Science topic

Transcriptomics - Science topic

Transcriptomics are the transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA produced in one or a population of cells.
Questions related to Transcriptomics
  • asked a question related to Transcriptomics
Question
2 answers
Hi everyone,
I want to perform a gene set enrichment analysis on some bacterial metatranscriptomic data. Right now the main idea is to reformat the KEGG orthology htext to gmt. I was wondering if someone has published such database or something similar already. Alas, my web searches have been unfruitful.
Thanks in advance.
Regards.
Miguel
Relevant answer
Answer
I ended up writing a python script which produced a GMT file that was valid for a GSEA. Here is the code if anyone else needs to do this:
  • asked a question related to Transcriptomics
Question
4 answers
What could be the probable techniques applied from transcriptomics to study metabolomics of plant pathogen interaction ?
Need suggestion.
Relevant answer
Answer
You can conduct RNA-Seq analysis (only in host or dual in both pathogen and host) to get the DEGs between infected and non-infected conditions and get a bit of idea regarding the genes and metabolic pathways which might be involved in inciting the disease response
  • asked a question related to Transcriptomics
Question
7 answers
Hi Everyone, I have query regarding cell type annotation for single cell characterisation. Whether automated annotation (based on identified clusters) methods or based on known marker genes (available in databases) Is better ?
Relevant answer
Answer
I think there are 2 major approaches: 1. Marker genes - the idea is that a gene with high average fold change and appropriate adjusted p value between all clusters is uniquely representing a cell type. Markers can be “canonical” - surface proteins detectable with flow cytometry, or “literature based” - genes known to distinguish cells by type and validated in literature. This is a developing area with new publications new frameworks to characterize cells and assign them with a defined type ( )
The second approach is classification based. 2. Using large collections of cells (I.e. https://www.humancellatlas.org or even cell line experiments with specific ceo type gene expression) we train a model to predict class assignment for new gene expression data. Such trained models rely on various numbers of datasets and features (hundreds) and more complex patterns than just logFC and adj. p-value. These are methods like celldex (https://bioconductor.org/packages/release/data/experiment/html/celldex.html).
Both methods have limitations in practice - many clusters can be assigned to several types of cells based on “good” logFC and p-value, so the user might choose the top values or go for unique cell type not present in other clusters. Since methods like Seurat can assign a cell type to each cell, you can also calculate proportion of cell types in a cluster and use that.
Automated annotation can also fail to assign a good cell type to a given set of cells. since there are many known types of cells and new variations are often found, combining these approaches and performing additional manual examination of marker genes, literature and expression patterns is typically required.
Hope that helps! We’re adding tutorials on this topic on our OmicsLogic portal: https://learn.OmicsLogic.com
  • asked a question related to Transcriptomics
Question
2 answers
I need a list of genes that are differentially regulated in diseases like Atopic dermitis. Is there a database for that?
Relevant answer
Answer
Markus Glaß thank you!
  • asked a question related to Transcriptomics
Question
3 answers
Hello,
I have the gene of the list of whole exome sequencing data from the paper. Can I use this list of genes to get the gene expression data? Should I download the transcriptomic data using this list of genes? How do I do that process? Also, can I get SNVs and CNVs data from those gene lists?
Thank you
Relevant answer
Answer
Simply download the raw counts as .csv and process it using DESeq2 or limma-voom in R. For limma you will have to normalize your counts first. DESeq2 is easier. You will get log2FC values along with adj. p value. If you just want to check their expression for wet lab experiments. You can also calculate the fold change without checking for statistical significance. Just average the counts of control samples and experiment samples and FC= exp/control .
  • asked a question related to Transcriptomics
Question
5 answers
I'm in the initial stages of planning a miRNA seq experiment using human cultured cells and decided on TRIzol extraction, Truseq small RNA prep kit, using an illumina HiSeq2500. The illumina webinar suggests 10-20 Million reads for discovery, the QandA support page suggests 2-5M, and I wrote the tech support to ask, who suggested I do up to 100M reads for rare transcripts. Exiqon guide to miRNA discovery manual says there is not really any benefit on going over 5M reads. I was hoping to save money by pooling more samples in a lane, so I was hoping someone with experience might be able to suggest a suitable number of reads.
Relevant answer
Answer
i am working on cardiomyopathy patients Blood samples . and wanted to do miRNA sequencing can some one please suggest how many millions reads i need to sequence 20 millions or 30 millions and also please suggest the platform as well .
  • asked a question related to Transcriptomics
Question
2 answers
Phenol - Chloroform based RNA extraction methods are most widely used for RNA extraction. I am wondering if people have tried alternate methods for cell lysis (yeast, animal cells, plants cell, etc), specifically using SDS and proteinaseK ? The idea is to avoid phase separation-based methods and toxic organic solvents like phenol.
- What kinds of buffers can be used for lysis?
- How does one get rid of SDS and other chaotropic agents used during cell lysis?
Thanks for your valuable insights.
Relevant answer
Answer
Agniva Saha the problem was RNA integrity. I don't have my full protocol notes but briefly, cell lysis was performed in a 'gentle' buffer consisting of hypo-osmotic sucrose and 0.5% Triton X-100, agitated at a low temperature. I supplemented with superasin. Following lysis (which was incomplete after 30 minutes) the buffer was supplemented with 1.5% SDS and sodium chloride, Proteinase K was added, and Prot K digestion performed at room temperature for 45 minutes.
This whole protocol is a huge tradeoff between the needs of RNA (low temperature, rapid separation from other cellular components) and deproteination using Prot K (ideal reaction temperature 50-65C, and for deproteination of DNA after ChIP we do 4-hour reactions to get rid of all the protein.) The result was that I isolated less than 1/3 of the typical yield of low-integrity RNA (RIN ~5.5-6.5).
Compare this to phenol, which is such a strong protein and nucleic acid denaturant that it results in a) near complete lysis and b) near complete protection of RNA from endogenous RNases within seconds of efficient sample homogenisation. I don't really think there's an alternative that goes close.
I will say though, every method I mentioned above for avoiding phase separation results in no loss in RNA integrity if performed properly. I haven't done a head to head yield comparison, but my routine RNA extraction protocol involves Nucleozol supernatant mixed 1:1 with ethanol and run through a column, and the yields are better in my hands than alcohol precipitation of RNA, with better purity.
  • asked a question related to Transcriptomics
Question
3 answers
What advantages does transcriptome have over proteome as the final product of gene expression is protein? Why to choose it?
Relevant answer
Answer
Not only it is much easier to work with transcriptomics but transcriptomics allow you to take into consideration the role played by non-coding sequences that are the majority of the genome and very often play a crucial role in biological regulation see for example:
  • asked a question related to Transcriptomics
Question
1 answer
Dear environmentalist in Bangladesh,
I would be happy to know where I can get the FTiR microscopy facility and the already developed protocol for micro-plastics characterisation in biological samples in Bangladesh?
Also suggest any transcriptomics marker to analyse in fish and molluscs.
Thanks in advance.
Relevant answer
Answer
This article might help you to MP analysis. You can get a detailed idea from the corresponding author of this paper.
Jabed Hasan, can you please help him to characterize the MP.
  • asked a question related to Transcriptomics
Question
5 answers
Dear colleagues, we plan to analyze human tumor tissue (lung, oral, and breast) samples using the Chromium single cell 3' gene expression solution. We need to store the collected samples for more than 3 months. What sample preparation and storage methods would you recommend?
Relevant answer
Answer
Hi Anna,
There are several freezing protocols in internet for mammalian cells.
