RNA-Seq - Science method

According to me,

To find transcript isoforms of a gene, you can use:

Microarrays are not suitable for discovering novel isoforms but can be used to detect known ones. RNA-Seq is the preferred method.

Airton Pereira e Silva Doing a test-run with samples that aren't worth much and minimizing risk wherever you can is always a good idea. In one of my experiments 12 mouse livers were worth £30k - so I put each of my samples in two separate RNAlater containers stored in two separate freezers... I extracted and analysed each sample twice independently...

Re good RNA integrity - many researchers blame the kit/storage when most likely it is their lab technique that leads to RNA degradation. Again, test-runs will help there...

Immediate submersion in RNAlater is important - I've seen people extract organs from all their mice, carry them to their lab and then put them all into RNAlater there, and also have done tests on how long it takes for degradation to set in if you delay submersion... 5 min was ok (= RIN>9), >30 min was borderline (= RIN~8), >1h was unusable. So I took prepared containers with RNAlater into the mouse unit and extracted each liver, cut them into pieces and immediately added them to the containers. At this stage there's no need to be RNase free as the RNases within the cells are much more likely to be the issue.

The time issue could partly explain how some people get RIN of >9 all the time and others don't... but apparently you also have to really avoid cutting the mouse gall bladder!

I also discussed the time issue with one of the human tissue bank coordinators - apparently they had consistently bad RNA... turns out a theatre nurse would just put the tissue sample or extracted organ from transplant patients on ice and then give it to the tissue bank collector AFTER the surgery finished to not to break sterility of the operating theatre and herself... we solved it by making the collector scrub in to wait for the sample.

Random mouse hearts shouldn't be hard to come by - just ask the Animal technichian/other researchers if they have any that will be culled or are superfluous to breeding.

Hello Seif,

I have been working on gene expression profiling for many years now. We have detected several biomarkers with our methods and, up to now, 4348 citations of our 231 Research articles. Try https://www.researchgate.net/profile/Joachim-Gruen/research.

In case the reference genome used for reads alignment does not contain the fused gene sequence,You may generate the reference genome using the expected fusion product and try aligning the reads to them.

I am also facing the same problem with TRIzol lysis. I am getting very low RIN. I used Qiagen RNeasy kit also but no affect in RIN. Is TRIzol reagent too harsh for the cells?

Depending on the type of your experiment, read duplication is to be expected and deduplication is not necessarily beneficial. You might want to have a look at this article: https://www.nature.com/articles/srep25533

There are two common ways to eliminate most ncRNAs.

1) Ribodepletion (depletion of rRNAs) is used to eliminate rRNAs, which constitute 80-85% of total RNAs. Although this approach eliminates the majority of rRNAs, the samples are still contaminated with tRNAs and other ncRNAs.

2) Enrichment for poly(A)+ transcripts is another approach which eliminates all poly(A)- transcripts from total RNA samples. This approach is highly desirable to study poly(A)+ transcripts, particularly mRNAs. However, one must keep in mind that some poly(A)+ kits do not eliminate all poly(A)- transcripts. Thus make sure to check the efficiency of elimination. If one is interested in studying lncRNAs, it is important to keep in mind that some lncRNAs are polyadenylated where some others are not. In this case, ribodepletion would be more appropriate.

Hello Olatunde Omotoso,

yes, it could be possible to find such huge numbers of significant differentially expressed genes (DEGs). It depends on which two groups you are comparing. Nevertheless this is not very valid, if using only p-values or even corrected p-values. In chip data analyses we used a multi-parameter method (High-Performance Chip Data Analysis, HPCDA), that includes also p-values but a lot of other relevant data, as could be seen in my profile and the added links. You should analyze your RNA-Seq data in a similar way and then upload the resulting DEGs into GO. Surprisingly a fold change (FC) was not advantageous to get the list of significant DEGs with HPCDA. Using a FC of 4 (log2 FC of 2) could loose a lot of relevant genes, but this depends on the number of individual datasets in both groups.

RT-qPCR (Reverse Transcription Quantitative Polymerase Chain Reaction) can be used to analyze the gut microbiome, but there are specific considerations and steps involved in applying this technique to microbiome studies. Here’s how RT-qPCR can be utilized for gut microbiome analysis:

**1. Overview of RT-qPCR for Microbiome Analysis

**1.1 Purpose:

Quantitative Measurement: RT-qPCR can be used to quantify specific microbial populations or functional genes in the gut microbiome.

