Welcome to the blog


My thoughts and ideas

Introduction to Alignment Free Analysis | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Introduction to Alignment Free Analysis

Module 4 - Key concepts

  • pseudo-alignment, reference-free expression estimation,

Module 4 - Learning objectives

  • Alignment free estimation of transcript abundance
  • Introduction to k-mers
  • Alignment free tools
  • Sailfish, RNA-Skim, Kallisto, Salmon
  • Abundance estimation and differential expression analysis with Kallisto and Sleuth


Integrated Assignment | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Integrated Assignment

Preamble: Note that the following integrated assignment asks you to work on new RNA-seq data and apply the concepts you have learned up to this point. To complete this assignment you will need to review commands we performed in many of the earlier sections. Try to construct these commands on your own and get all the way to the end of the assignment. If you get very stuck or would like to compare your solutions to those suggested by the instructors, refer to the answers page. The integrated assignment answers page is an expanded version of this page with all of the questions plus detailed code solutions to all problems. Avoid the temptation to look at it without trying on your own.

Background: Cell lines are often used to study different experimental conditions and to study the function of specific genes by various perturbation approaches. One such type of study involves knocking down expression of a target of interest by shRNA and then using RNA-seq to measure the impact on gene expression. These eperiments often include use of a control shRNA to account for any expression changes that may occur from just the introduction of these molecules. Differential expression is performed by comparing biological replicates of shRNA knockdown vs shRNA control.

Objectives: In this assignment, we will be using a subset of the GSE114360 dataset, which consists of 6 RNA-seq datasets generated from a cell line (3 transfected with shRNA, and 3 controls). Our goal will be to determine differentially expressed genes.

Experimental information and other things to keep in mind:

Experimental descriptions from the study authors:

Experimental details from the paper: “An RNA transcriptome-sequencing analysis was performed in shRNA-NC or shRNA-CBSLR-1 MKN45 cells cultured under hypoxic conditions for 24 h (Fig. 2A).”

Experimental details from the GEO submission: “An RNA transcriptome sequencing analysis was performed in MKN45 cells that were transfected with tcons_00001221 shRNA or control shRNA.”

Note that according to GeneCards and HGNC, CBSLR and tcons_00001221 refer to the same gene.

PART 0 : Obtaining Data and References


Create a working directory ~/workspace/rnaseq/integrated_assignment/ to store this exercise. Then create a unix environment variable named RNA_INT_ASSIGNMENT that stores this path for convenience in later commands.

export RNA_HOME=~/workspace/rnaseq
mkdir -p ~/workspace/rnaseq/integrated_assignment/
export RNA_INT_DIR=~/workspace/rnaseq/integrated_assignment

Obtain reference, annotation, adapter and data files and place them in the integrated assignment directory

Remember: when initiating an environment variable, we do NOT need the $; however, everytime we call the variable, it needs to be preceeded by a $.


wget http://genomedata.org/rnaseq-tutorial/Integrated_Assignment_RNA_Data.tar.gz

Q1.) How many items are there under the “reference” directory (counting all files in all sub-directories)? What if this reference file was not provided for you - how would you obtain/create a reference genome fasta file. How about the GTF transcripts file from Ensembl?

Q2.) How many exons does the gene SOX4 have? How about the longest isoform of PCA3?

Q3.) How many samples do you see under the data directory?

NOTE: The fastq files you have copied above contain only the first 1,000,000 reads. Keep this in mind when you are combing through the results of the differential expression analysis.

Part 1 : Data preprocessing


Q4.) What metrics, if any, have the samples failed? Are the errors related?

Q5.) What average percentage of reads remain after adapter trimming? Why do reads get tossed out?

Q6.) What sample has the largest number of reads after trimming/cleanup?

Part 2: Data alignment


Q7.) How would you obtain summary statistics for each aligned file?

Q8.) Approximately how much space is saved by converting the sam to a bam format?

In order to make visualization easier, you should now merge each of your replicate sample bams into one combined BAM for each condition. Make sure to index these bams afterwards to be able to view them on IGV.

Try viewing genes such as TP53 to get a sense of how the data is aligned. To do this:

Q9.) What portion of the gene do the reads seem to be piling up on? What would be different if we were viewing whole-genome sequencing data?

Q10.) What are the lines connecting the reads trying to convey?

Part 3: Expression Estimation


Q11.) How can you obtain the expression of the gene SOX4 across the transfected and control samples?

Part 4: Differential Expression Analysis


Adapt the R tutorial code that was used in Differential Expression section. Modify it to work on these data (which are also a 3x3 replicate comparison of two conditions).

Q12.) Are there any significant differentially expressed genes? How many in total do you see? If we expected SOX4 to be differentially expressed, why don’t we see it in this case?

Part 5: Differential Expression Analysis Visualization

Q13.) What plots can you generate to help you visualize this gene expression profile?

Hint, a volcano plot is popular approach to provide a high level summary of a differential expression analysis. Refer to the DE Visualization section for example R code.