Tools
From GersteinInfo
Line 6: | Line 6: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"| [[File:Morph-icon.jpg]] <br> [http://molmovdb.mbb.yale.edu/molmovdb/ MolMovDB] || | |style="width:15%; text-align:center;"| [[File:Morph-icon.jpg]] <br> [http://molmovdb.mbb.yale.edu/molmovdb/ MolMovDB] || | ||
Line 15: | Line 15: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"| [[File:pseudogene.png]] <br> [http://www.pseudogene.org/ Pseudogene.org] ||Pseudogene.org is a collection of resources related to our efforts to survey eukaryotic genomes for pseudogene sequences, "pseudo-fold" usage, amino-acid composition, and single-nucleotide polymorphisms (SNPs) to help elucidate the relationships between pseudogene families across several organisms. | |style="width:15%; text-align:center;"| [[File:pseudogene.png]] <br> [http://www.pseudogene.org/ Pseudogene.org] ||Pseudogene.org is a collection of resources related to our efforts to survey eukaryotic genomes for pseudogene sequences, "pseudo-fold" usage, amino-acid composition, and single-nucleotide polymorphisms (SNPs) to help elucidate the relationships between pseudogene families across several organisms. | ||
Line 23: | Line 23: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!class="unsortable"|Description |
|-style="height: 10px;" | |-style="height: 10px;" | ||
|style="width:15%; text-align:center;"| [[File:Network.jpg|center|x75px]] <br> [http://networks.gersteinlab.org/ Networks] || | |style="width:15%; text-align:center;"| [[File:Network.jpg|center|x75px]] <br> [http://networks.gersteinlab.org/ Networks] || | ||
Line 32: | Line 32: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"| [[File:SVpage logo.png|center|x85px]] <br> [http://sv.gersteinlab.org/ Structural Variants]|| | |style="width:15%; text-align:center;"| [[File:SVpage logo.png|center|x85px]] <br> [http://sv.gersteinlab.org/ Structural Variants]|| | ||
Line 43: | Line 43: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://sv.gersteinlab.org/cnvnator/ '''CNVnator'''] [http://papers.gersteinlab.org/papers/CNVnator/index.html (citation)]||style="width:7%; text-align:center;"|2013|| An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. | |style="width:15%; text-align:center;"|[http://sv.gersteinlab.org/cnvnator/ '''CNVnator'''] [http://papers.gersteinlab.org/papers/CNVnator/index.html (citation)]||style="width:7%; text-align:center;"|2013|| An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. | ||
Line 53: | Line 53: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://funseq.gersteinlab.org/ '''FunSeq''']||style="width:7%; text-align:center;"|2013|| This site can be used to automatically score and annotate disease-causing potential of SNVs, particularly the non-coding ones. It can be used on cancer and personal genomes. It also contains a downloadable tool. | |style="width:15%; text-align:center;"|[http://funseq.gersteinlab.org/ '''FunSeq''']||style="width:7%; text-align:center;"|2013|| This site can be used to automatically score and annotate disease-causing potential of SNVs, particularly the non-coding ones. It can be used on cancer and personal genomes. It also contains a downloadable tool. | ||
Line 63: | Line 63: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://archive.gersteinlab.org/proj/rnaseq/fusionseq/ '''FusionSeq''']<br>[http://github.com/gersteinlab/FusionSeq Github repo]||style="width:7%; text-align:center;"|2011||FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data. | |style="width:15%; text-align:center;"|[http://archive.gersteinlab.org/proj/rnaseq/fusionseq/ '''FusionSeq''']<br>[http://github.com/gersteinlab/FusionSeq Github repo]||style="width:7%; text-align:center;"|2011||FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data. | ||
Line 81: | Line 81: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://www.gersteinlab.org/proj/PeakSeq/ '''PeakSeq''']||style="width:7%; text-align:center;"|2009|| A tool for calling peaks corresponding to transcription factor binding sites from ChIP-Seq data scored against a matched control such as Input DNA. PeakSeq employs a two-pass strategy in which putative binding sites are first identified in order to compensate for genomic variation in the 'mappability' of sequences, before a second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. | |style="width:15%; text-align:center;"|[http://www.gersteinlab.org/proj/PeakSeq/ '''PeakSeq''']||style="width:7%; text-align:center;"|2009|| A tool for calling peaks corresponding to transcription factor binding sites from ChIP-Seq data scored against a matched control such as Input DNA. PeakSeq employs a two-pass strategy in which putative binding sites are first identified in order to compensate for genomic variation in the 'mappability' of sequences, before a second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. | ||