Gene Expression: It allows for the measurement of gene expression levels in microorganisms, providing insights into their activity and metabolic functions.

**2. Steps for RT-qPCR in Gut Microbiome Studies

**2.1 Sample Preparation:

Isolation of RNA: Extract RNA from gut microbiome samples (fecal samples, biopsies, etc.). This step is critical as the quality and purity of RNA can affect downstream results.

RNA Quality Control: Ensure that the RNA is of high quality and free of contaminants. RNA integrity can be assessed using a bioanalyzer or similar equipment.

**2.2 Reverse Transcription:

Convert RNA to cDNA: Use reverse transcriptase to convert the extracted RNA into complementary DNA (cDNA). This step is necessary because RT-qPCR amplifies DNA, not RNA.

**2.3 Design and Validation of Primers:

Target-Specific Primers: Design primers specific to the target genes or microbial taxa of interest. For gut microbiome studies, these targets might include genes involved in microbial functions or markers for specific microbial groups.

Primer Validation: Validate the specificity and efficiency of the primers using standard curves and melt curve analysis.

**2.4 RT-qPCR Setup:

Prepare Reaction Mix: Set up the RT-qPCR reactions using the cDNA samples, primers, and appropriate qPCR master mix.

Thermal Cycling Conditions: Optimize thermal cycling conditions (denaturation, annealing, and extension) according to the primers and master mix used.

**2.5 Data Analysis:

Quantitative Analysis: Analyze the qPCR data to quantify the expression levels of target genes or the abundance of specific microbial taxa.

Normalization: Normalize the data against housekeeping genes or other control genes to account for variations in sample input and efficiency.

**3. Applications of RT-qPCR in Gut Microbiome Studies

**3.1 Microbial Taxa Quantification:

Species Identification: Quantify specific bacterial species or genera in the gut microbiome by targeting unique genetic markers.

Abundance Measurement: Measure the relative abundance of specific microbial populations in different samples or conditions.

**3.2 Functional Gene Analysis:

Gene Expression: Assess the expression of functional genes related to metabolic processes, pathogenicity, or antibiotic resistance.

**3.3 Comparative Studies:

Disease Association: Compare the abundance or expression of microbial genes between healthy and diseased states to identify potential biomarkers or therapeutic targets.

**4. Considerations and Limitations

**4.1 RNA Stability:

Handling: RNA is less stable than DNA, so careful handling and storage are required to avoid degradation.

**4.2 Primer Specificity:

Cross-Reactivity: Ensure primers are specific to the target organisms or genes to avoid cross-reactivity and inaccurate quantification.

**4.3 Sensitivity:

Detection Limits: RT-qPCR may not detect low-abundance microbial populations effectively. Combining RT-qPCR with other techniques (e.g., metagenomics) can provide a more comprehensive view.

**4.4 Normalization:

Reference Genes: Use appropriate reference genes or internal controls to normalize the data and account for variations in sample processing and cDNA synthesis.

Summary

RT-qPCR is a powerful tool for analyzing the gut microbiome, providing insights into microbial composition and gene expression. By carefully preparing samples, designing specific primers, and analyzing the data, you can obtain quantitative information about microbial populations and their functional roles. However, due to the complexity and diversity of the gut microbiome, it’s often useful to combine RT-qPCR with other methods like metagenomics for a more comprehensive understanding.

l Perhaps this protocol list can give us more information to help solve the problem.

So basically this is not a question which require any answer.

This is unfortunately the pain-point of bioinformatics journey which I've been through. I can recommend this book - https://www.biostarhandbook.com/ & have a look at the companion course https://www.biostarhandbook.com/edu/course/6/.

