Expand all recipe descriptions

Found 6 recipes

What subnetworks of differentially expressed genes are enriched in my samples? What biological functions are they related to?

This recipe provides a method for identifying differentially expressed genes between two phenotypes, such as tumor and normal, to find subnetworks of interacting proteins and determine their functional annotations. An example use of this recipe is a case where an investigator may want to compare two phenotypes to determine which gene networks are similar between phenotypes, and to determine how functional annotation changes between phenotypes.

 

In particular, this recipe makes use of several GenePattern modules to identify differentially regulated genes, then uses several Cytoscape plugins to identify potential interactions between gene products, and to visualize the resulting network.

Why differential expression analysis? We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancer tissue, are said to be differentially expressed when compared to normal conditions. To identify which genes change in response to specific conditions (e.g. cancer), we must filter or process the dataset to remove genes which are not informative.

Why protein interaction network analysis? Gene expression analysis results in a list of differentially expressed genes, but it does not explain whether these genes are connected biologically in a pathway or network. To better understanding the underlying biology that drives changes in gene expression analysis, we can perform network analysis to determine whether gene products (e.g. proteins) are reported to interact. To identify potential networks or pathways, we search for highly interconnected subnetworks within a large interaction network.

Which genes are differentially expressed between my two phenotypes, based on my RNA-seq data?

This recipe provides one method to identify differentially expressed genes in RNA-seq read data. An example use of this recipe is a case where an investigator may want to compare two phenotypes, such as two types of cancer, to determine which genes are up- or down-regulated differently between these phenotypes.

 

In particular, this recipe uses the UCSC Table Browser to retrieve a reference genome to align RNA-seq reads against. We also used several modules in GenePattern to align the reads against the reference genome, and to identify differentially expressed genes when comparing two conditions. Finally, we use IGV to visualize the differentially expressed genes.

Why differential expression analysis? We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancerous tissue, are said to be differentially expressed when compared to normal conditions. To identify which genes change in response to specific conditions (e.g. cancer), we must filter or process the dataset to remove genes which are not informative.

 

 

Which genes are highly expressed, based on my RNA-seq data? Are any of the highly expressed genes also differentially expressed?

This recipe provides an outline of one method to identify and visualize genes and isoforms that are highly expressed in RNA-seq data. In particular, this recipe utilizes an analysis pipeline, allowing a user to chain together multiple analysis steps into one workflow that can be run in one step. An example use of this recipe is a case where an investigator wants to process several datasets in the same way, in which case the pipeline will allow the investigator to re-use the same modules and parameters, over and over again.

 

Given a set of raw RNA-seq reads, the goal is to align the reads to a reference genome, estimate expression abundance levels for reference genes and isoforms, filter out low-expressed genes and isoforms, and visualize the read alignments and their expression levels. In particular, this recipe uses the UCSC Table Browser to retrieve a reference genome to align RNA-seq reads against. We also uses several modules in GenePattern to align the reads against the reference genome, and to identify differentially expressed genes when comparing two conditions. Finally, we use IGV to visualize the differentially expressed genes.

Why differential expression analysis? We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancerous tissue, are said to be differentially expressed when compared to normal conditions. To identify which genes change in response to specific conditions (e.g. cancer), we must filter or process the dataset to remove genes which are not informative.

 

Which genes are differentially expressed in my microarray data? Are these genes enriched for certain biological pathways?

This recipe provides an outline of one method to identify known biological functions for genes that are differentially expressed between two conditions or phenotypes, using microarray data. An example use of this recipe is a case where an investigator may want to determine if a specific cancer phenotype is associated with expression of certain pathways.

 

Given a set of differentially expressed genes, the goal is to infer which biological functions (for example, Gene Ontology biological processes) are overrepresented in the set of reference genes found to be differentially expressed. In particular, this recipe uses a gene expression dataset which has two conditions: normal and mild hyperthermia. Then, GenePattern is used to identify differentially expressed genes, and finally MSigDB is used to identify biological functions and pathways that are enriched in the gene set.

Why differential expression analysis? We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancerous tissue, are said to be differentially expressed when compared to normal conditions. To identify which genes change in response to specific conditions (e.g. cancer), we must filter or process the dataset to remove genes which are not informative.

Why perform functional annotation? Many analyses end with the retrieval of a gene list, e.g. gene expression analysis identifies a list of genes which are differentially expressed when comparing multiple conditions. However, often times a researcher has additional questions about the function or relatedness of genes in a gene list: Are the genes a part of the same pathway? Do the gene products interact physically? Do the gene products localize to a specific part of the cell? Are the genes only expressed during a certain stage of development? These questions, and others like them, can be answered by performing functional annotation on gene lists, to better understand the underlying connections between genes.

Do phenotypically different expression datasets share a common signature? Can the signature distinguish phenotypes in an independent dataset?

