A short review of RNA sequencing and its applications

What are the omics sciences?

Omics sciences are targeting quantification of whole biomolecules such as RNA and proteins at organism, tissue, or a single-cell level. Omics sciences are separated into several branches such as genomics, transcriptomics, and proteomics1.

What is transcriptomics?

Transcriptomics is one of the omics sciences dissecting the organism’s transcriptome which is the sum of all of its RNA molecules2,3.

What is RNA sequencing?

RNA sequencing (RNA-seq) is a technique providing quantification of all RNAs in bulk tissues or each cell. The transcript amounts of each gene across samples are calculated by using this technique. It is utilizing next-generation sequencing (NGS) platforms deciphering the sequencing of biomolecules such as DNA and RNA4,5.

What are the kinds of RNA-seq?

Bulk tissue RNA-seq

The whole transcriptome of target bulk tissues is sequenced to make transcriptomics analyses. Here, target bulk tissue can contain various cell types, and therefore, the whole transcriptome is mixed with RNAs of those cells. This approach is the most common usage of RNA-seq and is performed for some aims such as elucidating of diseases7.

Single-cell RNA-seq

In contrast to bulk tissue RNA-seq, single-cell RNA-seq (scRNA-seq) is performed in individual cells. The whole transcriptome of each cell in a tissue is sequenced to make transcriptomics analysis. The scRNA-seq has revealed that the transcriptome of each cell in a tissue is different from each other and individual cells can be separated into specific clusters according to its transcriptomic signature. The scRNA-seq has helped the discovery of some cells such as ionocyte cells, which could be relevant to the pathology of cystic fibrosis7,8.

Spatial RNA-seq

The relationship between cells and their relative locations within a tissue sample can be critical to understanding disease pathology. Spatial transcriptomics is a technology that allows the measurement of all the gene activity in a tissue sample and map where the activity is occurring. This technique is utilized in the understanding of biological processes and disease. Spatial RNA-seq can be performed at intact tissue sections as well as a single-cell level. The general aim of this technique is a combination of gene expression and morphological information and providing information on tissue architecture and micro-environment for the generation of sub-cellular data. Current bulk and scRNA-seq methods provide users with highly detailed data regarding tissues or cell populations but do not capture spatial information7,9,10.

RNA-seq analysis work-flow

1) Experimental design

There are many various library types in RNA-seq resulted in sequencing reads (sequenced transcripts) with different characteristics. For instance, reads can be single-end in which a transcript is read from its only an end (5’ or 3’), however, in the paired-end libraries, a transcript is read from both its 5’ and 3’ end. Paired-end sequencing can additionally help disambiguate read mappings and is preferred for alternative-exon quantification, fusion transcript detection, particularly when working with poorly annotated transcriptomes7. In addition to that, libraries can be stranded or unstranded. The strandedness for libraries is important to determine which DNA strand reads coming from and it is utilized to assign reads to relevant genes. If strandedness information of libraries is misused, then reads are not assigned to true genes, thus gene expression results gonna be wrong11. Besides, technical replicates can be utilized in this process in which one sample is sequenced more than one by using the same high-throughput platform to increase the elimination of technical bias.

2) Laboratory performance

After RNA extraction from all samples, libraries are prepared for sequencing according to the selected library type. After detection of library type, libraries are sequenced to read depth of 10–30 million reads per sample on a high-throughput platform7.

3) Data analysis

After sequencing has been completed, the starting point for analysis is the data files, which contain base-called sequencing reads, usually in the form of FASTQ. The reads having poor quality in FASTQ files are eliminated before the alignment process in which raw sequences are aligned to a reference genome to find their relevant genes. Each sequence read is converted to one or more genomic coordinates and Sequence Alignment Map (SAM) files containing those coordinates are obtained after alignment process7,12. This process has traditionally been accomplished using distinct alignment tools, such as TopHat13, STAR14, or HISAT15, which rely on a reference genome. The SAM files are converted to Binary Alignment Map (BAM) files for further analyses because of their large size and this process is carried out by using Samtools16. After alignment and file conversation steps, reads (transcripts) quantification across samples is performed by using some tools such as featureCounts17 to obtain expression matrix in which each row corresponds to individual genes, however, each column corresponds to individual samples7. Normalization of transcripts abundance across samples is made by using expression matrix to lessen range-based gene expression differences between samples7,18,19. Normalization methods are shown in (Figure 1)20.