For variant calling (germline/hereditary) look into the GATK workflow https://gatk.broadinstitute.org/hc/en-us/articles/360036194592-Getting-started-with-GATK4

For RNAseq, I suggest you take a look at Salmon and Deseq-2 https://combine-lab.github.io/salmon/ & https://bioconductor.org/packages/release/bioc/html/DESeq2.html

For the Salmon and Deseq-2 combination this tutorial will help but you need to know some Linux & Rstudio https://www.hadriengourle.com/tutorials/rna/#import-read-counts-using-tximport

Most freeware stuff is optimized or available on Linux. Ubuntu is not a bad start. If you need a virtual machine, have a look into Docker or https://www.virtualbox.org/

You should look into develop basic skills in Linux, Rstudio and Python to run the tools. Nowadays, you don't really need java or C-level languages to run the tools. Developing algorithms are a different story though. Keep your determination, I knew nothing in 2017 when I started as well :)

I’ve had similar issues recently isolating small quantities of RNA (ribosome protected fragments). I use 300 mM sodium acetate pH 5.2 and 5 mM magnesium chloride followed by ethanol precipitation (2.5 volumes) overnight at -20 C with addition of 1.5 uL GlycoBlue. Spin at 20,000 x g for 1 h at 4 C in Eppendorf low bind tubes. I see good pellets in some samples but not for others. I do invert tubes sufficiently after addition of ethanol. I wonder if vortexing and centrifuging again might help. In some samples in looks like several particles precipitated on the side of the tube and not the bottom. Perhaps such multi-localized pelleting is the issue. Otherwise my best guess is RNase contamination. Although I am very careful with using fresh gloves and RNase Away on pipettes and gloves.

The reason you re getting less yield with gauge needles might be due to incomplete/partial mechanical breakdown. I see you said that you used 8 (perhaps you meant 18?), perhaps the 8 gauge isn't breaking down enough of the tissue clumps so that the 21 and 25 gauge can finish off at a finer level. I would stress that the first gauge is crucial for the success of the finer needles. 21 and 25 are quiet tough, I know 21 gauge was giving me real difficulty/strain on my fingers.

Yes you could combine the RNA from different columns together.

The Qiagen kit also says you should get 10-20ug of RNA from lungs, assuming 30mg. However, the binding capacity for the column is 100ug. So in theory, you could use 150-300mg tissue and still be under the max binding capacity.

Depending on how the samples were frozen years ago, flash freeze in liquid nitrogen then store at -80 vs just putting them in the -80 to freeze, could definitely contribute to degradation.

Thank you!

For the alignment step, I think it's important to mention pseudo alignment (Salmon, Sailfish, Kallisto) and RUM for hybrid alignment to both genome and transcriptome.

Why you want to align RNA data against WES? This does not make any sense. For WES, normally, the data is aligned against the whole genome as both are same.

Even if you do, it will take quite a long time, plus you need to take care of splice event as well that will complicate the already complex step.

Hi Mesele

you need to go to the SRA site (https://submit.ncbi.nlm.nih.gov/subs/), need to log in or create an account, and first create a bio project. once you'll get the bio project, you'll need to create the samples, and lastly load the data by the SRA download kit. take care of having words to explain the project and fill the forms, and to be exact on the samples names, it's very case sensitive.

all the best

fred

Use The Galaxy platform

There will likely be significant batch effects. I would analyze each set separately to get a higher power (which will be the case when the variability between the sets is large and won't be compensated by the reduction of standard errors due to the larger sample size).

You might consider pooling the p-values according to Fisher's method, if you need a single p-value per gene.

Hello Tiantian Fu,

DESeq2 can generate such files, usually the best one is VST normalized table, where you plot each gene alone with very little variations among samples.

Regarding the top 10, they are the 10 genes with the lowest FDR values from the DESeq2 results table, then you can go back to the VST normalized table and calculate the Z scores of the top 10 genes. After that you can use GraphPad prism to plot a heatmap.

Hope this helps

Thank you Govindkumar Balagannavar, for clearing my query. It was really helpful.

Ideally yes the RNAseq and PCR results are in agreement. However, there is often some variation. As you say, you are seeing the same trend of increased expression for your gene of interest. It is possible that the read coverage for your gene is low, or if there are other similar genes, reads may be mapped to homologs. Are you using paired tests of the same sample? There could also be sample to sample variability.