This recipe provides one method for identifying a consensus gene signature from a training set of several phenotypically distinct gene expression dataset. The recipe then validates the ability of the consensus signature to accurately distinguish phenotypes by using an independent test gene expression dataset. An example use case of this recipe is when an investigator may want to develop a gene expression signature to predict a specific phenotype, such as cancer or another disease.

 

Background information: What is a consensus gene expression signature?

A gene expression signature is the pattern of expression in a specific group of genes, usually ones that are related by function, position or other biological process. A consensus gene signature is an expression pattern for a specific group of genes, which is shared among different samples or across different phenotypes. For example, a group of genes regulating immune response could be similarly up-regulated during many different, unrelated infections. There are several types of consensus signatures; those that can be derived from gene expression data are called transcriptional consensus signatures. Consensus signatures can be created by overlapping individual gene signatures derived from multiple datasets. Compared to individual gene expression signatures, consensus signatures may be more accurate at distinguishing different phenotypes, such as diseased vs. normal samples.

 

Use case: Targeting MYCN in Neuroblastoma by BET Bromodomain Inhibition (Puissant et al. , Cancer Discov. 2013).

This study analyzed gene expression data generated from primary neuroblastoma tumors of two genetic classes: tumors harboring MYCN amplification (“MYCN amplified”) and tumors without MYCN amplification (“MYCN non-amplified”). MYCN amplified neuroblastoma is exquisitely dependent on the bromodomain and extra-terminal (BET) family of proteins. As such, treatment of MYCN amplified cell lines or tumors with JQ1, a small-molecule inhibitor of BET proteins, leads to dramatic transcriptional changes and induces cell death.

To identify a consensus signature to predict sensitivity to JQ1 treatment, two training datasets and one test dataset were used. The training dataset included acute myeloid leukemia (AML) and a multiple myeloid leukemia (MM) cell lines, which had been treated with either DMSO (control) or with JQ1 (treatment). The test dataset included MYCN amplified and MYCN nonamplified neuroblastoma primary tumor samples. GenePattern was used to analyze the AML and MM cell lines; for each dataset, a gene expression signature was derived to identify JQ1 response in the cell line. Using Galaxy, the two signatures were then overlapped to determine the consensus signature between the two phenotypes.

GenePattern was used to validate the ability of this JQ1-associated consensus signature to differentiate between phenotypes, by using the signature to hierarchically cluster the test dataset (neuroblastoma). Since the MYCN amplified and MYCN non-amplified neuroblastoma samples should have differing expression profiles, it was hypothesized that the consensus signature would be able to separate the samples by phenotype. Indeed, the consensus signature was able to cluster the MYCN-amplified and MYCN-nonamplified samples separately, revealing that the consensus signature accurately distinguishes the sensitivity-to-JQ1 phenotype.

Which genes are differentially expressed between my two phenotypes, based on my RNA-seq data?

This recipe provides one method to identify and visualize gene expression in different diseases and during cell differentiation and development. In collecting ChIP-seq data, we can obtain genome-wide maps of transcription factor occupancies or histone modifications between a treatment and control. In locating these regions, we can integrate ChIP-seq and RNA-seq data to better understand how these binding events regulate associated gene expression of nearby genes. An example use case of this recipe is when Laurent et al. observed how the binding of the Prep1 transcription factor influences gene regulation in mouse embryonic stem cells. The integration of both RNA-seq and Chip-seq data allows a user to identify target genes that are directly regulated by transcription factor binding or any other epigenetic occupancy in the genome.

What is Model-based Analysis of ChiP-seq (MACS)?

Model-based Analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modifications. It is often preferred over other peak calling algorithms due to its consistency in reporting fewer false positives and its finer spatial resolution. First, it removes redundant reads to account for possible over-amplification of ChIP-DNA, which may affect peak-calling downstream. Then it shifts read positions based on the fragment size distribution to better represent the original ChIP-DNA fragment positions. Once read positions are adjusted, peak enrichment is calculated by identifying regions that are significantly enriched relative to the genomic background. MACS empirically estimates the FDR for experiments with controls for each peak, which can be used as a cutoff to filter enriched peaks. The treatment and control samples are swapped and any enriched peaks found in the control sample are regarded as false positives.

Why differential expression analysis?

We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancerous tissue, are said to be differentially expressed when compared to normal conditions.

Use Case: ChIP-Seq and RNA-Seq Analyses Identify Components of the Wnt and Fgf Signaling Pathways as Prep1 Target Genes in Mouse Embryonic Stem Cells (Laurent et al., PLoS ONE, 2015)

The sample datatset, Series GSE6328, used for this recipe are from NCBI's GEO. We identify the interplay between epigentics and transcriptomics mouse embryonic stems cells by observing how the binding of the transcription factor, Prep1, influences gene expression. Prep1 is predominantly known for its contribution in embryonic development. In comparing genome-wide maps of mouse embryonic cells experiencing Prep1 binding to those that do not, we can identify potential target genes that are being differentially regulated by these binding events.

Filter by analysis type

Filter by data type

Filter by all available tags

Filter by tool