Figure 1. Normalization methods that are used in RNA-seq analyses.

After normalization step, genes with low expression across samples are filtered to prevent statistical noise7, and then statistically meaningful genes (namely, differentially expressed genes) can be detected by using some tools such as edgeR21, DESeq222. In the end, obtained genes can be used for enrichment analyses such as KEGG and Reactome to find out which pathways are affected. RNA-seq technology is utilized for distinct aims, some of which are shown in (Figure 2). The representations of RNA-seq results are shown in (Figure 3).

Figure 2. RNA-seq usage fields.

Figure 3. Representation of differential expression, splicing, and co-expression results. In differential expression figure, each row represents the expression amount of a gene, however, each column represents each sample. Red color shows higher expressions, but the yellow color shows lower expressions. In the co-expression figure, a network containing the interaction of each gene with other genes is depicted. In the differential alternative splicing figure, differential usage of E010 exon between control and knockdown groups is depicted.

A detailed RNA-seq work-flow is shown in (Figure 4)12.

Figure 4. An example of differential expression work-flow.

The various tools that are used for RNA-seq and their tutorials were listed below as well as visualization tools that are used for high-throughput data.

Table 1. List of RNA-seq tool and their usage fields.

Tool names Usage Tutorial Link
DESeq222 Differential expression https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html
edgeR21 Differential expression https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
DEXSeq23 Differential splicing https://bioconductor.org/packages/release/bioc/vignettes/DEXSeq/inst/doc/DEXSeq.html
WGCNA24 Co-expression https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/
GATK25 Variant-calling https://gatk.broadinstitute.org/hc/en-us

Table 2. List of high-throughput visualization and enrichment tools.

Tool names Usage
pheatmap26 Heatmap plot for differentially expressed genes
ggplot227 Most various visualizations ranging from bar charts to violin plots
igraph28 Network visualization for co-expression networks and other network types
Enrichr29 Enrichment analysis of genes
DAVID30 Enrichment analysis of genes

Note/ Most of the listed tools are dependent on the R statistical computing environment.

Table 3. Examples of differential expression work-flows.

Examples Links
Example 1 https://www.bioconductor.org/help/course-materials/2016/CSAMA/lab-3-rnaseq/rnaseq_gene_CSAMA2016.html
Example 2 https://digibio.blogspot.com/2017/11/rna-seq-analysis-hisat2-featurecounts.html
Example 3 https://bioinformaticsworkbook.org/dataAnalysis/RNA-Seq/RNA-SeqIntro/RNAseq-using-a-genome.html
Example 4 https://uclouvain-cbio.github.io/BSS2019/rnaseq_gene_summerschool_belgium_2019.html

In addition to differential expression pipelines above, If you want to examine my pipeline containing differential expression analysis with DESeq2, you can visit this https://github.com/kaanokay/Differential-Expression-Analysis/blob/master/HISAT2-featureCounts-DESeq2-workflow.md website address in which I attached my Linux and R scripts.

Transcriptome researches in autism spectrum disorder

Autism Spectrum Disorder (ASD) is an early-onset neuropsychiatric disorder. ASD is clinically described with behavioural abnormalities such as restrictive interest and repetitive behaviour. ASD is genetically heterogeneous and heritable (~50%) and 80% of its genetic background is unclear. Aberrations in autistic brains take mostly place in cortex regions (Figure 5) rather than cerebellum. When ASD is compared with other neuropsychiatric disorders such as schizophrenia and bipolar disorder, it has a higher heritability-rate than them, which means that it appears with the more strong genetic background than schizophrenia and bipolar disorder. Studies have revealed that ASD-related genes are enriched in brain-development, neuronal activity, signalling, and transcription regulation. Wnt signalling, synaptic function, and translational regulation are pathways that are affected by mutations in ASD-related genes31.

Figure 5. Brain regions most affected in autism.

Transcriptome studies have shown that mRNA, microRNA (miRNA), small nucleolar RNA (snoRNA), and long non-coding RNA (lncRNAs) misexpression occurred in autistic brains. Genes with mRNA misregulation are especially enriched in immune and neuronal pathways, briefly neuronal development and immune system activation are both misregulated in the brains of individuals with ASD. Misregulated miRNAs in autistic brains target mostly genes with synaptic function. Additionally, alternative splicing is misregulated in splicing regulators and this causes mis-splicing patterns in autistic individuals31.

