RNA-Seq Analysis

Introduction:

mRNA-Seq is a method used to sequence fragments of cDNA which are reverse-transcribed from messenger RNA (mRNA). mRNA fragments are transcribed from genes by RNA polymerase for use as a template by the ribosome to produce the protein encoded by the transcript. As such, levels of a specific mRNA are viewed as the “expression” of a gene.

A general goal of most mRNA-seq projects is to determine the expression of genes across the genome in a specific cell/tissue/organism type. Integrating gene expression information from many genes with known biochemical and genetic interactions allows generation and testing of molecular hypotheses.

One parameter essential to success in mRNA-seq is the use of adequate numbers of control and test samples. Descriptions of adequate numbers of biological and technical replicates are provided in the guidelines section.

 

Analysis Methods:

QC:
Initially each fastq file is assessed by a BRC member for quality control issues and trimmed to remove adapter sequences that were not removed by the initial demultiplexing.

Analysis:
After trimming and QC we align the reads to the genome of your choice using the splice-junction aware read aligner STAR.

We use RSEM to generate normalized read counts for each gene and its potential isoforms. Additionally, we filter genes with very low expression, which would otherwise reduce statistical power. EdgeR is then employed to perform differential gene expression analysis.

Two fundamental tasks are required of all differential gene expression (DGE) analyses. First, an estimation of the magnitude of differential expression between two or more conditions based on read counts from biologically replicated samples must be ascertained. This procedure requires calculation of the fold-change of read counts, taking into account the differences in sequencing depth and variation across samples and groups. Second, an estimation of the significance of the expression difference and a correction for multiple testing is required.

Differentially expressed genes are provided to you as an excel document (xlsx format) upon completion of the analysis.

Post Analysis Inspection:
Before DEGs are assessed we attempt to resolve sample differences through the unbiased method of sample clustering by unsupervised MDS using the top 500 expressing genes. This is an excellent opportunity to determine the sample to sample variation and see if it matches with your expectations of sample grouping. It also provides an opportunity to recognize errors in processing (sample switches & batch effects).  Ideally, each factor in the MDS plot will cluster well within the primary condition of interest and be separated from other conditions. This indicates that differences between groups (effect size) are larger than differences within groups.
We also view the results of a complementary unsupervised clustering approach which uses sample Pearson correlation to confirm the results of the MDS.

Example Output Report:

Click the link to be directed to our: Example mRNA-Seq Report

Introduction:

mRNA-Seq is a method used to sequence fragments of cDNA which are reverse-transcribed from messenger RNA (mRNA). mRNA fragments are transcribed from genes by RNA polymerase for use as a template by the ribosome to produce the protein encoded by the transcript. As such, levels of a specific mRNA are viewed as the “expression” of a gene.

A general goal of most mRNA-seq projects is to determine the expression of genes across the genome in a specific cell/tissue/organism type. Integrating gene expression information from many genes with known biochemical and genetic interactions allows generation and testing of molecular hypotheses.

One parameter essential to success in mRNA-seq is the use of adequate numbers of control and test samples. Descriptions of adequate numbers of biological and technical replicates are provided in the guidelines section.

 

Analysis Methods:

QC:
Initially each fastq file is assessed by a BRC member for quality control issues and trimmed to remove adapter sequences that were not removed by the initial demultiplexing.

Analysis:
After trimming and QC we align the reads to the genome of your choice using the splice-junction aware read aligner STAR.

We use RSEM to generate normalized read counts for each gene and its potential isoforms. Additionally, we filter genes with very low expression, which would otherwise reduce statistical power. EdgeR is then employed to perform differential gene expression analysis.

Two fundamental tasks are required of all differential gene expression (DGE) analyses. First, an estimation of the magnitude of differential expression between two or more conditions based on read counts from biologically replicated samples must be ascertained. This procedure requires calculation of the fold-change of read counts, taking into account the differences in sequencing depth and variation across samples and groups. Second, an estimation of the significance of the expression difference and a correction for multiple testing is required.

Differentially expressed genes are provided to you as an excel document (xlsx format) upon completion of the analysis.

Post Analysis Inspection:
Before DEGs are assessed we attempt to resolve sample differences through the unbiased method of sample clustering by unsupervised MDS using the top 500 expressing genes. This is an excellent opportunity to determine the sample to sample variation and see if it matches with your expectations of sample grouping. It also provides an opportunity to recognize errors in processing (sample switches & batch effects).  Ideally, each factor in the MDS plot will cluster well within the primary condition of interest and be separated from other conditions. This indicates that differences between groups (effect size) are larger than differences within groups.
We also view the results of a complementary unsupervised clustering approach which uses sample Pearson correlation to confirm the results of the MDS.

Example Output Report:

Click the link to be directed to our: Example mRNA-Seq Report

Introduction:

mRNA-Seq is a method used to sequence fragments of cDNA which are reverse-transcribed from messenger RNA (mRNA). mRNA fragments are transcribed from genes by RNA polymerase for use as a template by the ribosome to produce the protein encoded by the transcript. As such, levels of a specific mRNA are viewed as the “expression” of a gene.

A general goal of most mRNA-seq projects is to determine the expression of genes across the genome in a specific cell/tissue/organism type. Integrating gene expression information from many genes with known biochemical and genetic interactions allows generation and testing of molecular hypotheses.

One parameter essential to success in mRNA-seq is the use of adequate numbers of control and test samples. Descriptions of adequate numbers of biological and technical replicates are provided in the guidelines section.

 

Analysis Methods:

QC:
Initially each fastq file is assessed by a BRC member for quality control issues and trimmed to remove adapter sequences that were not removed by the initial demultiplexing.

Analysis:
After trimming and QC we align the reads to the genome of your choice using the splice-junction aware read aligner STAR.

We use RSEM to generate normalized read counts for each gene and its potential isoforms. Additionally, we filter genes with very low expression, which would otherwise reduce statistical power. EdgeR is then employed to perform differential gene expression analysis.

Two fundamental tasks are required of all differential gene expression (DGE) analyses. First, an estimation of the magnitude of differential expression between two or more conditions based on read counts from biologically replicated samples must be ascertained. This procedure requires calculation of the fold-change of read counts, taking into account the differences in sequencing depth and variation across samples and groups. Second, an estimation of the significance of the expression difference and a correction for multiple testing is required.

Differentially expressed genes are provided to you as an excel document (xlsx format) upon completion of the analysis.

Post Analysis Inspection:
Before DEGs are assessed we attempt to resolve sample differences through the unbiased method of sample clustering by unsupervised MDS using the top 500 expressing genes. This is an excellent opportunity to determine the sample to sample variation and see if it matches with your expectations of sample grouping. It also provides an opportunity to recognize errors in processing (sample switches & batch effects).  Ideally, each factor in the MDS plot will cluster well within the primary condition of interest and be separated from other conditions. This indicates that differences between groups (effect size) are larger than differences within groups.
We also view the results of a complementary unsupervised clustering approach which uses sample Pearson correlation to confirm the results of the MDS.

Example Output Report:

Click the link to be directed to our: Example mRNA-Seq Report