Current Research Interests for Mark Gerstein - Revision history

Mbg: /* Analysis of Diverse Networks */

2016-11-30T02:20:32Z

Analysis of Diverse Networks

← Older revision		Revision as of 02:20, 30 November 2016
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	Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.		Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.
	See: [http://papers.gersteinlab.org/subject/regnet networks' papers].		See: [http://papers.gersteinlab.org/subject/regnet networks papers].


	====Genomics at the Forefront of Data Science====		====Genomics at the Forefront of Data Science====

Mbg: /* Genomics at the Forefront of Data Science */

2015-11-02T16:13:46Z

Genomics at the Forefront of Data Science

← Older revision		Revision as of 16:13, 2 November 2015
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	====Genomics at the Forefront of Data Science====		====Genomics at the Forefront of Data Science====

	Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on simulation, machine learning, and ~~database~~ design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://kbase.us KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], ~~TCGA prostate~~, the [http://exrna.org exRNA Consortium] and the [http://mendelian.org/ Centers for Mendelian Genomics].		Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on applying technical approaches in simulation, machine learning, and knowledgebase design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://kbase.us KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], [http://pancancer.info PCAWG], the [http://exrna.org exRNA Consortium] and the [http://mendelian.org/ Centers for Mendelian Genomics].

Mbg: /* Protein Structure and Function: Macromolecular Motions */

2015-10-21T19:29:56Z

Protein Structure and Function: Macromolecular Motions

← Older revision		Revision as of 19:29, 21 October 2015
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	====Protein Structure and Function: Macromolecular Motions====		====Protein Structure and Function: Macromolecular Motions====

	While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).		While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. We have a particular focus on loss-of-function mutations. Moreover, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).
	See: [http://papers.gersteinlab.org/subject/motions molecular motion] and [http://papers.gersteinlab.org/subject/volumes structure papers].		See: [http://papers.gersteinlab.org/subject/motions molecular motion] and [http://papers.gersteinlab.org/subject/volumes structure papers].


	====Analysis of Diverse Networks====		====Analysis of Diverse Networks====

Mbg: /* Human Genome Annotation: Processing Next-Gen Sequencing Data */

2015-10-21T19:28:23Z

Human Genome Annotation: Processing Next-Gen Sequencing Data

← Older revision		Revision as of 19:28, 21 October 2015
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	====Human Genome Annotation: Processing Next-Gen Sequencing Data====		====Human Genome Annotation: Processing Next-Gen Sequencing Data====

	After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription ~~(often called TARs)~~. We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.		After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within a biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription. We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.
	See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].		See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].


	====Comparative Genomics: Pseudogenes as Molecular Fossils====		====Comparative Genomics: Pseudogenes as Molecular Fossils====

Mbg: /* Genomics at the Forefront of Data Science */

2014-03-08T06:05:22Z

Genomics at the Forefront of Data Science

← Older revision		Revision as of 06:05, 8 March 2014
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	====Genomics at the Forefront of Data Science====		====Genomics at the Forefront of Data Science====

	Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on simulation, machine learning, and database design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://~~genomicscience~~.~~energy.gov/compbio/index.shtml#page=news~~ KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], TCGA prostate, and the [http://mendelian.org/ Centers for Mendelian Genomics].		Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on simulation, machine learning, and database design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://kbase.us KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], TCGA prostate, the [http://exrna.org exRNA Consortium] and the [http://mendelian.org/ Centers for Mendelian Genomics].


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	Personal genomics also acts as a bridge connecting the biological sciences to larger issues facing other big-data disciplines. For instance, data mining generally poses questions related to privacy. We study the fundamental privacy implications of mining personal genomes, which contain immutable information, shared amongst relatives that will be increasingly revealing in generations to come. Also, we have examined how general knowledge-representation issues associated with publishing and digital libraries relate to biological databases. We envision a future of structured literature, with less distinction between databases and journals.		Personal genomics also acts as a bridge connecting the biological sciences to larger issues facing other big-data disciplines. For instance, data mining generally poses questions related to privacy. We study the fundamental privacy implications of mining personal genomes, which contain immutable information, shared amongst relatives that will be increasingly revealing in generations to come. Also, we have examined how general knowledge-representation issues associated with publishing and digital libraries relate to biological databases. We envision a future of structured literature, with less distinction between databases and journals.