My general protocol for freezing is like that, it should work with other mammalian cells:
*Make Cryoprotectant solution with following final composition:
10% DMSO (D2650, Sigma-Aldrich),
50% Fetal Bovine Serum,
40%-Cell culture medium without FBS,
1% antibiotic/antimycotic mixture (Penicillin/Streptomycin mixture).
*Suspend cells in cryoprotectant solution at a final concentration (5 million to 10 million cells/ml)
*Place the cell suspension in cryotubes (fill 70% only)(The cryopure tubes (cat# 72.377.002, Sarstedt or from any company)
*Place The cryopure tubes with cell suspension in Mr. Frosty™ freezing container (Cat# 5100-0001, Thermo Scientific)
*Place Mr. Frosty (with cryotubes) at -70˚C to -86C for 24h
*Either Transfer Cryotubes to liquid nitrogen, you can store for upto many years.
or
transfer these cryotubes tubes in cold boxes (-70 to -86C) and leave them there (-70 to -86C) for storage upto 1 year only
Thawing of cells:
Take cryotubes out of LN2 or Freezer (-70 to -86C) and thaw them at 37C water bath, for 5 to 10 minutes till the suspension melts.
Transfer the cell suspension in cell culture medium (you decide) in cell culture flask (you decide) under sterile hood.
Grow cells for a passage to recover from freezing shock than use for your RNA seq or other experiment after first or second passage
(I assume you know cell culture method, otherwise you can take help from anyone doing cell culture)
Good luck, any question, you can ask
Subhash
  • asked a question related to Transcriptomics
Question
3 answers
Hi all,
I want to know about the transcriptomic response to an infection. How long does it take for the genes to be activated?
Thanks for your insights
Julien
Relevant answer
Answer
Check out Aymoz et al. (2018) "Timing of gene expression in a cell‐fate decision system" (Mol. Syst. Biol. 14, 4, e8024) for a nice study on the timing of transcriptional responses mediated via MAPK to yeast pheromones, a system similar to antigen-induced gene activation pathways.
  • asked a question related to Transcriptomics
Question
5 answers
Hi all,
I want to know the kinetics of a transcriptomic response to infection. I am interested in the earliest time points, how minutes does it take a cell to activate a gene upon infection?
Thank you for your insights
Julien
Relevant answer
Answer
The cascade leading to the transcriptional factor activation is short, in the case of the canonical ones (NFkB, IMD). The accumulation of the transcripts is another subject matter. The structure of the promoters of AMPs suggest non immune related transcription factors are likely to bind and interfere with the specific transcription. HIF binding site, for instance, is frequently found nearby these genes, suggesting a potential hindrance upon redox stress.
  • asked a question related to Transcriptomics
Question
2 answers
I am interested in parallel genomic and transcriptomic sequencing at the single cell level but with the high throughput capacity of a system like 10X. I understand that this is doable at a low-throughput level via techniques like SMARTseq2, but I am wondering if such an option exists for HT methods like 10X, DropSeq, etc.
Thanks!
Relevant answer
Answer
Dear Wang, . I am Kiran work as Product and application manager for NGS portfolio in DSS Takara Bio. Takara has one automated solution known as ICELL8 cx Single-Cell System.Please do feel free to reach for any further information is required.
  • asked a question related to Transcriptomics
Question
3 answers
I am following the way how a previous paper (PMID: 30948552) treating their spatial transcriptomic (ST) data. It seems like they combined all expression matrix (not mentioned whether normalized or log transformed) of different conditions, and calculate a gene-gene similarity matrix (by Pearson rather than Spearman), and they finally got some gene modules (clustered by L1 norm and average linkage) with different expression between conditions.
So I have several combination of methods to imitate their workflow.
For expression matrix, I have two choice. The first one is a merged count matrix from different conditions. The second one is a normalized data matrix (default by NormalizeData function in seurat, log((count/total count of spot)*10000+1)). For correlation, I have used spearman or pearson to calculate a correlation matrix.
But, I got stuck.
When I use a count matrix, no matter which correlation method, I get a heatmap with mostly positive value pattern, which looks strange. And for a normalized data matrix (only pearson calculated), I got a heatmap with sparse pattern, which is indescribably strange too.
My questions:
  1. Which combinations of data and method should I use?
  2. Would this workflow weaken the correlation of the genes since some may have correlations only in specific condition?
  3. Whatever you think of my work?
Looking forward to your reply!
Relevant answer
Answer
You install the R and Rstudio software and visit this web site:
Heatmap in R: Static and Interactive Visualization - Datanovia
  • asked a question related to Transcriptomics
Question
1 answer
Hi all,
I am developing a high-throughput RNA extraction protocol for xylem vessels. Pre-emptively, the samples are going to be homogenized in a Genogrinder with a cryoblock attachment and then transferred to a 96-well format for total RNA extraction. For ease of transfer to the 96-well, I was thinking of maybe mixing the homogenized tissue with RNAlater-ICE purely for the fact that transfer of dry material to a 96-well will be too messy and not very high-throughput. Does anyone have experience with RNAlater altering the transcriptome gene expression profile? I read these attached articles on normal RNAlater and the influence it can have, but they only submerged whole tissues instead of ground tissues. What would be a good alternative to RNALater and RNALater-ICE? I am still inbetween using a 96-well plant RNA extraction kit vs CTAB RNA extraction. Would submerging the homogenized xylem in for eg. a kit's lysis buffer greatly affect the RNA integrity? Thanks in advance!
Relevant answer
Answer
Hi
RNA later is generally used when the whole the integrity of the RNA inside the tissue needs to be maintained, if it is not possible to freeze in the Liquid Nitrogen, immediately. It is possible that RNA later may also cause problem in RNA isolation, if not removed properly. Its removal is relatively easy from the surface of whole tissues, than if it is added to the homogenized tissue. For your experimental needs, the TRIZOL/TRI reagent may be a better good option. It maintains the quality of RNA in the homogenized tissue. And it should be useful of high-throughput isolation of RNA
  • asked a question related to Transcriptomics
Question
3 answers
I want to do RACE and I don't have any previous exposure. I am planning on using RLM-RACE kit invitrogen. But I am confused in a few places:
1. Should I choose the 5' or 3'? What criteria should I base my decision on?
2. After use (5' or 3') how do you store the product?
3. For sequencing do I clone it? Or do I give the product directly? I read that for the NGS-based approach there is no need for cloning.
Relevant answer
Answer
Pragya Tiwari Mam i am also going to use the same kit to do 3` RACE.I am planning to do pcr using 3` outer primer and 5` gene specific primer. Can i amplify my 3 kb size gene of interest using this 3` RACE only.Kindly reply
  • asked a question related to Transcriptomics
Question
1 answer
Apologies in advance; I'm new to transcriptomics, especially bacterial transcriptomics...
I generated DEG lists in a set of experiments using Staphylococcus aureus (UAMS-1 strain). In my results tables, each gene is represented by a non-redundant Refseq accession number (WP_xxxxxxx format), but most do not have conventional gene names (perR, bshA, etc.). Is there any relatively straightforward way for me to use these lists of (WP_) identifiers to perform GO, KEGG, or similar pathway type analyses for S. aureus, or is there a way I can convert them to relevant identifiers that can be used for these types of analyses? I would like to get as much insight out of this data as possible.
Thanks much!
Relevant answer
Answer
Hi,
you can submit your accession fasta file to the KAAS-KEGG server. The result will have a KO list and the pathways name details etc
  • asked a question related to Transcriptomics
Question
5 answers
Hello dear fellow scientists,
I would like to ask some basic naive questions:
1) when scientists perform a transcriptomic study, lets say to compare a mutant to a Wild type plant, they tend to look at the genes that are at least 2 times more or two times less expressed between the two samples, why not all genes that are differentially expressed between the two genotypes? is it because it is more reliable ?