Hi Mito

you'll need some expertise in bioinformatics to get this road.

starting from fastq files, you'll need to extract data, normalize them, align them on a genome and count the transcripts...once it's done, you'll have a count matrix you'll be able to use for your BW construction.

all the best

fred

Analyzing differential gene expression in single-cell RNA sequencing (scRNA-seq) data involves several steps. Below is a generalized workflow for obtaining differentially expressed genes (DEGs) from a scRNA-seq dataset contrasting two groups:

Here's a simplified example using the Seurat package in R:

RCopy code# Install and load necessary libraries install.packages("Seurat") library(Seurat) # Load and preprocess the scRNA-seq data seurat_object <- Read10X(data.dir = "path/to/data") seurat_object <- CreateSeuratObject(counts = seurat_object) # Perform normalization, identification of highly variable genes, and dimensionality reduction seurat_object <- NormalizeData(seurat_object) seurat_object <- FindVariableFeatures(seurat_object) seurat_object <- ScaleData(seurat_object) seurat_object <- RunPCA(seurat_object) # Cluster the cells seurat_object <- FindNeighbors(seurat_object) seurat_object <- FindClusters(seurat_object) # Perform cluster differential expression analysis de_results <- FindMarkers(seurat_object, ident.1 = 1, ident.2 = 2) # Visualize the results DoHeatmap(seurat_object, features = de_results$gene, group.by = "orig.ident")

Note: This is a simplified example, and the actual analysis may vary based on the characteristics of your dataset and the tools you choose. Additionally, always consult the documentation of the specific tools you are using for detailed guidance.

I just wanna update that I have the answer to this question. Anyone interested in this question can discuss it further.

It is preferable to avoid downsampling (randomly reducing the number of reads in some samples).

Normalization techniques like DESeq2, edgeR, and limma are preferred for handling differences in sequencing depth across samples, preserving the maximum amount of information. Downsampling, on the other hand, can be used to equalize sequencing depth across all samples but may reduce statistical power, especially in samples with lower read depths. Downsampling may be considered if computational capacity is limited, data size is significant, or extreme cases have vast differences in read depths. In most cases, normalization methods are preferable.

Generally you want to treat all your samples the same way, as minor things can introduce bias (different person handling the samples, different batch of reagents, different flow cell etc). If you have any internal controls, use those for QC, if not, a housekeeping gene to check for possible biases on those 10 samples.

Thank you Mansoor Hayat for your explanation now i understand better

If the genome is incomplete or poorly annotated, a transcriptome-based analysis using a custom-assembled transcriptome is preferred. Hybrid approaches that combine information from both genome and transcriptome data may be considered.

In single-cell RNA sequencing (scRNA-seq) data analysis, the preprocessing steps, including denoising and normalization, are essential to ensure that the data is suitable for downstream differential expression analysis. While it may seem intuitive to directly use denoised raw data without normalization for differential analysis, this approach is generally not recommended for several reasons:

In your example with C. elegans muscle cells at different ages, it's important to normalize the data to account for differences in library size between cells. This ensures that any observed changes in gene expression are more likely to reflect true biological differences rather than technical artifacts.

That said, you should still perform differential expression analysis on the normalized data. If a gene like Cox-4 is significantly downregulated with age, the normalization should help highlight this effect by reducing the influence of technical factors.

In summary, while it may seem reasonable to directly use denoised raw data for differential analysis, it's generally recommended to perform proper normalization to account for technical variations and ensure the robustness and biological relevance of your results in scRNA-seq data analysis

General answer: Everything will affect RNA-seq results. More specific answer: compare levels of housekeeping genes between the two methodologies and see if they show any discordance. If not, you are likely good to go with your analysis

There is a similar question.

samples are the key points in molecular biology and higher technologies. did you check for quality of your samples before going to library preparation. low quality (RIN) will give low target assignment.

you can also check after sequencing quality using multiQC tools but the worst is done.

fred

Here are some of them:

Hi Ahmad

before going to the software, be sure the samples were extracted with the right protocol and kits to allow miRNA to be selected or selected in the whole samples.

fred

@Can

I simply stored the sample in 50 microliter of RNAse free water àt -80.

Dear all,

thanks for the insightful commentaries of my colleagues. However, I beg to differ in some detail as this problem goes a bit deeper. (Just my 0.02$)

1. Throwing three samples into one is, of course, a bad idea because you waste ressources. Why not make three differently barcoded libraries and then send them for sequencing on one lane. You do not loose information and you can always ask for more reads if your sequencing depth is not sufficient. Thus you save in sequencing costs but keep all the options.

2. Do not make technical replicates. If you master the technique they will be +/- identical. If you have technical problems no replicate will help you anyway.