	===References===		===References===

Mbg at 09:51, 2 September 2013

2013-09-02T09:51:22Z

← Older revision		Revision as of 09:51, 2 September 2013
Line 35:		Line 35:

	Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on simulation, machine learning, and database design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://genomicscience.energy.gov/compbio/index.shtml#page=news KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], TCGA prostate, and the [http://mendelian.org/ Centers for Mendelian Genomics].		Overall the Gerstein lab acts a connector, bringing quantitative approaches from disciplines such as computer science and statistics to bear on practical questions and large-scale data in molecular biology. In particular, we have focused on simulation, machine learning, and database design. Often, we carry out our work in multi-disciplinary teams. Some of the key collaborative efforts that we are involved in include [http://genomicscience.energy.gov/compbio/index.shtml#page=news KBase], [http://www.brainspan.org/ Brainspan], [http://encodeproject.org/ENCODE/ ENCODE], [http://www.modencode.org/ modENCODE], [http://www.1000genomes.org/ 1000 Genomes], TCGA prostate, and the [http://mendelian.org/ Centers for Mendelian Genomics].


	As a discipline, genomics is an exemplar for using big data to construct a resource and answer questions. Consequently, it is at the forefront in the emerging field of data science and provides an ideal training for future data scientists.		As a discipline, genomics is an exemplar for using big data to construct a resource and answer questions. Consequently, it is at the forefront in the emerging field of data science and provides an ideal training for future data scientists.


	Personal genomics also acts as a bridge connecting the biological sciences to larger issues facing other big-data disciplines. For instance, data mining generally poses questions related to privacy. We study the fundamental privacy implications of mining personal genomes, which contain immutable information, shared amongst relatives that will be increasingly revealing in generations to come. Also, we have examined how general knowledge-representation issues associated with publishing and digital libraries relate to biological databases. We envision a future of structured literature, with less distinction between databases and journals.		Personal genomics also acts as a bridge connecting the biological sciences to larger issues facing other big-data disciplines. For instance, data mining generally poses questions related to privacy. We study the fundamental privacy implications of mining personal genomes, which contain immutable information, shared amongst relatives that will be increasingly revealing in generations to come. Also, we have examined how general knowledge-representation issues associated with publishing and digital libraries relate to biological databases. We envision a future of structured literature, with less distinction between databases and journals.

Public at 18:37, 30 July 2013

2013-07-30T18:37:04Z

Public at 18:36, 30 July 2013

2013-07-30T18:36:24Z

← Older revision		Revision as of 18:36, 30 July 2013
Line 6:		Line 6:
	We are involved in finding variants in personal genomes. We focus on particular types of variants, which involve the re-arrangement of large blocks of the genome (structural variation). It is believed that structural variants involve as many nucleotides in the genome as the better-known SNPs. Moreover, re-arrangements are very prevalent in genomic diseases such as cancer, and we have developed tools for identifying them (e.g. using split reads and fusion genes).		We are involved in finding variants in personal genomes. We focus on particular types of variants, which involve the re-arrangement of large blocks of the genome (structural variation). It is believed that structural variants involve as many nucleotides in the genome as the better-known SNPs. Moreover, re-arrangements are very prevalent in genomic diseases such as cancer, and we have developed tools for identifying them (e.g. using split reads and fusion genes).
	See: [http://papers.gersteinlab.org/subject/sv SV papers].		See: [http://papers.gersteinlab.org/subject/sv SV papers].


