP2, SETDB1, ZNF180, ZNF239, ZNF263, ZNF266, ZNF317, ZNF342

  • Part of project pipeline for raising and testing antibodies.

Rna measurement

RNA measurement

  • RNA-seq of poly-A selected RNA to measure mRNA levels in many cell lines.

  • Sequencing of G-cap selected tags (CAGE)

  • Sequencing 5’ and 3’ ends (paired end tags)

  • Measurement of RNAs of several types in several cell compartments of a few cell lines.

    • Long/short, polyA/nonPolyA, associated with proteins/not associated with proteins

    • Nucleus, cytosol, polysomes, chromatin, nucleolus

New pilot projects starting to sprout

New Pilot Projects Starting to Sprout

New pilot projects

New Pilot Projects

  • Immunoprecipitation of RNA binding proteins/RNA sequencing.

  • Mapping silencers and enhancers with transient transfection assays

  • Computational identification of active promoters

  • Deep comparative sequencing in targeted regions and conservation analysis.

  • Chromatin Conformation Capture Carbon Copy (5C) to capture long range regulatory elements and their targets.

Encode timeline1

ENCODE Timeline

  • Grants funded for 4 years starting Sept 2007.

  • First production data just now starting to roll into UCSC, not quite ready for public display.

  • Data should accumulate quickly over next few years.

Data release policy

Data Release Policy

  • Once have reproducible data (where at least 2 of 3 replicates agree) should be released to public within a month.

  • Data is still considered pre-publication!

    • Ok to publish a paper using data on a few genes.

    • Please wait for consortium papers before papers doing full genome analysis.

    • Anyone can join ENCODE consortium analysis group to help us write the papers.

    • We just have ~1 year after data release to write papers, after that fair game to publish full genome analysis.

    • If in doubt please contact consortium via UCSC.

Web works for mice and men

Web Works for Mice and Men

Mouse es cell chromatin ip

Mouse ES Cell Chromatin IP

  • Brad Bernstein lab ChIP-seq based experiment on methylated histones now on UCSC Genome Browser.

  • Shows some of the user interfaces that will be used for the ENCODE data

Decoding encode

List of mouse chromatin subtracks….

Decoding encode

Signal densities of entire mouse chromatin data set.

The unending quest for genes

The unending quest for genes

Gencode project

Gencode Project

  • Project to define structure (exons and introns) for all common splice varients of all genes.

  • Human curators merge many lines of evidence including

    • Computational gene predictions

    • RNA/DNA alignments

    • Paired end tags

    • Cross-species alignments

    • Possibly chromatin state data

  • PI is Tim Hubbard

  • Much of the work done by Havana group

Data mining with table browser

Data Mining with Table Browser

Table browser

Table Browser

  • Complete access to UCSC Database with results in tab-delimited format

  • Method for creating “custom tracks” by combining and filtering existing tracks.

  • Sample query - getting a table of Ensembl gene coordinates and associated Superfamily annotations.

Decoding encode

Selected fields from related tables results: Ensemble Gene (ensGene) and Superfamily Description (sfDescription).

Table browser filters

Table Browser Filters

  • Getting list of Ensembl genes that have SH3 domains.

Table browser intersection

Table Browser Intersection

  • Getting list of Ensembl genes that don’t intersect UCSC Known Genes

Custom track output

Custom Track Output

  • Useful for visualizing results of queries in genome browser

  • The way to produce more complex queries.

  • Here we look at how well genes that are Ensembl but not UCSC are conserved across species.

Decoding encode

681/3329 (20%) of Ensemble not known also not conserved

1728/33,666 (5%) of Ensembl in general not conserved

Ucsc gene sorter

UCSC Gene Sorter

  • Swiss army knife for dealing with gene sets.

  • Hilights relationships and connections between genes.

  • Powerful data mining tool.

Decoding encode

Cytochrome P450 - a gene family important in drug metabolism.

The family is related in many ways. Sorted by protein homology

Decoding encode

Various sorting methods let you focus on different types

of relationships between genes.

Decoding encode

Sorting by gene distance is a quick way to browse candidate

genes in a region.

Decoding encode

Clicking on row # or gene name selects that gene.

Decoding encode

Configuration page controls column order and display options.

Decoding encode

Also you can upload your own columns here.

Decoding encode

Controlling expression display

Decoding encode

GNF Atlas 2 column in ‘median of replicates’ mode. Actual

Column includes 79 tissues, slide only fits first half.

Decoding encode

Sorting based on expression similarity to selected gene.

Decoding encode

The filters page turns the Family Browser into a powerful

data mining tool.

Decoding encode

Candidate Pancreatic Islet Membrane Genes

GO-annotated membrane proteins that are expressed at least 8X in pancreatic islets cells and no more than 4X elsewhere outside of pancreas and central nervous system. These might be good candidates for targets of the autoimmune response that can cause Type I diabetes.

Direct data access

Direct Data Access

Ftp or http download

FTP or HTTP Download

  • Sequence

  • Multiple genome alignments

  • “Wiggle” track data.

  • Database as tab-separated files

  • Follow downloads link from

  • Via

Public mysql access

Public MySQL Access

  • Query mirror of our database directly

    • Host:

    • User: genome

    • No password needed

  • Best to use table browser to find relevant tables in many cases.