2) Usually when you perform a transcriptomic and a proteomic study (on the same sample and same conditions) you only find a low number of genes that show the same expression pattern (up-regulation or downregulation) between the two experiments, why ??
I did a transcriptomic and a proteomic study on a mutant and I found a small overlap between the differentially expressed genes and the differentially expressed proteins,
I mean its not surprising overall but I can't think of an explanation,
is it related to the degradation of transcripts ? post-translational regulations ?
I hope my questions are clear..
Sincerely
Relevant answer
Answer
Yes indeed... Polyadenylation controls many things, but mainly the stability of the transcript. It is well known in mammals that for instance those proteins involved in signal transduction have the tendency to harbor weaker polyadenylation signals, which conditions the stability of the mRNA and the protein levels.
  • asked a question related to Transcriptomics
Question
4 answers
Hi!
I have jellyfish samples (gonads and tentacles) preserved in ethanol and stored at -80º for about 2 years. I would like to know if I can use these samples to extract RNA for transcriptomics.
Thank you all in advance!
Relevant answer
Answer
Hi,
I recommend using NanoDrops and agarose gel electrophoresis to check the quality and integrity of your extracted RNA sample, so you can use it if it has good quality and integrity.
  • asked a question related to Transcriptomics
Question
2 answers
Hi!
In single cell droplet sequencing, 2 cell lysis buffer are often chosen: 0.5%CA-630, or 0.2% sarkosyl 160 + 6 % of the Ficoll PM 400. What is the difference of these 2 choice in RNA yielding, mRNA completence and etc.?
Thanks!
Relevant answer
Answer
Hello Zonghan Gan based on my understanding both are used for protein isolation. Igepal or CA630 is nonionic detergent usually used for membrane proteins, the latter is a modified gradient lysis buffer that is milder than SDS in denaturing proteins mixed with ficoll it allows for size separation and inhibiting protein aggregates or cell clumping. I would think both would give variable RNA yield based on their use. Igepal usually gives a higher yield as it disrupts membranes efficiently but it depends on the concentration you use. As for sarkosyl I expect RNA yield to be much lower and contaminated with RNA binding proteins. If I had to choose I would choose CA630. check ref (PMID: 26242641) for CA630 efficiency in RNA yield / efficiency. Good luck.
  • asked a question related to Transcriptomics
Question
6 answers
Hello,
I am looking to obtain global RNA-Seq data for either E. coli or P. putida. I assume RNA-seq data is publicly available for many microbes, but I am unsure where I can access this information. Does anyone have insight as to what website or database I can find this data?
Many thanks,
Shawn
  • asked a question related to Transcriptomics
Question
1 answer
Hi. I'm dealing with spatial transcriptomic data and find the gene of interest. Now we need to know what transcript isoform of the RNA was expressed in our sample. However, NCBI shows this gene has 3 isoforms while ENSEMBL only shows one. Thus we want to run spaceranger with the reference of NCBI, but 10X only provides the mice reference of ENSEMBL. So I downloaded the gff and fna file from NCBI, transfered the gff into gtf, then generated the reference directory as taught in the spaceranger tutorial. But spaceraneger can not work with this reference directory. It just crashes in the middle of the process. Did I do something wrong when generating the reference? Or does anyone have the mice NCBI reference for spaceranger?
Relevant answer
Answer
Hi Kleran
I think you followed the support (https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/advanced/references) and I see 2 points that could be at the origin of your problem:
- the first one is the genome you selected, is it mm10 genome?
- the second one is that data you downloaded must be compatible with STAR aligner, a point you need to dig in...
all the best
fred
  • asked a question related to Transcriptomics
Question
4 answers
Our lab has sent rat cardiac tissue for sequencing and have obtained indigestible fastq data files. Is there a software I can use to organize these fastq sequence files in order to obtain meaningful results?
Relevant answer
Answer
You may follow this pipeline.
Let me know if you are interested to outsource to my lab www.eminentbio.com
  • asked a question related to Transcriptomics
Question
4 answers
Differential Expression tables in R - transcriptomics
I want someone to explain to me please, what are the de (differential expression) tables are in RNAseq experiments.. I know they contain the P-value, adjusted P-value and log2fold.. But I am confused about what are these values measured for?
For example:
I have 231 sample, but they are collected according to age, bmi, and sex. so, the de age table is different than the de bmi and de sex.. although the entry numbers are the same, BUT, the p, p.adjust and log2fold values are different.
Can somebody explain to me why??
Relevant answer
Answer
Differential expression of Transcriptome means the difference between the expression of genes whether up-regulated or down-regulated. The read counts are deduced by mapping followed by gene annotation. Read counts means the number of time a sequence overlap to the genomic feature such as gene or transcripts. These all read counts of given samples (eg. control and treatment)are provided to the respective tool in form of a matrix which is then compared by log base 2 (control /treatment). P-value is the probability of occurrence of the test randomly. A P-value less than 0.05 is considered best.
  • asked a question related to Transcriptomics
Question
2 answers
Hi everyone,
At the moment I am designing a spatial gene expression experiment using the 10X Visium assay. There are a few papers out there that have used this assay. There are also several packages available to analyze the data (e.g. Seurat). However, if I am correct, none of these methods take biological replicates into account. In other words, is it possible to align different slices of biological replicates and then perform differential expression analysis to compare conditions?
Relevant answer
Answer
Honestly, this tech is still in its infancy, and is also _suuuuuper_ expensive. Most people are hard-pressed to make use of the gigabytes of sequence data they get from a single experiment, let alone take that forward to N=3 or more.
That isn't to say what you're proposing is impossible, or even impractical, but more to suggest that for such specific questions there are usually cheaper, more specific solutions. You absolutely can compare multiple RNAseq datasets, and adding a spatial component to this should also be possible provided your segmentation/tissue designation is solid enough (or you have cell-specific markers to aid comparisons), but really...I think you need to carefully formulate your question and work out exactly what this very, very expensive (but very, very neat) approach can do that other more conventional methods cannot.
Also, obviously I am jealous of your budget and resources, but that kinda goes without saying. ;-)
  • asked a question related to Transcriptomics
Question
5 answers
I am looking for ideal configuration details for a workstation to perform a metagenomic, transcriptomic and whole genomic analysis.
Relevant answer
Answer
You may see the image attached herewith. I hope this resolves your query.
  • asked a question related to Transcriptomics
Question
3 answers
Say, we'd like to publish an experimental paper in which a certain metabolic pathway is investigated from the points of various methods, such as RNA-Seq, mass spectrometry, enzyme assays, gene knock-out, etc. RNA-Seq is used for the analysis of differential expression of genes encoding the enzymes related to the pathway, so only a few tens (out of thousands) of differentially expressed genes are discussed in the paper. Nevertheless, we have to publish the full set of RNA-Seq raw reads since virtually any journal requires sequencing data availability.
There are no problems with uploading our reads to the SRA database and inserting an SRA accession number into the manuscript. But we'd like to analyze the rest of our RNA-Seq data and write one more article to publish elsewhere (without overlapping the aspects discussed in the first paper). Thus, we'll upload the reads in the SRA once, and then refer to the same accession number in two different articles. Is it OK? Is it ethical? Are there any copyright issues to face?
Relevant answer
Answer
Adding to the above points, this is a common practice and often adopted by many researchers, however, the problem only arises when publishing the research without providing any data to the public repositories.
  • This is done in order to protect the data from others in fear that they could use the data in their studies before the author/owner of the data.
