Hello!! Welcome to Transcriptomics Lab- Where we do all things RNA:mechanical_arm: :tada:

👨‍🔬 👩‍🔬 We are research enthusiasts from diverse backgrounds in life sciences🧑‍🔬 👨‍🔬 .In our lab we study transcriptome to provide insight into how genes work🦠 . This ReadMe provides complete analysis of an RNA-seq experiment profiling Drosophila cells after the depletion of a regulatory gene called Pasilla (Ps).

Enjoy!🌟

Summary

A SNEAK PEEK INTO OUR LAB

Pasilla gene 🧬 encodes a set of proteins that are most similar to those found in humans Nova-1 and Nova-2 are the names of two satellites. Nova-1 and Nova-2 are nuclear RNA-binding proteins that are normally expressed in the CNS and regulate splicing directly.There are numerous applications for RNA sequencing data with a reference genome, and there is no optimal pipeline for all cases. We will go over all the major steps in Reference based RNA-seq data analysis 💻, such as quality control (FastQC, MutiQC, Cutadapt), reads alignment (RNA STAR, IGV), gene and transcript quantification (HT-Seq count), differential gene expression (DEseq2), functional profiling (goseq) and advanced analysis. Using this tutorial, we were able to do differnetial gene expression analysis besides identifying the genes and pathways regulated by the Pasilla gene as they were affected by its depletion. To know more about our adventures 🤯 with the analysis process, don'y hesitate to check the following hyperlinks 🔥🔗. You will explore a lot about the field of transcriptomics in the next minutes 👀

📌 Our Workflow 📝

To check out the results of our workflow in galaxy, click on the links below:

Quality_Control | Mapping | Analysis_Visualisation_Functional Enrichment

📃 Datasets and Results 📈

Click on each image to know the detailed procedure to conduct this analysis step

Finally, after finishing our analysis 🎉🎉, our team decided to start an initiative to help all the members in learning from each other and in developing the biggest set of skills 🧰during this stage! Hence, we organized a training on Friday, where we had a workshop for each step in the workflow. The highly passionate members 👨‍🔬 👩‍🔬 who volunteered to give the workshops are highlighted in the contributions list. In this workshop, the moderators explained the purpose of doing each step in the tutorial and how it can be benefitial for the analysis 🔖 beside highlighting some improvement points. Also, they did the anlysis process practically to help the other members follow their steps. At the end, there was a troubleshooting and a Q&A session.

Getting to the end of our work, are you excited to meet our team members?!! 😍🥳🥳

👩‍🔬 Team Members 👨‍🔬

👩‍💻 Contributions 👨‍💻

Team Sub-groups	Specific Task	Contributors	Slack IDs
Quality Control	Sub-samples	Yasmeen & Eman	@Sam & @Eman
Quality Control	Full datasets	Bandana, Jaspreet, Pankaj	@Bandana, @Jaspreet, @Pankaj
Quality Control	Markdown Documnetation	Jaspreet, Bandana, Eman	@Eman, @Bandana & @Jaspreet
Mapping	Inspection of Mapping Results	Yasmeen, Bandana, Dawoud, Nirvana	@Sam, @Bandana, @Dawoud, @Nirvana
Mapping	Counting the number of reads per annotated gene	Yasmeen, Saket, Johny	@Sam, @Saket, @Johny
Mapping	Estimation of strandness	Eman, Saket, Johny	@Saket, @Johny, @Eman
Mapping	Counting reads per genes	Ankita, Favour, Nirvana	@Anku., @Nirvana, @OYEFAVOUR
Mapping	Markdown Documentation	Eman, Yasmeen, Dawoud, Johny	@Sam & @Eman, @Dawoud, @Johny
Differential Gene Expression Analysis	Identification of the differentially expressed features	Rana, Utkarsha, Osama, Nikita, Chigozie, Yasmeen, Johny, Shruti	@RanaSalah, @-Utkarsha12-, @Osama, @Nikita2Chimera, @GozieNkwocha, @Sam, @Johny, @ShrutiG
Differential Gene Expression Analysis	Extraction of annotation of differentially expressed genes	Utkarsha, Osama, Rana, Jaspreet, Nikita, Chigozie	@RanaSalah, @-Utkarsha12-, @Osama, @Nikita2Chimera, @GozieNkwocha, @Jaspreet
Differential Gene Expression Analysis	Markdown Documentation	Rana	@RanaSalah
Visualization of the DE genes' expression	Visualization of the normalized counts	Osama, Utkarsha, Rana, Ankita, TosinA, Dawoud	@RanaSalah, @-Utkarsha12-, @Osama, @Anku., @TosinA, @Dawoud
Visualization of the DE genes' expression	Computation & Visualization of the Z-score	Saket, Utkarsha, Osama, Rana, Ankita, TosinA, Diyar	@Saket, @RanaSalah, @-Utkarsha12-, @Osama, @Anku., @TosinA, @diyar
Visualization of the DE genes' expression	Markdown Documentation	Osama, Jaspreet, Utkarsha	@Osama, @Jaspreet, @-Utkarsha12-
Functional enrichment analysis of the DE genes	Gene Ontology analysis	Amira, TosinA, Bandana, Johny, Shruti	@Amira, @TosinA, @Bandana, @Johny, @ShrutiG
Functional enrichment analysis of the DE genes	KEGG pathways analysis	Amira, TosinA, Chigozie, Rana	@Amira, @TosinA, @GozieNkwocha, @RanaSalah
GitHUb Markdown Development	Format & Organization	Main ReadMe: Utkarsha, Osama, Rana, Ankita, TosinA, Bandana. Quality control: Jaspreet. Mapping: Saket, Yasmeen, Johny, Dawoud. Differential Gene Expression Analysis: Rana. Visualization: Osama, Jaspreet, Utkarsha. Functional Enrichment Analysis: TosinA	@RanaSalah, @-Utkarsha12-, @Osama, @Anku., @TosinA, @Bandana, @Jaspreet, @Sam, @Saket, @Johny, @Dawoud
Graphical Abstract Design		Rana, Osama, Jaspreet, Ankita, Diyar	@RanaSalah, @Osama, @Jaspreet, @Anku., @diyar
Advertisement	Writing post on transfer-market	Tosin	@TosinA
Training	Moderated the training workshops & presented the workflow steps practically	Quality control: Yasmeen & Jaspreet. Mapping: Saket, Yasmeen. Differential Gene Expression Analysis: Rana & Osama. Visualization: Osama. Functional Enrichment Analysis: Amira	@Sam & @Jaspreet, @Saket, @RanaSalah, @Osama, @Amira

