In RNA-seq data analysis, after alignment, the most common step is to estimate gene or/and transcript expression abundance, the expression level is represented by read counts. There are three options in this step:
The three options will be discussed below.
When the reads are aligned to a genome reference, e.g. hg38, the quantification is performed on transcriptome, you need to provide the annotation model file of the transcriptome.
If the alignment was generated in Partek® Flow®, the genome assembly will be displayed as text on the top of the page (Figure 1), you do not have the option to change the reference.
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If the bam file is imported, you need to select the assembly with which the reads were aligned to, and which annotation model file you will use to quantify from the drop-down menus (Figure 2).
If the Require junction reads to match introns check button is selected, only junction reads that overlap with exonic regions and match the skipped bases of an intron in the transcript will be included in the calculation. Otherwise, as long as the reads overlap within the exonic region, they will be counted. Detailed information about read compatibility can be found in the Understanding Reads tutorial.
There are five options in Strand specificity drop-down selection. We recommend verifying with the data source how the NGS library was prepared to ensure correct option selection.
Some library preparations reverse transcribe the mRNA into double stranded cDNA, thus losing strand information. In this case, the total transcript count will include all the reads that map to a transcript location. Others will preserve the strand information of the original transcript by only synthesizing the first strand cDNA. Thus, only the reads that have sense compatibility with the transcripts will be included in the calculation.
In the options, forward means the strand of the read must be the same as the strand of the transcript while reverse means the read must be the complementary strand to the transcript (Figure 3). The dash separates first- and second-in-pair. For paired end reads, we determine these by the flag information of the read in the BAM file. For single end reads, they are treated as the first read of paired end read. Briefly, the Strand specificity options are:
If the Report unexplained regions check button is selected, an additional report will be generated on the reads that are considered not compatible with any transcripts in the annotation provided. Based on the Min reads for unexplained region cutoff, the adjacent regions that meet the criteria are combined and region start and stop information will be reported.
In the annotation file, there might be multiple features in the same location, or one read might have multiple alignments, so the read count of a feature might not be an integer. Our white paper on the Partek E/M algorithm has more details on Partek’s implementation the E/M algorithm initially described by Xing et al. [2]
Depending on the annotation file, the output could be one or two data nodes. If the annotation file only contains one level of information, e.g. miRNA annotation file, you will only get one output data node. On the other hand, if the annotation file contains gene level and transcript level information, such as those from the Ensembl database, both gene and transcript level data nodes will be generated. If two nodes are generated, the Task report will also contain two tabs, reporting quantification results from each node. Each report has two tables. The first one is a summary table displaying the coverage information for each sample quantified against the specified transcriptome annotation (Figure 4).
The second table contains feature distribution information on each sample and across all the samples, number of features in the annotation model is displayed on the table title (Figure 5).
The bar chart displaying the distribution of raw read counts is helpful in assessing the expression level distribution within each sample. The X-axis is the read count range, Y axis is the number of features within the range, each bar is a sample. Hovering your mouse over the bar displays the following information (Figure 6):
The coverage breakdown bar chart is a graphical representation of the reads summary table for each sample (Figure 7)
In the box-whisker plot, each box is a sample on X-axis, the box represents 25th and 75th percentile, the whiskers represent 10th and 90th percentile, Y-axis represents the read counts, when you hover over each box, detailed sample information is displayed (Figure 8).
Cufflinks assembles transcripts and estimates transcript abundances on aligned reads. Implementation details are explained in Trapnell et al. [1]
The Cufflinks task has three options that can be configured (Figure 10):
When the Use bias correction check box is selected, it will use the genome sequence information to look for overrepresented sequences and improve the accuracy of transcript abundance estimates.
This task does not need an annotation model file, since the annotation is retrieved from the BAM file itself. The sequence names in the BAM files constitute the features with which the reads are quantified against
This task is generally performed on reads aligned to a transcriptome, e.g when a species does not have a genome reference, and the bam files contain transcriptome information. In this case, the features for this quantification task are the reference sequence names in the input bam files.
There are two parameters in Quantify to reference (Figure 11):
The output data node will display a similar Task report as the Quantify to annotation model task.
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