3. If you run biological replicates I wouldn't use the classical R-programs. Most assume that there is a "true" value that you can't measure because of random variation in your method/sample. However that is not exactly what happens in nature. Imagine you derive three transgenic cell lines with an inducible transcription factor to find target genes. Now you compare 3 times TFon/TFoff. You get following values:

TFon TFoff

geneA

sampleA: 1000 100

sampleB: 100 10

sampleC: 10 1

It is clear that geneA is very interesting. However, if you define sampleA,B,C as triplicate most analysis programs will throw this gene out because base-line expression has a higher variation as the overall difference between on/off.

Alas, geneA may be a perfect and important target gene as you do not control the overall concentration of the transcripiton factor in the transgenic cells. So it may be perfectly ok that you see this large variation in base expression. Fold-change is what counts here. In biology a value very frequently depends on more than one factor and not all can be controlled. Classical statistics fails in this cases.

Therefore I'd recommend to run barcoded libraries - evaluate each one individually and look for the intersection of genes that come up as interesting in all three instances. Then follow up on these.

In the end no statistics can replace the good old biological confirmatory experiment anyway (although "wet" biology seems to be out of fashion nowadays).

Good luck with your experiment.

Best

Robert

@ Kangkon Saikia Thanks so much for your answer. Yes, I'm using protein sequences as well. It will be really helpful if you can explain this part in your answer a bit more. "For this you have to perform gene prediction first followed by annotation of the CDS/transcripts."

Thanks again!

you can reach out to vikram@aiims.edu , they have bsl3 facilty.

Oxford Nanopore announced that they are training their base caller to recognise m1Ψ in direct RNA sequencing data at their London Calling 2023 conference. I am unsure of its current availability. You may want to ask on the Oxford Nanopore sequencing Community page for the latest on this topic.

It is better to consider batch as a factor in the design formula. The tximport pipeline proposed by Michael Love himself offers the most useful solution. Please have a look.

Chat-GPT really is a jabber-box. Context must be critically reworked by a person knowing the topic. Answers like the one posted by Rana above are not only not useful but also potentially misleading, not to say wrong. But most relevantly, it has nothing to do with a scientific interaction of researchers.

Hi Jan,

You can try PROGENY scores directly for pathways. You can get a pathway activity scores here : https://bioconductor.org/packages/release/bioc/html/progeny.html

You can also consider Gene Set Variance Analysis. https://bioconductor.org/packages/devel/bioc/vignettes/GSVA/inst/doc/GSVA.html This is can be used as a one sample GSEA and you can analyse how the samples are different in your favourite pathways (gene sets) instead of genes. You can find biological outliers or the response to each one compound.

A simple sodium acetate precipitation of the RNA will clean up your A260/230 ratios.  Here is the method I use:

- Add ultra pure water make up RNA samples to 100ul in total

- Add 300ul 100% EtOH & 10ul 3M Sodium Acetate per sample

- Vortex + incubate overnight @ -20oC

- Centrifuge full speed @ 4oC for 30mins

- Pipette off supernatant.

- Add 500ul 70% EtOH + mix

- Centrifuge full speed @ 4oC for 15-20mins

- Pipette off supernatant & air dry for at least 15 mins [check that no EtOH remaining]

- Resuspend in 30ul or 12ul (depending on RNA concentration)

- Enjoy good A260/230 values!

Give it a go, it works really well.  Just remember the RNA pellets will be difficult to see (pellet paint can help).

qPCR is considered the more sensitive and direct analysis. You use the RNA-seq to get a list of "interesting candidate genes" to then investigate using qPCR. You don't need to do any direct comparison to the original RNAseq data.

You can't know that the high TPM from RNA seq will also have high expression during qPCR. That's why you do the experiment.

Yes with the kit, Agilent, and or Qiagen,

No, kit is ok

Normalizing RNA-seq data generated from PolyA and Total RNA can be challenging, as they have different properties that can affect their library preparation and sequencing, resulting in differences in the sequencing depth and composition of the resulting reads.

One common method for normalizing RNA-seq data is to use the reads per kilobase per million mapped reads (RPKM) or transcripts per million (TPM) approach. These methods normalize for sequencing depth and gene length and provide a measure of the expression level of each gene relative to other genes within the same sample. However, RPKM and TPM normalization may not be suitable for comparing expression levels across samples with different RNA types, as they assume that the total transcriptome is being sequenced uniformly, which may not be true in the case of Total RNA.

