Sunday, May 18, 2014

Re: Uppsala ICM seminar -- request for title and abstract

Great Mark!

Thanks,

Jan

Jan Komorowski, Professor of Bioinformatics
Program in Computational and Systems Biology
Department of Cell and Molecular Biology
Uppsala University
jan.komorowski@icm.uu.se
and
Institute of Computer Science, PAN, Warsaw






On 17 maj 2014, at 22:14, Mark Gerstein <mark@gersteinlab.org> wrote:

> The ENCODE and modENCODE consortia have generated a resource
> containing large amounts of transcriptomic data, extensive mapping of
> chromatin states, as well as the binding locations of over 300
> transcription-regulatory factors for human, worm and fly. We performed
> extensive data integration by constructing genome-wide co-expression
> networks and transcriptional regulatory models, revealing fundamental
> principles of transcription and network organization that are
> conserved across the three highly divergent animals. In particular:
>
> (1) We developed a novel cross-species clustering algorithm to
> integrate the co-expression networks of the three species, resulted at
> conserved modules shared between the organisms. These modules are
> enriched in developmental genes and exhibited hourglass behavior. They
> were then used to align the stages in worm and fly development,
> finding the normal embryo-to-embryo and larvae-to-larvae pairings in
> addition to a novel pairing between worm embryo and fly pupae.
>
> (2) We developed a global optimization algorithm to examine the
> hierarchical organization of the regulatory network. We found that,
> despite extensive rewiring of binding targets, high-level organization
> principles such as a three-layer heirarchy are conserved across the
> three species.
>
> (3) We found the gene expression levels in the organisms, both coding
> and non-coding, can be predicted consistently based on their upstream
> histone marks. In fact, a "universal model" with a single set of
> cross-organism parameters can predict expressionlevel for both protein
> coding genes and ncRNAs.
>
> (4) Finally, we found that the extent of the non-coding, non-canonical
> transcription is consistent between worm, fly and human.
>
> encodenets.gersteinlab.org
>
> ==
> cmptxn,i0se14

Saturday, May 17, 2014

Fwd: Uppsala ICM seminar -- request for title and abstract

The ENCODE and modENCODE consortia have generated a resource
containing large amounts of transcriptomic data, extensive mapping of
chromatin states, as well as the binding locations of over 300
transcription-regulatory factors for human, worm and fly. We performed
extensive data integration by constructing genome-wide co-expression
networks and transcriptional regulatory models, revealing fundamental
principles of transcription and network organization that are
conserved across the three highly divergent animals. In particular:

(1) We developed a novel cross-species clustering algorithm to
integrate the co-expression networks of the three species, resulted at
conserved modules shared between the organisms. These modules are
enriched in developmental genes and exhibited hourglass behavior. They
were then used to align the stages in worm and fly development,
finding the normal embryo-to-embryo and larvae-to-larvae pairings in
addition to a novel pairing between worm embryo and fly pupae.

(2) We developed a global optimization algorithm to examine the
hierarchical organization of the regulatory network. We found that,
despite extensive rewiring of binding targets, high-level organization
principles such as a three-layer heirarchy are conserved across the
three species.

(3) We found the gene expression levels in the organisms, both coding
and non-coding, can be predicted consistently based on their upstream
histone marks. In fact, a "universal model" with a single set of
cross-organism parameters can predict expressionlevel for both protein
coding genes and ncRNAs.

(4) Finally, we found that the extent of the non-coding, non-canonical
transcription is consistent between worm, fly and human.

encodenets.gersteinlab.org

==
cmptxn,i0se14

Fwd: CSHL Meeting Abstract Submission

Thank you for submitting your abstract for Systems Biology: Global
Regulation of Gene Expression 2014 .

Comparative network analysis of ENCODE and modENCODE data

Mark Gerstein1,2, Koon-Kiu Yan1,2, modENCODE/ENCODE transcriptome group1

1Yale University, Program of Computational Biology and Bioinformatics,
New Haven, CT, 2Yale University, Molecular Biophysics and
Biochemistry, New Haven, CT

The ENCODE and modENCODE consortia have generated a resource
containing large amounts of transcriptomic data, extensive mapping of
chromatin states, as well as the binding locations of over 300
transcription-regulatory factors for human, worm and fly. We performed
extensive data integration by constructing genome-wide co-expression
networks and transcriptional regulatory networks, revealing
fundamental principles of transcription and network organization that
are conserved across the three highly divergent animals. First, we
developed a novel cross-species clustering algorithm to integrate the
co-expression networks of the three species, resulted at conserved
modules shared between the organisms. These modules are enriched in
developmental genes and exhibited hourglass behavior. They were then
used to align the stages in worm and fly development, finding the
normal embryo-to-embryo and larvae-to-larvae pairings in addition to a
novel pairing between worm embryo and fly pupae. Second, we defined a
new score to quantify the degree of hierarchy (preponderance of
downward information flow) of a network, and developed a global
optimization algorithm to compare the hierarchical organization of the
three species. We found that, despite extensive rewiring of binding
targets, high-level organization principles like the hierarchical
structures are conserved across three species. Finally, we found the
gene expression levels in the organisms, both coding and non-coding,
can be predicted consistently based on their upstream histone marks.
In fact, a "universal model" with a single set of cross-organism
parameters can predict expression level for both protein coding genes
and ncRNAs. The algorithms introduced by this study can be easily
applied to other model datasets such as those from yeast.

i0cshsb