  • This practice is not good and should be discouraged. Many journals/editors/reviewers failed to prevent this and let the author publish without providing the data for public access. This is a huge problem in the way of transparent and open/reproducible science.
  • It is basically the responsibility of journal to take care of.
  • asked a question related to Transcriptomics
Question
1 answer
Hello everybody,
I am PhD student and i am working with a nonmodel tree under drought stress. I want to know if i can use GSEA in my experimental design with my nonomodel plant. One of my experiments consist in three control plants and three stressed plants. I took total RNA and performed a RNA Sequencing, "de novo" assembly and DE analysis, thus I have about 190.000 genes with its normalized counts (TMM). I could create the files: data set (.gct) and phenotypes labels (.cls) .
1) But I can´t or I don´t know how to create a Gene sets file (.gmt) matched with GO terms because my IDs data set file comes from Illumina, they are like: c0001_g1. And there is more,
2) I do not quite understand if it is necessary to have a chip necessarily to run the analysis.
I would be very grateful in an answer adapted for biologists not specialists in bioinformatics.
Thnaks
Edgardo,
Relevant answer
Answer
Hi Edgardo,
Technically, you still can perform GSEA even though you are using non-model plant. Some of the necessary steps/files are as follows:
1. a gmt file. I don't reckon that there is a readily available gmt file for your plant. So, you may be able to use the gmt file of the closest plant relative to your species. This publication (https://pubmed.ncbi.nlm.nih.gov/23632162/) and this database (http://systemsbiology.cau.edu.cn/PlantGSEA/download.php) may help.
2. You will need to map your gene ID to match that of the gmt file. To do so, you will need to annotate the transcriptome which you have assembled. I am not familiar with the annotation process, but this manuscript may help ( ). After the transcriptome annotation, you can map your transcripts to genes. If the gene ID matches that of the gmt file, the files can be used directly for GSEA. If not, you will need to remap the gene ID before GSEA. BiomaRt (https://plants.ensembl.org/biomart/martview/76f17c602cf7f4cb9adc75e9a97c87df) may be helpful in this case.
I hope this helps.
Regards
Hong Sheng
  • asked a question related to Transcriptomics
Question
3 answers
Transcriptomic experiments was conducted and I obtained 355 differently expressed genes. Besides, a number of enrichment was analysed such as NOG, KOG, COG, GO, KEGG etc. There are about 10 plots generated. However, which plot should be shown in a scientific article?
Relevant answer
Answer
Anything which makes sense and provide/help in visualizing the thing you write and describe in paper.
I guess if you are the author, it should be you to decide what to put in your paper. If you are not sure, ask co-authors, if you have any.
  • asked a question related to Transcriptomics
Question
4 answers
I have RNA-Seq data for different cell lines and I'm looking to find lncRNAs which maybe deferentially expressed.
Relevant answer
Answer
Is there any method to work with NONCODE in R?
  • asked a question related to Transcriptomics
Question
2 answers
For RNA profiling of frozen tissues, researchers recommend to use single-nuclei RNA sequencing instead of single-cell. What is the reason for this?
Also, what is the best way to freeze cells for RNAseq at a later time?
Thank you very much for your help, be safe!
Relevant answer
Answer
If it is not feasible to process fresh tissue, fresh-frozen tissue samples can be used for Single Cell RNA sequencing. Before freezing, the tissue could be dissociated into a single-cell suspension. Cells can then be cryopreserved in a suitable freezing medium. When freezing cells, we recommend starting with at least 1 million total cells to recover sufficient numbers post-thaw since almost half may be lost in the freeze-thaw process and during the wash and centrifugation steps.
If the tissue is frozen whole, it is more challenging to isolate viable single cells after thawing. In this case, it is recommended to extract nuclei from the snap-frozen tissue. Please refer to this article: How do you isolate nuclei from snap-frozen tissue for 3’ gene expression profiling?
The choice of one method over the other(dissociating and then cryopreserving vs. snap-freezing whole tissue) would depend on the sample type. If a well-optimized tissue dissociation protocol is already available that yields >90% viable cells with no cell type bias, then dissociation followed by cryopreservation is preferred. However, if the tissue dissociation protocol is not optimized then it may be better to snap-freeze the tissue whole. It is recommended to try both approaches with a non-precious sample before the actual experiment to know which approach is giving better yields.
  • asked a question related to Transcriptomics
Question
1 answer
Hi all,
I am collecting blood from human donors on TEMPUS tubes for RNA stabilization. After RNA extraction, we want to use the tubes for different transcriptomics downstream applications that will take place in different labs.
I was wondering if aliquoting the blood/TEMPUS buffer mix right after collection was a viable option to optimize shipment of the samples. The workflow would be as follows: collection of blood on TEMPUS tube, thorough mixing/vortexing to ensure complete mixing of the blood with the TEMPUS buffer, then aliquoting of the entire contents of the TEMPUS tube into three Falcon 15mL, and storage at -80°C before shipment.
Has anyone ever done this or something similar? I may be paranoid but I am worried that the tube itself might be optimized for RNA preservation (e.g. special coating of the glass...). Better safe than sorry!
Relevant answer
  • asked a question related to Transcriptomics
Question
4 answers
I need to perform ligand-receptor interactions map for the data of bulk RNA sequencing (mouse). In all methods which I found they want to have matrix with columns of gene symbol and mean expression values for each cell type. I have only tsv files with metadata and counts. Do you know how to get this from the data I have. Is there any R library/protocol/tutorial for that? Which method you suggest for obtaining receptor-ligand Interactome for bulk RNA?
Here is how my metadata looks like:
id nCount_RNA nFeature_RNA PercentMito ERCCCounts PercentERCC Animal Plate
X11_E1 569589 11505 0.00331115945006 20 3.51E-05 11 11 X11A10
.......
Birthdate Gender Organ CellType RowID ColID
old Female BM GMP E 1
.......
Counts:
gene X11_E1 X11A10 X11A12 X11A3 X11A5 ........
Gnai3 23 4 22 25 94 ..........
.......
  • asked a question related to Transcriptomics
Question
2 answers
I tried to run this function sitetest to perform Site-level Differential Methylation Analysis using IMA package but I got error message.
sitetestALL = sitetest(dataf,gcase="KO",gcontrol="WT",testmethod ="wilcox" ,Padj="BH", rawpcut = NULL,adjustpcut =NULL,betadiffcut = NULL,paired = FALSE) and I got this error message: Error in wilcox.test.default(x[1:length(lev1)], x[(length(lev1) + 1):(length(lev1) + : not enough (finite) 'x’ observations
Can you help me to solve this problem?
Relevant answer
Answer
Hi
I suggest using if and else in lapply.
for example:
if(nrow(coulmn1)> 30) {
x <- with(data, cor(a, b))
}
else {
x <- 0
}
This is a good solution when the number of samples are small.
  • asked a question related to Transcriptomics
Question
2 answers
Hi,
I have raw data from [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array that I want to process using the Expresso function for Affymetrix microarrays.
My samples include tumor tissues and matched adjacent tissues.
I am planning to use the RMA method which includes RMA+Quantiles+pmonly+median polishment, but it would be great if you share your experience with me. Which methods would you prefer to combine according to your statistical experience in this field?
Background correction Options:
  • Affymetrix MicroArray Suite (MAS)
  • Robust Multiarray Analysis (RMA)
  • None
Normalization Options:
  • Quantiles
  • Lowless
  • Cubic Spline (Qspline)
  • Invariant set
Probe match correction Options:
  • Perfect-Match (PM) only ["pmonly"]
  • Subtract with Mismatch (MM) ["subtractmm"]
  • Affymetrix MicroArray Suite (MAS) ["mas"]
Values presentation Options:
  • Average Difference ["avgdiff"]
  • Li & Wong (2001) outlier removal ["liwong"]
  • John Tukey median polishment ["medianpolish"]
Thank you in advance,
Sevcan
Relevant answer
Answer
I appreciate Sevcan Atay for the valuable topic. Would be interested to know as well.