Another method that can be used for normalizing RNA-seq data is to use the trimmed mean of M values (TMM) normalization approach, which is based on the assumption that most genes are not differentially expressed across the samples being compared. TMM normalization estimates a scaling factor for each sample based on the relative expression of a set of housekeeping genes, which are assumed to be stably expressed across all samples.

A more advanced method for normalizing RNA-seq data is to use a normalization technique that takes into account the differences between the RNA types, such as the "DESeq2" package in R, which uses a generalized linear model to estimate size factors that adjust for the differences in sequencing depth and composition between PolyA and Total RNA samples.

In summary, while RPKM or TPM normalization can be used for normalizing RNA-seq data generated from PolyA and Total RNA, it may not be suitable for comparing expression levels across different RNA types. TMM normalization can be used as a general normalization method, while advanced techniques such as DESeq2 can be used to specifically address the normalization of RNA-seq data generated from different RNA types.

Thank you, Wilhelm,

That still did not address the main question, though. Please check again.

After 2nd strand synthesis step, there is an SPIA amplification step, SPIA primer can bind to only the second strand and amplify only that strand. The first strand does not have the SPIA primer binding site, so it does not get amplified.

This may lead to a high quantity of single-stranded cDNA (due to SPIA amplification) and very low double-stranded cDNA if at all there is any.

So the question is, How did the manufacturer claim that the product will be double-stranded cDNA? whereas whatever is shown in the diagram, can generate only one strand.

as Péter Gyarmati said the extraction method might introduce an important bias in the quantification results. How did you define "differentially expressed genes"? 25% of down expression will not pass the fold-change threshold in RNA-Seq analysis. In future experiments, it could be a good idea to add a spike in RNA-Seq samples to compare the results with RT-qPCR

Honestly, DESeq2 takes almost no time to run: it's by far the easiest part of the entire pipeline.

So...do it all. All comparisons.

Male treated vs male untreated -> effects of treatment on males specifically

Female treated vs female untreated -> effects of treatment on females specifically

Male and female treated vs male and female untreated -> sex-agnostic effects of treatment

Male untreated vs female untreated -> sex-specific differences under normal conditions

Male treated vs female treated -> sex-specific differences under treated conditions

male (all) vs female (all) -> treatment-agnostic effect of sex

What you'd hope is that the same genes would crop up under the same sort of comparisons (i.e. the best hits for male treated vs male untreated would be the same as those for female treated vs female untreated), and any genes that bucked the trend by being highly altered in males but not females (or vice versa) would also pop out of the other datasets, allowing you to determine whether the differences reflect sex alone, or are a consequence of sex + treatment.

RNAseq data always gives you far, far more candidates than you can realistically handle, so you can be quite aggressive in your culling of "interesting but not that interesting" candidates, but making multiple comparisons in this manner allows you to interpret your results with more nuance. And it's really quick and easy to do.

Mitra Riasi

Many R packages are available for DEGs analysis. You may go through the following sources.

1. ccb.jhu.edu/software/stringtie/index.shtml?t=manual#deseq

2. rnabio.org/course/#module-03-expression

3. www.nature.com/articles/nprot.2012.016

4. www.nature.com/articles/nprot.2016.095

Welcome to RNA-seq! It's a crazy and wild world. You will find that responses will depend a great deal on what you're aiming to achieve. So take my responses with this in mind...it depends!

Get as close to 100% as possible otherwise you'll be having to perform a set of validation experiments to ensure that any interesting findings are due to changes in your cell type of interest and not in a "contaminating" cell type. Single cell RNA-seq may be suitable here since you'll be able to get some cell type resolution and identify the populations in which the change is occurring, of course you'll still need to validate. The strenght of scRNA-seq is that you don't need to purify/enrich your population since these get resolved as part of the procedure/analysis. However, the a drawback with scRNA-seq is that you will loose a lot of low abundant transcripts, "dropout" is also a major issue. So, if you're comfortable loosing some info on potentially valuable transcripts then scRNA-seq may be the way to go. They do potentially complement each other especially because with bulk, you may get data about low expressed transcripts. But a big caveat, it all depends! You may consider identifying and collaborating with someone with expertise in RNA-seq (sample prep and data analysis) at your local institution.