  • Some tables are split by chromosomes

    • chr1_est, chr2_est, etc.

  • Some data (genome sequence, multiple alignments, wiggles) are in files just referenced by SQL tables.

  • For some purposes easier to use via UCSC C library code than via SQL.

The sordid details of the ucsc genome informatics code base

The Sordid Details of the UCSC Genome Informatics Code Base

Download via

Many modules require MySQL to be installed.

Lagging edge software

Lagging Edge Software

  • C language - compilers still available!

  • CGI Scripts - portable if not pretty.

  • SQL database - at least MySQL is free.

Problems with c

Problems with C

  • Missing booleans and strings.

  • No real objects.

  • Must free things

Coping with missing data types in c

Coping with Missing Data Types in C

  • #define boolean int

  • Fixing lack of real string type much harder

    • lineFile/common modules and autoSql code generator make parsing files relatively painless

    • dyString module not a horrible string ‘class’

Object oriented programming in c

Object Oriented Programming in C

  • Build objects around structures.

  • Make families of functions with names that start with the structure name, and that take the structure as the first argument.

  • Implement polymorphism/virtual functions with function pointers in structure.

  • Inheritance is still difficult. Perhaps this is not such a bad thing.

Decoding encode

struct dnaSeq

/* A dna sequence in one-letter-per-base format. */


struct dnaSeq *next; /* Next in list. */

char *name; /* Sequence name. */

char *dna; /* a’s c’s g’s and t’s. Null terminated */

int size; /* Number of bases. */


struct dnaSeq *dnaSeqFromString(char *string);

/* Convert string containing sequence and possibly

* white space and numbers to a dnaSeq. */

void dnaSeqFree(struct dnaSeq **pSeq);

/* Free dnaSeq and set pointer to NULL. */

void dnaSeqFreeList(struct dnaSeq **pList);

/* Free list of dnaSeq’s. */

Decoding encode

struct screenObj

/* A two dimensional object in a sleazy video game. */


struct screenObj *next; /* Next in list. */

char *name; /* Object name. */

int x,y,width,height; /* Bounds of object. */

void (*draw)(struct screenObj *obj); /* Draw object */

boolean (*in)(struct screenObj *obj, int x, int y);

/* Return true if x,y is in object */

void *custom; /* Custom data for a particular type */

void (*freeCustom)(struct screenObj *obj);

/* Free custom data. */


#define screenObjDraw(obj) (obj->draw(obj))

/* Draw object. */

void screenObjFree(struct screenObj **pObj);

/* Free up screen object including custom part. */

Relational databases

Relational Databases

  • Relational databases consist of tables, indices, and the Structured Query Language (SQL).

  • Tables are much like tab-separated files:#chrom start end name strand score chr22 14600000 14612345 ldlr + 0.989 chr21 18283999 18298577 vldlr - 0.998Fields are simple - no lists or substructures.

  • Can join tables based on a shared field. This is flexible, but only as fast as the index.

  • Tables and joins are accessed a row at a time.

  • The row is represented as an array of strings.

Converting a row to object

Converting A Row to Object

struct exoFish *exoFishLoad(char **row)

/* Load a exoFish from row fetched with select * from exoFish

* from database. Dispose of this with exoFishFree(). */


struct exoFish *ret;


ret->chrom = cloneString(row[0]);

ret->chromStart = sqlUnsigned(row[1]);

ret->chromEnd = sqlUnsigned(row[2]);

ret->name = cloneString(row[3]);

ret->score = sqlUnsigned(row[4]);

return ret;


Motivation for autosql

Motivation for AutoSql

  • Row to object code is tedious at best.

  • Also have save object, free object code to write.

  • SQL create statement needs to match C structure.

  • Lack of lists without doing a join can seriously impact performance and complicate schema.

Autosql data declaration

AutoSql Data Declaration

table exoFish

"An evolutionarily conserved region (ecore) with Tetroadon"


string chrom; "Human chromosome or FPC contig"

uint chromStart; "Start position in chromosome"

uint chromEnd; "End position in chromosome"

string name; "Ecore name in Genoscope database"

uint score; "Score from 0 to 1000"


See autoSql.doc for more details.

Occasionally useful tools

Occasionally useful tools

Unix command line

Unix Command Line

  • BLAT - RNA/DNA and DNA/DNA alignment.

  • featureBits - figure out number of bases covered by a track or intersection of tracks, output track intersections.

  • htmlCheck - check html tables and other basic web page stuff. Look at form variables.

  • dbSnoop - summarize a MySQL database.

  • autoSql - generate serialization C code for relational databases/tab-separated files.

  • autoXml - generate XML parsers

  • xmlToSql/sqlToXml - convert between XML and relational database representations

  • parasol - manage jobs on computer cluster

C library modules

C Library Modules

  • hdb - access UCSC genome database

  • jksql - access SQL databases

  • htmlPage - parse web pages, submit forms

  • readers/writers for maf, psl, chain, net, bed, 2bit other formats used at UCSC

  • rangeTree & binRange - fast interval intersection tools

  • Hashes, lists, trees, etc.

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