	====Human Genome Annotation: Processing Next-Gen Sequencing Data====		====Human Genome Annotation: Processing Next-Gen Sequencing Data====

Public at 17:52, 30 July 2013

2013-07-30T17:52:54Z

← Older revision		Revision as of 17:52, 30 July 2013
Line 10:		Line 10:

	After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.		After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.
	See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen ~~papers~~] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].		See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].

	====Comparative Genomics: Pseudogenes as Molecular Fossils====		====Comparative Genomics: Pseudogenes as Molecular Fossils====
Line 20:		Line 20:

	While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).		While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).
	See: [http://papers.gersteinlab.org/subject/motions molecular motion ~~papers~~], [http://papers.gersteinlab.org/subject/volumes structure papers].		See: [http://papers.gersteinlab.org/subject/motions molecular motion] and [http://papers.gersteinlab.org/subject/volumes structure papers].

	====Analysis of Diverse Networks====		====Analysis of Diverse Networks====

Public at 17:49, 30 July 2013

2013-07-30T17:49:22Z

← Older revision		Revision as of 18:37, 30 July 2013
Line 12:		Line 12:
	After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.		After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.
	See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].		See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].


	====Comparative Genomics: Pseudogenes as Molecular Fossils====		====Comparative Genomics: Pseudogenes as Molecular Fossils====
Line 17:		Line 18:
	Pseudogenes provide a contrasting annotation to binding sites and ncRNAs in being derived from comparative rather than functional genomics data. They provide information about human molecular history. We have developed methods for identifying them. We were one of the first groups to perform comprehensive surveys, illustrating the different pseudogene repertoires in different organisms. Moreover, we have found hints that some supposedly "dead" pseudogenes may actually harbor biochemical activity.		Pseudogenes provide a contrasting annotation to binding sites and ncRNAs in being derived from comparative rather than functional genomics data. They provide information about human molecular history. We have developed methods for identifying them. We were one of the first groups to perform comprehensive surveys, illustrating the different pseudogene repertoires in different organisms. Moreover, we have found hints that some supposedly "dead" pseudogenes may actually harbor biochemical activity.
	See: [http://papers.gersteinlab.org/subject/pseudogenes pseudogene papers].		See: [http://papers.gersteinlab.org/subject/pseudogenes pseudogene papers].


	====Protein Structure and Function: Macromolecular Motions====		====Protein Structure and Function: Macromolecular Motions====
Line 22:		Line 24:
	While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).		While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).
	See: [http://papers.gersteinlab.org/subject/motions molecular motion] and [http://papers.gersteinlab.org/subject/volumes structure papers].		See: [http://papers.gersteinlab.org/subject/motions molecular motion] and [http://papers.gersteinlab.org/subject/volumes structure papers].


	====Analysis of Diverse Networks====		====Analysis of Diverse Networks====
Line 27:		Line 30:
	Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.		Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.
	See: [http://papers.gersteinlab.org/subject/regnet networks' papers].		See: [http://papers.gersteinlab.org/subject/regnet networks' papers].


	====Genomics at the Forefront of Data Science====		====Genomics at the Forefront of Data Science====

← Older revision		Revision as of 17:49, 30 July 2013
Line 5:		Line 5:

	We are involved in finding variants in personal genomes. We focus on particular types of variants, which involve the re-arrangement of large blocks of the genome (structural variation). It is believed that structural variants involve as many nucleotides in the genome as the better-known SNPs. Moreover, re-arrangements are very prevalent in genomic diseases such as cancer, and we have developed tools for identifying them (e.g. using split reads and fusion genes).		We are involved in finding variants in personal genomes. We focus on particular types of variants, which involve the re-arrangement of large blocks of the genome (structural variation). It is believed that structural variants involve as many nucleotides in the genome as the better-known SNPs. Moreover, re-arrangements are very prevalent in genomic diseases such as cancer, and we have developed tools for identifying them (e.g. using split reads and fusion genes).
	See: [http://papers.gersteinlab.org/subject/sv SV papers]		See: [http://papers.gersteinlab.org/subject/sv SV papers].