  • asked a question related to Transcriptomics
Question
4 answers
As we know in nucleic acid extraction/purification process using a young plant material is better than old ones. It would be resulted better nucliec acid purity because old plant material has higher sugar and phenolic compund than younger material.
However i did not know It would be affected to transcriptome profile or not? Since the expression of several gene might be different in old and young plant material.
Does anyone have an idea?
Relevant answer
Answer
Hi Mabrur, it is a very well-known fact that transcriptome expression is highly variable and is age-specific, time-specific, gender-specific, organ / tissue specific, environment-specific and also differs individual to individual and the same matters for plants too. It all depends what you are trying to find in the transcriptome and does that entity have any impact on its expression profile due to any or all of the above-mentioned factors and based on this decide whether it is better to go for transcriptome mapping or transcriptome assembly in your case. Also you need to take in account several other factors like adopting proper extraction protocols, yielding good proportion of the transcriptome you are interested (coding / non-coding / whole), good library preps, and using proper transcriptomic controls for judging asay variations, use substantial amount of replicates (biological and technical both) etc. Only then you can rely on the results
of the transcriptome experiments.
Regards...
  • asked a question related to Transcriptomics
Question
10 answers
What do you think about the balance between exploring widely different designs vs. local optimization at different levels of biology (genomics, transcriptomics, proteomics, anatomy, etc.)? Which levels are more or less modular or plastic?
In the endocrine system, for example, one feels that having tropic hormones (i.e., those controlling the release of other signaling hormones at other glands) may offer a finer and perhaps more robust regulation, compared to a being where all hormones were non-tropic. However, the anatomic location of elements in these networks is not trivial. For example, in the renin-angiotensin-aldosterone system, renin is produced in the kidney, and aldosterone eventually exerts its effects in the kidney as well. However, the intermediate step by angiotensin-converting enzyme (ACE) mainly occurs in the lungs, which could introduce a delay in the regulation.
Do we have good explanations for the sites of production and action of different hormones in the body? Are there common principles to be learned as optimized by evolution in this respect? Or are happenstances/contingent evolution stronger determinants?
Thank you for sharing your thoughts!
Relevant answer
  • asked a question related to Transcriptomics
Question
7 answers
Dear colleagues!
I am trying to figure out how to do an extraction for soil microorganisms for further metabolomic analyses. I am only interested in the microbiota part, so I would like to discard any organic matter present in the samples.
Do you know any useful method for that? Any suggestions that could help me?
Thanks for your help!
  • asked a question related to Transcriptomics
Question
4 answers
I want to use salmon tool to quantitate the transcripts coming from different tissues. All the transcripts I've found seems to be an assembled reference.
I think it would be easier to find tissue-specifc references as this can potentially make the analysis more robust!
Relevant answer
Answer
The ideal is you produce your own control (ideally, at least three replicates to each condition + three replicates of control samples), even if you find fastq files of tissue transcriptomes.
The library building process and the sequencing itself can introduce some "biases". So, if you have all sequencing samples produced at the same time with the same conditions, they will be more comparable and your results more reliable.
As Amir Vajdi said if you have no disease or stress or other condition and you want to compare the expression between tissues, you can compare one tissue to the other.
  • asked a question related to Transcriptomics
Question
3 answers
I have a list of LipidMAP IDs for a bunch of metabolites and I need them all converted to Human Metabolite Database IDs. I have over 300 entries so I need a way of converting these IDs in bulk, all at the same time. I have tried The Chemical Translation Service but this doesn't seem to have updated LipidMaps entries as it doesn't find matching HMDB IDs for a lot of the LipidMAP IDs. Does anybody know of a software or service that allows conversion of LipidMAP IDs to HMDB IDs in bulk?
Relevant answer
Answer
Dear Frankie;
I usually use a trick on MetaboAnalyst web server (www.metaboanalyst.ca). I dosent have direct conversion system. therefore, put the names in the enrichement section. The database will offer you most of the HMDB IDs.
Browse to the homepage
There is a red line " Welcome>> click here to start << "
Then use the "enrichment analysis" section.
Best
  • asked a question related to Transcriptomics
Question
3 answers
I am trying to optimize a spatial transcriptomics assay and I have to validate the tissue permeabilization with in-house-printed slides before buying the final ones. But I am experiencing problems with Codelink protocols. It seems that I am not printing any probe on the slide therefore I can not see the RNA footprint after the tissue permeabilization.
I ordered an biotilinated amine-modified oligo but I can not detect it, therefore I think that I am not actually printing. If somebody have used this protocol before, can please tell me the critic parts which I have to pay attention to?
Relevant answer
Answer
Our CRO lab (Arrayjet Ltd.) have successfully printed oligos on Codelink slides for use in spatial transcriptomics projects, mentioned in this Nature Comms paper: https://www.nature.com/articles/ncomms13182
The slides do have a limited shelf-life, so your problem may lie there.
If either of you are still working on this, I'd be pleased to arrange a call/webchat with our Application Scientist and Project Manager who extensively optimised the printing protocol.
I can be reached directly at: shawkings@arrayjet.co.uk or simply drop me a line here and I'll do what I can to help.
Best wishes,
Sam
  • asked a question related to Transcriptomics
Question
5 answers
I have been trying to check the supplementary file for this paper entitled "Transcriptomic and proteomic analyses of the pMOL30-encoded copper resistance in Cupriavidus metallidurans strain CH34" on the journal web page. Unfortunately, the journal didn't provide it. The paper doi number is
Please help me in this regard.
Thanks
Relevant answer
Answer
Frederic Lepretre Probably. By the way, I found the corresponding author's email by the google search and got the information that I need.
  • asked a question related to Transcriptomics
Question
3 answers
I have obtained CNV data from the TCGA GDC portal. The data is barcoded and is difficult to understand. I have checked for different annotation tools like CNVTools, PennCNV, QunatiSNP.... Can anyone suggest which will be a better annotation tool for annotating CNV data from GRCh38.p0 Genome build???
Relevant answer
Answer
We have used multiple tools to analyze TCGA data. Links have been attached in the paper for easy reproduction. It will help for a good start with TCGA
  • asked a question related to Transcriptomics
Question
6 answers
Can anyone help with a simplified work flow for Allele Specific Expression from RNA Seq data?
Relevant answer
Answer
Just to add a very different approach to all the ones mentioned already: you could also use competitive read mapping. The idea is that you map your reads for individual 1 to two parental genotype-specific references. Then you parse both BAM files and compare the alignment and mismatch score for unique mapping reads overlapping a given genomic interval. Then count the reads in the sets that are best in each reference.
I wrote a Python program that can streamline this hosted on GitHub (https://github.com/santiagosnchez/CompMap). In addition, the program will assign the number of equally good mapping reads to each genotype-specific count. You can do this with a direct measurement or stochastically by sampling from a binomial distribution.
I've tested it using simulated reads and it works pretty well for genes with species-level divergence. I still need to test it with genes with less/population-level divergence.
Feel free to give it a try.