Line 89: | Line 89: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://alleleseq.gersteinlab.org/home.html '''AlleleSeq''']||style="width:7%; text-align:center;"|2011||The AlleleSeq is a computational pipeline that is used to study allele-specific expression (ASE) and allele specific binding (ASB). The pipeline first constructs a diploid personal genome sequence, then map RNA-seq and ChIP-seq functional genomic data onto this personal genome. Consequently, locations where there are differences in number of mapped reads between maternally- and paternally-derived sequences can be identified and these provide evidence for allele-specific events. | |style="width:15%; text-align:center;"|[http://alleleseq.gersteinlab.org/home.html '''AlleleSeq''']||style="width:7%; text-align:center;"|2011||The AlleleSeq is a computational pipeline that is used to study allele-specific expression (ASE) and allele specific binding (ASB). The pipeline first constructs a diploid personal genome sequence, then map RNA-seq and ChIP-seq functional genomic data onto this personal genome. Consequently, locations where there are differences in number of mapped reads between maternally- and paternally-derived sequences can be identified and these provide evidence for allele-specific events. | ||
Line 97: | Line 97: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://motips.gersteinlab.org/ '''motips''']||style="width:7%; text-align:center;"|2003||MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains. | |style="width:15%; text-align:center;"|[http://motips.gersteinlab.org/ '''motips''']||style="width:7%; text-align:center;"|2003||MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains. | ||
Line 107: | Line 107: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://networks.gersteinlab.org/genome/interactions/networks/ '''TopNet''']||style="width:7%; text-align:center;"|2004||An automated web tool designed to calculate topological parameters and compare different sub-networks for any given network. | |style="width:15%; text-align:center;"|[http://networks.gersteinlab.org/genome/interactions/networks/ '''TopNet''']||style="width:7%; text-align:center;"|2004||An automated web tool designed to calculate topological parameters and compare different sub-networks for any given network. | ||
Line 121: | Line 121: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://coevolution.gersteinlab.org/coevolution/ '''Coevolution analysis of protein residues''']||style="width:7%; text-align:center;"|2008||An integrated online system that enables comparative analyses of residue coevolution with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods. | |style="width:15%; text-align:center;"|[http://coevolution.gersteinlab.org/coevolution/ '''Coevolution analysis of protein residues''']||style="width:7%; text-align:center;"|2008||An integrated online system that enables comparative analyses of residue coevolution with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods. | ||
Line 129: | Line 129: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://geometry.molmovdb.org '''Macromolecular Geometry and Packing Tools''']||style="width:7%; text-align:center;"|1994-2009||A number of programs for calculating properties of protein and nucleic acid structures have been collected into a single distribution. Included are a library of utility functions for dealing with structures, a convenient interactive command-line interpreter, and tools for the calculation of geometrical quantities associated with macromolecular structures and their motions. | |style="width:15%; text-align:center;"|[http://geometry.molmovdb.org '''Macromolecular Geometry and Packing Tools''']||style="width:7%; text-align:center;"|1994-2009||A number of programs for calculating properties of protein and nucleic acid structures have been collected into a single distribution. Included are a library of utility functions for dealing with structures, a convenient interactive command-line interpreter, and tools for the calculation of geometrical quantities associated with macromolecular structures and their motions. | ||
Line 144: | Line 144: | ||
:{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | :{|class="wikitable sortable" border="1" cellspacing="0" cellpadding="10" | ||
|- bgcolor="lightsteelblue" | |- bgcolor="lightsteelblue" | ||
- | ! | + | !Name!!Release Date!!class="unsortable"|Description |
|-style="height: 100px;" | |-style="height: 100px;" | ||
|style="width:15%; text-align:center;"|[http://sv.gersteinlab.org/breakdb/ '''BreakDB''']||style="width:7%; text-align:center;"|2008||This database, which is part of the PEMer package, contains information about structural variants and associated breakpoints. | |style="width:15%; text-align:center;"|[http://sv.gersteinlab.org/breakdb/ '''BreakDB''']||style="width:7%; text-align:center;"|2008||This database, which is part of the PEMer package, contains information about structural variants and associated breakpoints. |
Revision as of 00:36, 9 April 2014
Below we highlight some of our tools and data sets. For an overview of the published literature associated with our tools and databases, please visit our tools publication page. You may also view a list of the publications associated with our core tools. Source code is available on our lab Github page.
Contents |
Tool portals
MolMovDB
Name Description
MolMovDBThis describes the motions that occur in proteins and other macromolecules, particularly using movies. Associated with it are a variety of free software tools and servers for structural analysis.
Pseudogene.org
Name Description
Pseudogene.orgPseudogene.org is a collection of resources related to our efforts to survey eukaryotic genomes for pseudogene sequences, "pseudo-fold" usage, amino-acid composition, and single-nucleotide polymorphisms (SNPs) to help elucidate the relationships between pseudogene families across several organisms.
