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
Dear ResearchGate community,
I'm currently engaged in research involving the prediction of immune responses using transcriptome data. As part of this, I'm exploring the utility of random forests and decision trees as predictive models.
In case of transcriptomics, what performance metrics have you found most informative when comparing the predictive accuracy of random forests and decision trees? Given the complexity of gene expression data, are there metrics that particularly resonate with understanding immune response prediction? Do you have any tips for optimizing model parameters to prevent overfitting and enhance generalization?
I'm excited to hear about your experiences working at the intersection of transcriptomics, immune responses, and machine learning.
Thank you in advance for your contributions, and I'm looking forward to engaging in enlightening discussions.
Hi - I'm currently working with two RNA-Seq studies; one has RNA extracted from whole blood, the other PBMCs. Eventually we want to combine these data and perform some cell-specific deconvolution to look at DEGs.
Are there any recommended methods for batch correcting these data from different sources?
I want to purchase Macbook mainly for the bioinformatics analysis propose i.e., Transcriptomics, smalRNA, Methylation, lncRNA and other. Would anyone please suggest to me the best affordable one?
I want to know that, when a heterologous gene is expressed under CMV promoter in mammalian cells, what is the percentage of this heterologous gene's mRNA in comparison to total cellular RNA and total cellular mRNA? Is there any mention of this in the literature?
I have tried to separate a direct coculture of MSCs (mesenchymal stromal cells) and macrophages to do bulk RNA seq on macrophages, as I want to find out how MSCs change the genetic expression on macrophages. I have tried different methods to separate the coculture as much possible, but I can only manage to retrieve a cell population with 95% macrophages, and 5% MSCs still present.
Therefore, I want to know if anyone has experience with analyzing data when the population is not completely pure with one cell type and how do I handle such data?
Is it wise to proceed with bulk RNA seq when 5% of my cells are still MSCs, well aware that the expressed genes observed could come from the 5% MSCs?
I have two DEG sets for 2 disease conditions (from mild to severe condition) of the same viral infection. When I look at the common gene from these two sets of DEGs, I found that some genes show opposite expression among these two conditions ( Like a gene downregulated in mild but up-regulated in severe or vice versa). So what I want to know is that,
1) If this phenomenon is normal in viral infection??
Do you know if is that possible to identify bacterial enzymes (from microbiota) by analyzing transcriptomics data from a human tissue (i.e. TCGA samples)?
Thanks for you response!
I have full access to several metabolomic (metabolite concentrations) and transcriptomic databases (FC and pvalues). I would like to integrate all these info in one to obtain not only DEGs and metabolite boxplots but pathways and tissue/cell type information. I'm stuck searching for free software or friendly R packages other than mixOmics. Any idea?
I work in the cancer research field and human disorders by using the bioinformatics approach. These projects contain the analysis of transcriptomic data such as microarray, RNA-seq analysis, TCGA, systems biology analysis, survival analysis and etc. also, the metagenomic analysis in microbiome fired are conducted. Those interested in participating in analyses and writing articles are invited to send their CV to the email below.
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.
I have performed small RNA sequencing on zebrafish tissue from wild-type and mutant lines. I have deduced a list of differentially expressed miRNAs. I have used DIANA-microT-CDS to predict targets for these miRNAs and have filtered the list of targets to remove genes not expressed in the tissue of interest.
I would like to perform GO term enrichment analysis using GOrilla on the resulting lists of targets. I have two approaches in mind.
1) Use targets with a high predicted repression against a background list of genes expressed in the tissue of interest.
2) Use a single list of targets ranked from very high predicted repression strength to low predicted repression strength.
Could anybody advise if these methods seem suitable for the analysis I would like to perform?
Any advice on alternative methods of softwares would also be greatly appreciated.
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.
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 ?
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?
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.
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.
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.
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?
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
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.
- Which combinations of data and method should I use?
- Would this workflow weaken the correlation of the genes since some may have correlations only in specific condition?
- Whatever you think of my work?
Looking forward to your reply!
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!
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.
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..
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!
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.?
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?
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?
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?
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??
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?
I am looking for ideal configuration details for a workstation to perform a metagenomic, transcriptomic and whole genomic analysis.
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?
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.
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?
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!
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!
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
gene X11_E1 X11A10 X11A12 X11A3 X11A5 ........
Gnai3 23 4 22 25 94 ..........
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?
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)
- 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,
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?
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!
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!
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!
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?
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?
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.
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???
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
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!
We are planning to do RNA-Seq for RNA extracted from two types of samples:
- Routine snap-frozen mouse fetal tissues
- 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?
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.
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!
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.
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?
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?
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
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?
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?
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?
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?
Does anybody know of a paper or a database, where I can get such data?
I would appreciate any information.
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?
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.
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?
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?
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 ?