  • asked a question related to Transcriptomics
Question
4 answers
Can you recommend a good review on methods for transcriptome analysis? In our lab we expand human T cells and magnetically seperate cells being positive for our target protein. Now, we want to compare transcription status between these cells and control sets. For this, i'm looking for a good review comparing different transcriptomic techniques (eg. singe cell RNA seq., microarrays, ht-RNA seq etc.) with a special focus on costs, time requirement, advantages and limits. Many thanks and kind regars, Marc
Relevant answer
Answer
Hi colleague
Find the following URL, may help you:
Regards..
  • asked a question related to Transcriptomics
Question
5 answers
We recently identified a novel transcriptional isoform of a gene in brain. It's endogenous expression is very low compared to the annotated one. Exon 1 of the gene is missing, and a portion of a long terminal repeat (33bp) spliced into Exon 2. Thus, the first ATG for this new isoform is found in Exon 6 due to the loss of Exon 1. my question is: 1. is the new isoform translated into protein? 2. if not, how can we test it is a non-coding RNA? 3. If it is translated, how can we test the protein it makes.
Thank you very much!
Relevant answer
Answer
northern blot analysis
  • asked a question related to Transcriptomics
Question
2 answers
We are planning to do RNA-Seq for RNA extracted from two types of samples:
  1. Routine snap-frozen mouse fetal tissues
  2. Laser microdissected tissue sections (FFPE sections and/or cryosections)
We only need gene expression profiling, not any deeper data. We are considering Lexogen Quantseq and Qiagen UPX sequencing. UPX is cheaper but not sure if it has been applied for this type of samples. Are there other methods worth considering?
Relevant answer
Answer
Informatics analysis would be the limiting factor. If you are going with commercial sequencing, make sure it is included.
  • asked a question related to Transcriptomics
Question
3 answers
I will start a study using peripheral cells in blood samples of new coronavirus infected individuals in São Paulo, Brazil. The aim of the project is to perform epigenetic and transcriptomic analyzes in these patients. However, is necessary to inactivate the virus first. For this, we intent to use Biomerieux lysis buffer. Can this inactivation process affects the analysis?
Thank you for attention.
Relevant answer
Answer
I will also recommend following the recently published article in Nature journal.
  • asked a question related to Transcriptomics
Question
5 answers
I'm working on transcriptomic data from Physcomitralla patens mutants, and would like to check differentially expressed genes lists for functional clustering, enrichment an so on.
The issue is, I used the genome assembly and annotation from Phytozome, so my gene IDs are not recognized by any GO analysis platform. I also couldn't convert my IDs to any recognizable dene IDs.
For most genes I have Gene Ontology IDs, though.
Is there any platform that allows to start such analysis with GO IDs and not gene IDs?
Thanks to everyone!
Relevant answer
Answer
You can use a simple script which I have written in python here for GO enrichment with a list of input genes:
(just run it in google colab)
  • asked a question related to Transcriptomics
Question
8 answers
I developed a time-course study of kidney fibrosis and evaluated the expression of nominated genes using real-time PCR. Evaluation of genes expression during time-course demonstrated oscillatory patterns of expression in both sham and treated mice groups, now my question is how can I interpret the oscillatory pattern of these genes. I have 5 diagrams with different oscillatory pattern and I'm not sure how to discuss them.
Relevant answer
Answer
Hi again, Ali Motahharynia
I think you should plot your points using error bars with replicates. Unfortunately, you can not conclude anything because you don't have replicates (at least three per time point).
Now, assuming that your points are the average of some replicates, what is exactly your question? I mean, they look similar in their evolution, but I don't know what you want to know about them. Could you be more specific in your question?
And finally, an advice for the future, please put names in your plots.
Regards,
  • asked a question related to Transcriptomics
Question
4 answers
We want to run 10x Genomic Visium spatial transcriptomics on lung tissue from COVID-19+ patients. How can we inactivate the virus so it's safe to work with the tissue at BSL2? The first step is to freeze the tissue in a isopentane/liquid nitrogen bath. After putting the tissue on the slide it is incubated in 100% methanol for 30 minutes at -20 degrees. Would either of these steps inactivate the virus?
Relevant answer
Answer
Hi,
I Think 100% methanol fixation would be useful for getting rid of infection causing virus. Apart from that you could try fixing tissue by using 4% PFA or 10% Neutral Buffered formalin. That would be effective! Also you may try working with RNA later. It should destroy viral protease
  • asked a question related to Transcriptomics
Question
3 answers
When you assembly a transcriptome with Trinity, for example, only one final fasta is created with the transcriptome. The de novo transcriptome assembly does not assemble transcripts for the separate alleles, and usually there is only one transcript generated and it is mapped to both alleles. Is there any software that allows assembly de novo and with reference genome transcrips for separete alleles?
Relevant answer
Answer
Thanks a lot for your response Karol and Ireneusz!
  • asked a question related to Transcriptomics
Question
5 answers
I have been asked to discover what are the genetic causes that allow Moloch horridus to be able to drink water through the skin and the change of colour. There was no genomic information about this specie, so we have sequenced it, assembly and annotated structurally (thanks to ab initio and transcriptomics approach) and functionally through GO terms with BLAST2GO.
However, we have to use comparative genomics in order to identify the genes. We thought of using 1 to 1 orthologues because the most part of these kind of projects use it, but if we are comparing close species that do not share this property I don't see the point in looking for them.
Another doubt I have is about the study of expansion or reduction of family genes and the use of a phylogenetic tree. And the last thing is about enrichment of GO terms, I would like to know why is it useful. Thank you so much
Relevant answer
Answer
Got it. Regrettably, I have not had sufficient time to explore positive selection, only getting as far as installing and implementing PAML, which is a useful tool for such measurements. Although we tried an analysis, we did not use sufficient rigor for me to speak knowledgeably besides to say this seems like a useful way to distinguish orthologs.
  • asked a question related to Transcriptomics
Question
7 answers
Bivalve specimens are preserved in 70% ethanol for about 6 months. What is possibility of being able to extract DNA/RNA of bacteria that was previously consumed?
Relevant answer
Answer
Hello, alcohol toxic for bacteria but not for DNA. If there is no DNAse in you culture samples the extraction of DNA is possible for 95%.
  • asked a question related to Transcriptomics
Question
2 answers
When Venn diagrams are used to analyze microarray data, I know that Poisson distribution is sometimes used to decide if the number of genes in the intersection of different treatments is higher than that expected in a random distribution. Certain requirements must be met in Poisson distribution, for example, all the genes present in the microarray should have the same probability to express, and is a distribution for infrequent events. Moreover, in this distribution the mean must be equal to the variance, etc.
In our laboratory we would like to apply this statistical reasoning to Venn diagrams of overrepresented GO terms. But in this case, I suppose that the total number of GOs is the number of tagged GOs that we have.
Does this kind of data satisfy the requirements for a Poisson distribution?
Has anybody else applied this statistical reasoning with overrepresented GO terms?
Relevant answer
Answer
you can use this online tool DiVenn which will generate a nice graph https://divenn.noble.org
  • asked a question related to Transcriptomics
Question
4 answers
Would you please tell me what come in the genomics and bioinformatics skills?
What is difference between Bioinformatics and Transcriptomics?
Relevant answer
Answer
Transcriptomics is the analysis of gene expression in a certain condition, i.e. exploration of the transcriptome by analysis of RNA. Genomics is larger and comprises all analyses that use exploration of the genome (at the DNA, RNA, protein and even epigenome levels) to understand biological processes. It comprises both wet lab experiments (biological setting, RNA/DNA isolation, libraries preparation, sequencing) and bioinformatics analysis (reads mapping, counts, statistics...). Bioinformatics is also larger than genomics analysis, and can involve any computer science topic that can be used for analysis of biological / health data, for sequencing data but not only: it can be used for image analysis, signal / noise ratio filtering, regulation networks building...