Networks
Name Description
NetworksThe Gerstein lab has been a pioneer in applying network analysis to generate knowledge form large-scale experiments.To this end, we have developed a portal for our network research.
Structural Variants (SV)
Name Description
Structural VariantsStructural Variations (SVs) and Copy Number Variations (CNVs) are a major source of genetic variation. our analysis tools are available in this portal.
Genome Technology Tools
Structural Variation
Name Release Date Description CNVnator (citation) 2013 An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. AGE (citation) 2013 AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision.
Functional Annotation
Name Release Date Description FunSeq 2013 This site can be used to automatically score and annotate disease-causing potential of SNVs, particularly the non-coding ones. It can be used on cancer and personal genomes. It also contains a downloadable tool. VAT
Github repo2012 A computational framework to functionally annotate variants in personal genomes using a cloud-computing environment.
RNA-seq
Name Release Date Description FusionSeq
Github repo2011 FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data. ACT 2011 The aggregation and correlation toolbox (ACT) is an aggregation and correlation toolbox for analyses of genome tracks. IQseq
Github repo2010 A tool for isoform quantification with RNA-seq data. Given isoform annotation and alignment of RNA-seq reads, it will use an EM algorithm to infer the most probable expression level for each isoform of a gene. LESSeq
Github repo2013 Local Event-based analysis of alternative Splicing using RNA-Seq RSEQtools 2010 A suite of tools that use Mapped Read Format (MRF) for the analysis of RNA-Seq experiments. MRF was developed to address privacy concerns associated with the potential for mRNA sequence reads to identify and genetically characterise specific individuals; it is a compact data summary format that enables anonymization of confidential sequence information, while maintaining the ability to conduct subsequent functional genomics studies. RSEQtools provides a suite of modules that convert to/from MRF data and perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads, and segmenting that signal into actively transcribed regions.
ChiP-Seq
Name Release Date Description PeakSeq 2009 A tool for calling peaks corresponding to transcription factor binding sites from ChIP-Seq data scored against a matched control such as Input DNA. PeakSeq employs a two-pass strategy in which putative binding sites are first identified in order to compensate for genomic variation in the 'mappability' of sequences, before a second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances.
Allele-specific effects
Name Release Date Description AlleleSeq 2011 The AlleleSeq is a computational pipeline that is used to study allele-specific expression (ASE) and allele specific binding (ASB). The pipeline first constructs a diploid personal genome sequence, then map RNA-seq and ChIP-seq functional genomic data onto this personal genome. Consequently, locations where there are differences in number of mapped reads between maternally- and paternally-derived sequences can be identified and these provide evidence for allele-specific events.
Microarrays & Proteomics
Name Release Date Description motips 2003 MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains. PARE 2007 (Protein Abundance and mRNA Expression). A tool for comparing protein abundance and mRNA expression data. In addition to globally comparing the quantities of protein and mRNA, PARE allows users to select subsets of proteins for focused study (based on functional categories and complexes). Furthermore, it highlights correlation outliers, which are potentially worth further examination.
Networks
Name Release Date Description TopNet 2004 An automated web tool designed to calculate topological parameters and compare different sub-networks for any given network. tYNA 2006 (TopNet-like Yale Network Analyzer). A Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks etc. PubNet 2005 A web-based tool that extracts several types of relationships returned by PubMed queries and maps them into networks, allowing for graphical visualization, textual navigation, and topological analysis. DynaSIN 2011 The Dynamic Structure Interaction Network (DynaSIN) is a resource for studying protein-protein interaction networks in the context of conformational changes.
Evolution Tools
Name Release Date Description Coevolution analysis of protein residues 2008 An integrated online system that enables comparative analyses of residue coevolution with a comprehensive set of commonly used scoring functions, including Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Variation (ELSC), mutual information and correlation-based methods.
Structure and Macromolecular Motions
Name Release Date Description Macromolecular Geometry and Packing Tools 1994-2009 A number of programs for calculating properties of protein and nucleic acid structures have been collected into a single distribution. Included are a library of utility functions for dealing with structures, a convenient interactive command-line interpreter, and tools for the calculation of geometrical quantities associated with macromolecular structures and their motions. 3vee 1998 3vee is collection of program for the assessment of volumes in protein files.
HIT 2006 (Helix Interaction Tool). A web-based comprehensive package of tools for analyzing helix-helix interactions in proteins. Morph Server 2006 A web server for generating and viewing models of protein conformational change using interpolation with energy minimization.
Data sets
Name Release Date Description BreakDB 2008 This database, which is part of the PEMer package, contains information about structural variants and associated breakpoints.