	====Human Genome Annotation: Processing Next-Gen Sequencing Data====		====Human Genome Annotation: Processing Next-Gen Sequencing Data====

	After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.		After one has determined all of the variants in an individual’s genome, the next step is understanding what they mean. This involves genome annotation, where one places each base within biochemical context. Our focus has been on transcription-factor binding sites and non-coding RNAs (ncRNAs). We have carried out this effort by processing next-generation sequencing data (i.e. RNA-seq and ChIP-seq). We have developed tools to identify ncRNAs and regions of intragenic transcription (often called TARs). We also have developed methods for finding transcription-factor binding sites by processing ChIP-seq reads and using the level of this binding to predict statistically the expression of target genes.
	See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen papers], [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers]		See: [http://papers.gersteinlab.org/subject/ngtools Next-Gen papers] and [http://papers.gersteinlab.org/subject/rnaseq RNAseq papers].

	====Comparative Genomics: Pseudogenes as Molecular Fossils====		====Comparative Genomics: Pseudogenes as Molecular Fossils====

	Pseudogenes provide a contrasting annotation to binding sites and ncRNAs in being derived from comparative rather than functional genomics data. They provide information about human molecular history. We have developed methods for identifying them. We were one of the first groups to perform comprehensive surveys, illustrating the different pseudogene repertoires in different organisms. Moreover, we have found hints that some supposedly "dead" pseudogenes may actually harbor biochemical activity.		Pseudogenes provide a contrasting annotation to binding sites and ncRNAs in being derived from comparative rather than functional genomics data. They provide information about human molecular history. We have developed methods for identifying them. We were one of the first groups to perform comprehensive surveys, illustrating the different pseudogene repertoires in different organisms. Moreover, we have found hints that some supposedly "dead" pseudogenes may actually harbor biochemical activity.
	See: [http://papers.gersteinlab.org/subject/pseudogenes ~~Pseudogene~~ papers]		See: [http://papers.gersteinlab.org/subject/pseudogenes pseudogene papers].

	====Protein Structure and Function: Macromolecular Motions====		====Protein Structure and Function: Macromolecular Motions====

	While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).		While non-coding regions play an important, if underappreciated, role in genome function and disease, we also work on characterizing coding sequences, drilling deep into their protein products. In particular, by analyzing protein motions we can better predict how a mutation affects function. This effort involves devising a system for characterizing motions in standardized fashion in terms of key statistics, such as the degree of rotation about hinges. It is guided by the fact that protein mobility is highly restricted by tight packing. We have developed tools for measuring packing efficiency using specialized geometric constructions (e.g. Voronoi polyhedra).
	See: [http://papers.gersteinlab.org/subject/motions molecular motion papers], [http://papers.gersteinlab.org/subject/volumes structure papers]		See: [http://papers.gersteinlab.org/subject/motions molecular motion papers], [http://papers.gersteinlab.org/subject/volumes structure papers].

	====Analysis of Diverse Networks====		====Analysis of Diverse Networks====

	Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.		Networks are a way of tying together much of our research. Network representations can be applied consistently to many different types of biological data; thus, we have developed tools to build and analyze regulatory networks, protein-protein interactions and metabolic pathways, identifying key nodes such as hubs and bottlenecks. Moreover, because they are generic and flexible representation, networks provide an ideal framework for data integration. We have integrated networks with dynamic gene-expression data (identifying transient hubs), 3D-protein structures, and even satellite imagery. Finally, as people have more intuition for commonplace networks, such as those in social and computer systems, we have found cross-disciplinary comparisons helpful elucidating system-level properties of biological networks, such as the association of greater connectivity with more evolutionary constraint.
	See: [http://papers.gersteinlab.org/subject/regnet ~~Networks~~' papers]		See: [http://papers.gersteinlab.org/subject/regnet networks' papers].

	====Genomics at the Forefront of Data Science====		====Genomics at the Forefront of Data Science====