To summarize, RNA-seq is strong technology for understanding diseases and it can be used for various aims.

That’s all 🙂

If you have any questions about this short review and my differential expression pipeline in GitHub, you feel free to contact me via kaan.okay@msfr.ibg.edu.tr e-mail address.

Very thanks for your interest and time!


1) https://en.wikipedia.org/wiki/Omics.

2) https://en.wikipedia.org/wiki/Transcriptomics_technologies.

3) https://en.wikipedia.org/wiki/Transcriptome.

4) Kadakkuzha, B. M., Liu, X. an, Swarnkar, S. & Chen, Y. Genomic and proteomic mechanisms and models in toxicity and safety evaluation of nutraceuticals. in Nutraceuticals: Efficacy, Safety and Toxicity 227–237 (Elsevier Inc., 2016). doi:10.1016/B978-0-12-802147-7.00018-8.

5) Behjati, S. & Tarpey, P. S. What is next generation sequencing? Arch. Dis. Child. Educ. Pract. Ed. 98, 236–238 (2013).

6) https://www.ebi.ac.uk/training/online/course/functional-genomics-ii-common-technologies-and-data-analysis-methods/performing-rna-seq.

7) Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).

8) https://en.wikipedia.org/wiki/Single_cell_sequencing.

9) https://www.10xgenomics.com/spatial-transcriptomics/.

10) https://www.diva-portal.org/smash/get/diva2:1068517/FULLTEXT01.pdf.

11) https://salmon.readthedocs.io/en/latest/library_type.html.

12) https://bioinformaticsworkbook.org/dataAnalysis/RNA-Seq/RNA-SeqIntro/RNAseq-using-a-genome.html.

13) Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

14) Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

15) Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

16) Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

17) Liao, Y., Smyth, G. K. & Shi, W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

18) Evans, C., Hardin, J. & Stoebel, D. M. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Brief. Bioinform. 19, 776–792 (2018).

19) Liu, X. et al. Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review. Front. Bioeng. Biotechnol. 7, 1–11 (2019).

20) https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html.

21) Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

22) Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, (2014).

23) Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-Seq data. Nat. Preced. 1–30 (2012) doi:10.1038/npre.2012.6837.2.

24) Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 9, (2008).

25) McKenna, A. et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

26) https://cran.r-project.org/web/packages/pheatmap/pheatmap.pdf.

27) https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf.

28) https://cran.r-project.org/web/packages/igraph/igraph.pdf.

29) https://amp.pharm.mssm.edu/Enrichr/.

30) https://david.ncifcrf.gov/.

31) Quesnel-Vallières, M., Weatheritt, R. J., Cordes, S. P. & Blencowe, B. J. Autism spectrum disorder: insights into convergent mechanisms from transcriptomics. Nat. Rev. Genet. 20, 51–63 (2019).

Evolution and Unprecedented Variants of the Mitochondrial Genetic Code in a Lineage of Green Algae – David Žihala


David Žihala


Mitochondria of diverse eukaryotes have evolved various departures from the standard genetic code, but the breadth of possible modifications and their phylogenetic distribution are known only incompletely. Furthermore, it is possible that some codon reassignments in previously sequenced mitogenomes have been missed, resulting in inaccurate protein sequences in databases. Considering the distribution of codons at conserved amino acid positions in mitogenome-encoded proteins, mitochondria of the green algal order Sphaeropleales exhibit a diversity of codon reassignments, including previously missed ones and some that are unprecedented in any translation system examined so far, necessitating redefinition of existing translation tables and creating at least seven new ones. We resolve a previous controversy concerning the meaning the UAG codon in Hydrodictyaceae, which beyond any doubt encodes alanine. We further demonstrate that AGG, sometimes together with AGA, encodes alanine instead of arginine in diverse sphaeroplealeans. Further newly detected changes include Arg-to-Met reassignment of the AGG codon and Arg-to-Leu reassignment of the CGG codon in particular species. Analysis of tRNAs specified by sphaeroplealean mitogenomes provides direct support for and molecular underpinning of the proposed reassignments. Furthermore, we point to unique mutations in the mitochondrial release factor mtRF1a that correlate with changes in the use of termination codons in Sphaeropleales, including the two independent stop-to-sense UAG reassignments, the reintroduction of UGA in some Scenedesmaceae, and the sense-to-stop reassignment of UCA widespread in the group. Codon disappearance seems to be the main drive of the dynamic evolution of the mitochondrial genetic code in Sphaeropleales.