References

Trapnell, C., L. Pachter, and S. L. Salzberg, 2009 TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25: 1105–1111. https://academic.oup.com/bioinformatics/article/25/9/1105/203994

Bérénice Batut, Mallory Freeberg, Mo Heydarian, Anika Erxleben, Pavankumar Videm, Clemens Blank, Maria Doyle, Nicola Soranzo, Peter van Heusden, 2021 Reference-based RNA-Seq data analysis (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html Online; accessed Sat Aug 21 2021

Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

Levin, J. Z., M. Yassour, X. Adiconis, C. Nusbaum, D. A. Thompson et al., 2010 Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nature Methods 7: 709. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005310/

Young, M. D., M. J. Wakefield, G. K. Smyth, and A. Oshlack, 2010 Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11: R14. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-2-r14

Marcel, M., 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17: http://journal.embnet.org/index.php/embnetjournal/article/view/200

Brooks, A. N., L. Yang, M. O. Duff, K. D. Hansen, J. W. Park et al., 2011 Conservation of an RNA regulatory map between Drosophila and mammals. Genome Research 21: 193–202. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032923/

Robinson, J. T., H. Thorvaldsdóttir, W. Winckler, M. Guttman, E. S. Lander et al., 2011 Integrative genomics viewer. Nature Biotechnology 29: 24. https://www.nature.com/nbt/journal/v29/n1/abs/nbt.1754.html

Wang, L., S. Wang, and W. Li, 2012 RSeQC: quality control of RNA-seq experiments. Bioinformatics 28: 2184–2185. https://www.ncbi.nlm.nih.gov/pubmed/22743226

Dobin, A., C. A. Davis, F. Schlesinger, J. Drenkow, C. Zaleski et al., 2013 STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29: 15–21. https://academic.oup.com/bioinformatics/article/29/1/15/272537

Liao, Y., G. K. Smyth, and W. Shi, 2013 featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923–930. https://academic.oup.com/bioinformatics/article/31/2/166/2366196

Kim, D., G. Pertea, C. Trapnell, H. Pimentel, R. Kelley et al., 2013 TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology 14: R36. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-4-r36

Luo, W., and C. Brouwer, 2013 Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29: 1830–1831. https://academic.oup.com/bioinformatics/article-abstract/29/14/1830/232698

Love, M. I., W. Huber, and S. Anders, 2014 Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 550. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8

Kim, D., B. Langmead, and S. L. Salzberg, 2015 HISAT: a fast spliced aligner with low memory requirements. Nature Methods 12: 357. https://www.nature.com/articles/nmeth.3317

Anders, S., P. T. Pyl, and W. Huber, 2015 HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166–169. https://academic.oup.com/bioinformatics/article/31/2/166/2366196

Ewels, P., M. Magnusson, S. Lundin, and M. Käller, 2016 MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32: 3047–3048. https://academic.oup.com/bioinformatics/article/32/19/3047/2196507

Thurmond, J., J. L. Goodman, V. B. Strelets, H. Attrill, L. S. Gramates et al., 2018 FlyBase 2.0: the next generation. Nucleic Acids Research 47: D759–D765. https://academic.oup.com/nar/article-abstract/47/D1/D759/5144957

Kim, D., J. M. Paggi, C. Park, C. Bennett, and S. L. Salzberg, 2019 Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology 37: 907–915. https://www.nature.com/articles/s41587-019-0201-4

Used usegalaxy.org: "The sequencing data were uploaded to the Galaxy web platform, and we used the public server at usegalaxy.org to analyze the data ( Brooks et al. 2011)."