  • asked a question related to Transcriptomics
Question
4 answers
To be used in Genome MuSIC.
Relevant answer
Answer
  • asked a question related to Transcriptomics
Question
1 answer
Dear All, 1 ug of permissible quality FFPE RNA is transcribed to cDNA through SSIV VILO RT kit.
I have Qubit v.4 device, which can measure cDNA to a good extent. The cDNA ranges from 25-100 ng / ul for these FFPE samples through Qubit. Now, is there any rule regarding cDNA input to create libraries for RNA SEQ to be performed through Ion Torrent platform?
For FFPE samples, we always use more template to create libraries (like in whole exome or whole genome sequencing), but for cDNA library I am not much sure that what should be the starting cDNA amount to get 100 pM range libraries. If I make libraries with starting material 50-60 ng of total cDNA, would it be suffice, if my samples are FFPEs in origin?
Also, if someone does not have Qubit to measure cDNA, so what strategy we should use for the cDNA starting quantity for RNA SEQ library preparations after reverse transcribing the RNA?
Thanks....
Relevant answer
Answer
Jawwad,
It will all depend on the library preparation kit that you are using. Different kits require different starting amounts.
  • asked a question related to Transcriptomics
Question
10 answers
Does anybody know about published high-throughput mRNA expression data, with microRNA over-expression or knockdown vs. control experiments in HEK293 cells? It can be using any technology like micro arrays, RNA-seq, CAGE or other. Could you point to the paper and/or GEO accession?
Relevant answer
Answer
I think this website can help you.
  • asked a question related to Transcriptomics
Question
7 answers
thanks for your suggestions
Relevant answer
Answer
I have a follow-up question to that:
For a dual RNAseq in which RNA of infected cells and bacteria are collected together, which reagent should I use?
I guess RNAprotect Bacteria Reagent would be able to release and stabilize both eukaryotic and prokarytoic RNA?
Thanks
Sven
  • asked a question related to Transcriptomics
Question
3 answers
Does anybody know of a paper or a database, where I can get such data?
I would appreciate any information.
thanks
Assa
Relevant answer
Answer
As Rafael said, it is tough to find an integrated database, and I also don`t know any. However, I have some papers with me on the subject. One is a review on the topic of transcriptomic and proteomic data integration that might have something to could aid you in this matter - this paper provides an extremely comprehensive review, with a lot of focus on bioinformatics. Here [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637682/] .
The other is a study published in Sci. Rep. that uses three types of data (genomics, transcriptomics, and proteomics), which they provide the link to download their results. It might be useful to you, in some way. Here [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700448/].
Cheers.
  • asked a question related to Transcriptomics
Question
3 answers
The goal is to perform an RNA-seq analysis of dendritic cells performing phagocytosis of tumor cells, but both the DCs and tumor cells are mouse-derived cells. Is there a known or established method to distinguish both types of RNA (e.g. tumor cell DNA/RNA labeling techniques,...)? Since tumor cells have a Balb/c origin and the DCs C57bl6 origin, would it be possible to distinguish both types of RNA on SNP level?
Thanks!
Relevant answer
Answer
Hey,
thank you very much for the information.
To be honest, I think this is quite a tough task. In my opinion it could be useful to also sequence the tumorcells. Then you could probably use a set of genes or the karyotype (normal to aberrant) to determine the percentage of "contamination" and use this information for your further analysis. Sadly there should/might be a adaption in expression after the coincubation and you therefore lack information about the origin of the altering expression you could only calculate that there is to a certain percentage an altered expression in some genes.
Example: You measured (FPKM) 10 for Gene X in DC and 2 in tumor then you would expect to have 6 i n 50/50 combination or better : you calculated a 50/50 combination using your set of (housekeeping) genes. If you now would expect 6 for your gene but you have 25 you know (pretty sure) that this gene has an altered expression in your "contamination" setup.
Hope I got your approach and what you would like to know right and this helps you a bit. Feel free to ask any further question.
Regards,
André
  • asked a question related to Transcriptomics
Question
4 answers
Omics like genomics, proteomics, transcriptomics, metabolomics and so on
Relevant answer
Answer
Hi Martiniano,
Thanks so much for your help!
Balamane
  • asked a question related to Transcriptomics
Question
1 answer
Hi,
I am looking for a public dataset in "Single-cell microscopy - tissue diagnosis" like the data which is available in (https://www.nature.com/articles/nmeth.4391). In the context of Single-cell imaging, Spatial transcriptomic. Indeed, images of tissue in the resolution of single-cell.
thanks
Relevant answer
  • asked a question related to Transcriptomics
Question
3 answers
i have RNA transcriptomic data and i would like to select a pathway for further analysis, but in transcriptomic ,have many pathways, so on which basis, and how to select a specific path way?
Relevant answer
Answer
You can do a quick ontology enrichment with gene lists using the web tools for reactome, panther, GO or other public ontologies (just search for any of those, upload your gene lists and run the analysis). An over-representation analysis will tell you how many and what elements in your gene list overlap with the defined elements in an ontology pathway as well as a p-value (or FDR) of the significance of that overlap. An established best practice for picking gene lists for over-representation analysis is to pick those that are both statistically significantly differentially expressed (typically with an FDR<0.05) and show a change that is at least some minimum magnitude (e.g. 1.5 fold, or 2 fold different, up or down, from controls). You can then focus on the most significantly enriched pathways. Depending on how much differential gene expression your experiment indicates, you may need to alter that to "cast a big enough net" of genes to get ontology enrichment. So you might take the 500 most up-regulated and 500 most down-regulated by rank order fold change. There are also alternative methods to see what gene changes are functionally interesting - the Broad Instutes GSEA tools and MSigDB gene sets may be useful (depends on what you're studying or are looking for). Or the enrichR tool and databases at the Ma'ayan Laboratory at the Icahn School of Medicine at Mount Sinai which is very useful if you think your transcriptomic response involves some transcription factor mediated response.
  • asked a question related to Transcriptomics
Question
6 answers
I've got microarray transcriptomic data (from S. cerevisiae) - and I've made GO enrichment analyses - since there were many redundant GO terms I've used REVIGO for getting rid of them but I found this tool not accurate enough (I mean that I still have some doubts whether I get rid of right terms). Is there any other tool (for total bioinformatic dummy) that makes similar work as REVIGO - so I could compare if obtained results are similar or not?
Relevant answer
Answer
Ali Javadmanesh I've actually installed it yesterday but since you need a licence from the ClueGO authors I'm waiting for their answer :)
  • asked a question related to Transcriptomics
Question
6 answers
I optate to study bacterial-Synechococcus transcriptome and exoproteome in a co-culture system. I can either seperate the two organisms by using appropriate size filters and sequence them separately. In this case, the problems are - all bacteria cannot be separated from Synechococcus. Secondly, the transcriptome/exoproteome data may change because of the seperation. Thirdly, the sequencing cost doubles up. So, is it possible to sequence the samples without separating the two entities and then utilizing some software/pipelines, separate out the annoted transcriptome and proteome ?
Relevant answer
Answer
Newton Verbisck Thanks for informative articles. The whole point of my experiment is to find out the functional proteoms exported during co-culture and by whom. I understand it sounds easy in the question but is really difficult to separate them out in co-culture system
  • asked a question related to Transcriptomics
Question
3 answers
I have transcriptomic and proteomics data from prokaryotes. What is the best way to integrate this data? Is there any bioinformatic tool for this data integration?
Relevant answer
Answer
Galaxy-P (http://galaxyp.org/) might be useful.