Date: April 28th, 2020 – 7:00 pm (GMT+3)

Language: English

To register the webinar, you can visit this link:

Accessing Multi-omics Data for the Purposes of Tumour Profiling – Aashil A. Batavia


Aashil A. Batavia

Aashil Batavia received his undergraduate degree from the University of Manchester obtaining a B.Sc. in Biomedical Sciences in 2014. During his dissertation, he implemented in silico experimental evolution to gain insights into the relationship between mutation rate plasticity, evolvability and robustness; exposing him to computational approaches for biomedical research for the first time. In 2015, he elected to return to the University of Manchester where he obtained an M.Sc in Bioinformatics and Systems Biology. Here he completed two research projects, one of which assessed the impact of human variants on the structure and function of Prpf8; mutations in which have been shown to cause retinitis pigmentosa. This work paved the way for his move to Switzerland in 2017 where he would begin his PhD at the Institute of Pathology and Molecular Pathology, USZ and the Department of Biosystems Science and Engineering, ETH Zurich. With a foot in both the computational and experimental worlds, his current work is focused on the multi-omics assessment of a rare form of renal cell carcinoma termed wild-type von Hippel-Lindau (wtVHL) clear cell renal cell carcinoma.


Cancers are a very complex and heterogeneous set of diseases and therefore, cancer research is by no means trivial. The greater our understanding of the molecular landscape of a particular tumour type the better equipped we will become to combat its growth and spread. Publicly available multi-omic datasets provide a valuable resource to further this understanding. These data sets are commonly used for the identification of novel areas of study, the validation of results and the benchmarking/assessment of novel statistical methods. The Cancer Genome Atlas (TCGA) provides one such dataset with its repository consisting of 11,000 patients across 33 cancer types. This rich resource assists research on both a tumour specific and pan-cancer setting. In this webinar, I will introduce the various ways of accessing The Cancer Genome Atlas repository, navigating the multiple data types available and the tools I use for the multi-omics assessment (single and integrated) of my tumours of interest; renal cell carcinomas.

Date: May 5th, 2020 – 3:00 pm (GMT+3)

Language: English

To register the webinar, you can visit this link:

Connecting to Virtual Machine for Windows by using Putty (3-steps)

Big data requires big infrastructure. If your computer cannot handle with big data, you need to connect with a server or virtual machine to store and process your data.

I have been participating COVID19-bh20. If you are newbie like me to participate such events, and inexperienced in handling with big data in such a big hackathon, here is the first thing you need to know about how to manage such metadata: connecting the Virtual Machine (VM) via Putty.

  • First you need to download PuTTy
  • Please open the putty key generator,


  • You need to generate the public and private keys in the format requested by the admin such as RSA format, shown in yellow box
  • You need to save them
  • After generation, you need to share the public key, shown in red box, with the admin of virtual machine/server
  • Btw you need to generate a password, which is shown with green box


  • Next type the IP address to the host name/IP address box, shown in purple box
  • (Do not open without changing the Connection settings, which will be done in the following steps)
  • Then you will enter the private key to access to VM via changing the Connection settings, shown with an orange arrow


  • After clicking the Connection, denoted with orange arrow
  • Next step is to click SSH, shown in orange arrow
  • Then you need to click select Auth, shown in orange arrow
  • When you select Auth, you need to add the path of the private key via browsing it, shown in red box
  • Now you need to click OPEN to access, shown in green arrow
  • Username is given by the admin username@IP_address, highlighted with bold
  • And the password will be the password you generated as key passphrase while generating the key.

I hope you find this post useful,

For detailed information you can check with Microsoft Azure page.

PS: Although my labmates showed me how to do it before, I forgot it. Thanks to hackathon, I had a chance to refresh my old memories. In case you are a newbie like me, this post might be useful.

All the best with your analysis!

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