The output from sequencing is a massive amount of data. The raw data comes as a text file listing millions of DNA sequences in random order.
To give you an easy overview, we offer bioinformatic analysis giving you the final results displayed in figures and tables to enable easy visual interpretation. Our work will be summed up in a written report detailing the steps taken and results generated for the various analyses performed by omiics.
STANDARD BIOINFORMATIC ANALYSES
To get you started searching for trends from the raw data, we offer a selection of standard analyses. For more details, a menu is listed on the right
EXTENDED OR NON-STANDARD INVOLVEMENT
Contact us for a non-binding discussion if you have any special requests or questions
Differential gene expression
Significantly up- or downregulated genes
Changes in isoform prevalence
Pathways most significantly affected by changes
RESULTS IN REPORT
Principal Component Analysis (PCA) Plot is used to give you a quick visual overview of the general difference between your samples.
The NGS method is comparative, hence you will at least have two groups of samples you compare, such as "mutant" vs. "normal".
RNA sequencing can provide the complete expression profile of RNA in your samples.
Using small RNA sequencing we can quantify RNA species of short length such as miRNAs, whereas total RNA sequencing will give you a complete expression profile for RNA species longer than 200 nucleotides.
That is, you get all the RNA expressed in your samples which could be "normal" and "mutant". Next step is to uncover which RNAs are differently expressed between "normal" and "mutant". This is done by comparing individual expression levels between the two groups.
Consequently, the result of differential gene expression is a list of the changes in all RNAs between your two groups.
If you have 3 "normal" samples and 3 "mutant", you will expect them to group. A high degree of grouping means that the difference between groups "normal" and "mutant" is higher than the variability internally in the groups.
LIST OF GENE EXPRESSION
A comprehensive list in excel of all genes, their differential expression and the degree of significance. This you can use as a catalogue to zoom in on a specific gene of interest.
Ex.: is the expression of receptorA changed in "mutant" compared to "normal" and how much?
The Volcano plot is a visual representation of all RNAs, correlating the change in expression level between the samples. Each dot denotes a specific RNA. Black dots represent non-significant changes, while red dots display significant values.
RESULTS IN REPORT
We offer two different approaches for characterisation of alternative splicing using the power of RNA-seq:
DIFFERENTIAL EXON USAGE
Alternative splicing can be inferred from observing a change in the relative number of reads mapping to individual exons. If an exons is spliced out, a drop in the number of mapping reads will be observed.
From this analysis you will get an excel file showing the significant exons and report(s) visually detailing the exons.
Another approach we use is to build whole transcripts from RNA-seq data. Many identical to known transcripts, some novel. These transcripts are then tested for differential expression and reported in an excel file. Genes of interest can be examined in detail by generating isoform reports laying out which transcript isoforms are detected from the particular genes, showing isoform composition and change between sample groups. Individual pdf isoform reports are generated.
Comparing, or identifying, various RNA variants from the same gene.
Once a full-length RNA strand is formed, parts of it may have been removed by the process of alternative splicing. In this way, one gene can produce multiple different RNAs with similar, yet non-identical functions.
The NGS technology gives you the full scope of RNA variants produced on a genomewide scale.
By applying bioinformatics, you can discover novel splice variants, detect significant changes in alternative splicing and find altered distribution of variants, also called isoforms.
RESULTS IN REPORT
Most significantly enriched GO terms. Gives you a visual representation of the pathways/terms most significantly affected by the difference between the samples.
All significantly enriched GO terms. Dive into the specifics behind each term and get exact individual info for each GO term, including GeneIDs, p-values, and FDR values.
Detecting significantly up- or downregulated pathways.
The complexity of regulatory mechanisms and interplay within a cell is formidable. Changing just one element is likely to cause downstream effect, disturb feedback-loops and trigger compensatory backup systems.
Bioinformatic pathway analysis address which main cellular pathways are affected by the difference between the samples. This is done by comparing the results from the differential gene expression analysis with a pathway database.
We mainly perform pathway analysis on gene ontology (GO) terms. GO terms are divided into three categories; biological process, cellular component and molecular function.
Alternatively, we also use the KEGG database.
While the existence of circular RNAs has been recognized since the 1980ties, specific functionalities were first reported in 2013. Since then, there has been massive interest in the potential of this novel class of regulators.
The formation of the circular RNA species occurs by a back-splice event, producing a so-called backsplice-junction (bsj).
In most cases, the expression level of circRNAs are much lower than the parental transcript, yet using NGS, it is possible to quantify the expression level, by identifying the bsj sequence.
RESULTS IN REPORT
PCA PLOT, EXCEL TABLE AND VOLCANOPLOT
The result of the circRNA detection analysis is a differential gene expression analysis as described above.
In addition to comparisons between groups, you will get values for ratios between circRNA and the parental host transcript within each sample.
RESULTS IN REPORT
PCA PLOT, EXCEL TABLE AND VOLCANO PLOT
The result of the tRNA profiling is a differential gene expression analysis as described above.
tRNA COVERAGE VISUALIZATION
To display tRNA fragment mapping on the individual full-length tRNAs, tRNA coverage and read length plots are included together with the EXCEL file to enable quick and easy detection of obvious trends.
For each specific amino acid there are multiple tRNAs differing in single bases and/or modifications. The composition of the tRNA pool affects the rate of translation, which again affects the dynamics of protein folding. Hence, the specific tRNA pool serves a regulatory role in protein maturation.
An additional regulatory role is through cleaveage-products, tRNA halves (Angiogenin cleaved) or smaller tRNA fragments (Dicer cleaved). These are highly expressed in exosomes, accumulate in certain diseases (biomarker) and in some cases have been shown to act similar to miRNA together with Ago.
Quantification of tRNA cleavage products is performed on small RNA sequencing data and can be the primary purpose of sequencing or a added benefit from data generated for miRNA quantification.
tRNA cleavage fragments are gaining considerable attention for their biological roles and application as biomarkers, making their analysis in small RNA data highly desirable.