  • asked a question related to Transcriptomics
Question
6 answers
Dear all,
The fecal samples contain plenty of complex biologically active substance, is there any efficient protocol that increase the percentage of microbial RNA in the extraction of RNA from feces? Additionally, rRNA deleting is also the feature of microbial RNA isolation.
Could anyone please give me some suggestions or reference? Thank you in advance.
Kind regards,
Yang
Relevant answer
Answer
MagMax microbiome kit efficiently recovers both DNA + RNA from fecal and many other sample types
  • asked a question related to Transcriptomics
Question
4 answers
I am preparing for doing a comparative transcriptome analysis and would like to know which are the skills I should try and work on before getting the data. Which kind of coding program would be more useful to learn? Any articles with pipelines you can recommend? Other programs widely used? Anything other than computing abilities that may come handy?
Relevant answer
Answer
First, I recommend to figure out what you want to analyze. For example, transcriptome data can be used for improving gene predictions, for checking which transcripts are expressed to what extent, for comparing transcription in different samples of the same species, or even between different species.
Once you have figured that out, look for a recent review in that particular field, read what software is currently recommended. Also look at recent publications and preprints, check which software did those authors use (might be more recent that something in a review).
Then download and install that software, hopefully it comes with a toy data set to play with. If not, download test data from NCBI genomes & SRA (you can shrink both data sets, e.g. by restricting your analysis to part of a chromsome or the transcripts there, and to reads that map to that part of chromsome instead of the full library).
I believe bash/unix skills are extremely useful in addition to learning how to use some specific software.
Software that I personally found useful:
RNA-Seq quantification for comparing different samples from the same species: kallisto & R
RNA-Seq to genome mapping: hisat2
RNA-Seq aided genome annotation: BRAKER/AUGUSTUS/GeneMark-ET
  • asked a question related to Transcriptomics
Question
4 answers
Hello,
I've been looking around but can't seem to find a solution to a problem. I want to understand how does E. coli respond to CO2 in their environment (0-10% CO2).
I know they can change their metabolism to fumarate respiration but I'm looking for a paper or book chapter which will tell me:
1. What is happening metabolically
2. What is happening with gene expression
or any other change
Something like transcriptomic / metabolomic analysis of E. coli grown in different CO2 concentration would be perfect.
Anybody knows a paper like this?
Relevant answer
  • asked a question related to Transcriptomics
Question
19 answers
we want to carry out transcriptomics study of a mixed marine bacterial community. The experiment is designed as such that we have to preserve the samples for later transcriptomic studies (preservation for 3-6 months. we have -20 and -800C facility)
Is there any significant difference between flash freezing and using RNAlater/ or other reagents? Can anyone suggest / provide tips for better RNA preservation technique?
Relevant answer
Answer
Do not use RNAlater
RNAlater is a solution of concentrated Ammonium Sulphate salts with some preparations also containing Cesium Sulphates. It works by denaturing RNAase proteins. It is not a tissue fixative.Plant and animal samples placed in RNAlater solution immediately undergo two major reactions which compromises the transcriptome you are attempting to isolate
1. There is an osmotic cell response to the high external salt concentration with water moving out and cell desiccation. This gives a response of new transcripts produced related to salt stress ( it takes 10-20 minutes for a eukaryotic cell to respond to a stimulus and start transcription). These new transcripts pollute your targeted, desired transcripts.
2. The tissue in RNAlater immediately starts dying (necrosis not apoptosis) even if stored cold. With the start of cell death there is a molecular response (as in 1.) including new transcripts (in attempts to repair, destroy insults etc.) and new RNAases. Again a new transcriptome is produced.
Is RNAlater useful for any experiment-probably not.
  • asked a question related to Transcriptomics
Question
4 answers
The Qiagen RNeasy plant kit failed. which other kits could you recommend? We were hoping to avoid Trizol.
Relevant answer
Answer
What about Sigma Plant RNA extraction kit?. As you might be well aware that fine powder of your sample may increase your chance for getting better quality.
  • asked a question related to Transcriptomics
Question
13 answers
I need a suggession for a good RNA isolation protocol from Serum/Plasma. Looking for any kit name of any modification of protocol etc
Relevant answer
Answer
Hi Andrea,
RNA found in both plasma and serum is normally fragmented RNA or small RNA , mainly miRNA, and is either bound to proteins or contained inside extracellular vesicles. If your target is to purify mRNA from plasma/serum then you have to be aware that this mRNA may not be the full length, original RNA, that you may be getting when isolating RNA from whole blood.
I personally recommend for you using one of Norgen's Plasma/Serum RNA Purification kits ( https://norgenbiotek.com/sites/default/files/resources/Plasma-Serum-RNA-Purification-Kit-PI55000-5.pdf). These kits covers a sample volume range from 50ul up to 5mL without using any phenol or carrier RNA. Unlike other purification kits, Norgen's kits uses Silicon carbide as its separating matrix instead of silica. Some literature has mentioned that silica-based technology with/without phenol has a bias towards binding large RNA sizes as well as a bias towards binding RNA with sequences containing high GC contents whereas Norgen's Silicon carbide doesn't have such a bias.
For the isolation of RNA from blood, this should be a straight forward isolation but you have to use a different isolation method for this. I also recommend using Norgen's Total RNA Purificiation kit (Cat. 17200) for this.
When isolating RNA from plasma/serum you should be expecting low RNA amounts (normally in the picogram range) therefore you can't quantify you plasma/serum RNA using conventional methods such as regular spectrometer or Nanodrop since they are not sensitive enough to quantify such low RNA amounts. I recommend using Agilent Bioanalyzer RNA Pico Chip for quantifying plasma/serum RNA. Also don't relay on the RIN values from the bioanalyzer chips cause it will be low and won;t reflect the quality of the purified RNA. RIN values are calculated based on the ratio of the 28S rRNA and the 18S rRNA and since plasma/serum doesn't have cells therefore the purified RNA won't have these two bands which will lead to a very low RIN value. The best way to evaluate the quality of the purified RNA is to amplify a highly abundant small RNA target such as the 5s rRNA or to amplify a highly abundant miRNA such as miR-21. I believe that what Norgen does when performing small RNA sequencing services. https://norgenbiotek.com/services/small-rna-and-microrna-next-gen-sequencing
Sorry for the long message but I just want to give you some detailed information so you don't face any issues through out you project.
I hope this answered all of your concerns.
Best Regards and Good luck.
  • asked a question related to Transcriptomics
Question
6 answers
I want to do transcriptomics of Cymbopogon citratus, at which point I must take the sample for RNA extraction. Can someone recommend articles?
Relevant answer
Answer
Thank, Xinkun Wang It`s good data!
  • asked a question related to Transcriptomics
Question
3 answers
We have performed Clariom-S microarray-based transcriptome and TMT-MS based proteomics analysis on healthy and patient samples.
Our idea was to study the correlation between gene- and protein expression between healthy and patient samples, for which we have calculated an overall mean Spearman coefficient for all the genes- vs proteins-expression correlation. More specifically, I have identified 8000 genes common in both gene and protein study, and calculated mean correlation coefficient. I obtained a Spearman coefficient value of 0.58 for this dataset. 
Further, what we are interested in doing is: calculating pair-wise Correlation between mRNA and protein expression of all 8000 individual gene-protein pairs so as to create figures (as shown in Fig. 2a in Paper -2 and Fig. 3a in Paper1 attached here).
I was wondering if you could provide any insights into how to perform such an analysis. To my knowledge, most researchers use program ‘R’ to find such gene-wise correlation. Unfortunately, I have no experience with using this program. Is there any software/program that I can use for this.
I would much appreciate any help/advice.
Thank you!!!