Which papers? It depends. Start with papers that are answering a similar question to yours, then dig into what would be best for you study. You can consider reaching out to representative of companies like 10X Genomics and Miltenyi Biotec...that's also a good starting point. Good luck!

Just to confirm before sending it for library prep.

I am not an expert in this by any means, but I have read a lot and have seen this type of data and interpreted it before as well. I can give you what I know from my experience and others may chime in.

It is a read out of transcripts that correspond to that particular site. it might be referred to as base resolution expression of the particular sequence. Essentially the higher number of transcripts that coincide with that particular sequence the higher the score. It could be areas that were difficult to resolve due to all kinds of aspects. 1) Sequence has a lot of repeats if that was the case you would see the same resolution in the other two samples but that does not seem to be the case. These areas might be resolved better if you increase the read depth of the study.

2) It may be more suggestive of a difficulty to read them. These results may be affected by post modification of the RNA as well.

This paper describes this is clinical samples but that does not restrict the affect only to humans post-modification

Sci Adv. 2021 Aug; 7(32): eabd2605.

Published online 2021 Aug 4. doi: 10.1126/sciadv.abd2605

PMCID: PMC8336963

PMID: 34348892

Judging by the fact the title on the samples say drought I might think a more epigenetic effect (Post-modification of the RNA maybe due to stress)

I nominate DNA methylation clocks to measure aging. The problem is not that it's expensive, but that they don't work in the context of intervention. People need to double-check on whatever their methylation clock is saying with a good old life span study. But nobody has time for that anymore, and if they did it, they wouldn't like the result. Instead, they just take the clock on faith.

It is generally not recommended to use a viral RNA extraction kit for the isolation of human RNA, as the kit is specifically designed for the isolation of viral RNA and may not efficiently extract human RNA. Additionally, the reagents and protocols used in the kit may not be optimized for the isolation of human RNA and may lead to poor quality or quantity of RNA. It is recommended to use a kit specifically designed for the isolation of human RNA, such as the Quick-RNA™ MicroPrep Kit-Zymo Research.

Yes, it is possible to extract total RNA from PAXgene blood RNA tubes using the PAXgene blood RNA kit. The PAXgene blood RNA kit is specifically designed for extracting RNA from blood samples that have been collected and stored in PAXgene blood collection tubes.

To extract total RNA from a portion of the blood sample collected in a PAXgene blood RNA tube, you will need to follow the manufacturer's instructions for using the PAXgene blood RNA kit. This typically involves the following steps:

It is worth noting that the specific procedures and reagents used in the PAXgene blood RNA kit may vary depending on the specific kit that you are using. Therefore,

How does your institute financially support the applicants?

Well there is no method or procedure which can be universally used to normalize the data as you are looking for.

Normalization is done for different samples/conditions within the data and not across or between multiple datasets. And that is what the paper mentioned above is dealing with. Normalization methodology for a data set.

Further, its not just the data sets are different, they are generated by different platform. Thus, in addition to the batch effect, there can be several other factors which should also be considered before normalization.

The plot shows that the average quality per base alongside your 150 pb reads is very high which is good. This result is kind of normal when the sequencing has been outsourced. Most companies will give you prefiltered fastq files containing only reads with high quality. You can ask your sequencing provider if that was the case, although sometimes you can also find that info in the report they send together with your fastq files.

Wei Zhou

Thanks Abhijeet & Sonja.

After I deeply check the data. Forward has more over represented sequence of Illumina nextra adaptor/Index and Reverse has highly over represented by Poly (G) sequence. Finally we dropped this data and asked the vendor to redo the sequence.

In NGS, read counts have somehow to be mapped to genes. Reads are not always matching to only a unique gene, so it partially depends on the mapping algorithm how many "reads" are attributed to a gene.

Further, genes may be expressed in different variants, and the presence or abscence of an exon may impact the number of reads attributed to that gene.

For genes that are not regulated very strongly, the method used for normalization can impact the eventually observed direction of regulation.

qPCR is sensitive to the assay performance and the stability of the chosen reference genes. If amplification efficiencies are not ideal, results may change depending on the absolute Ct values of the gene. However, these are eventually "human errors" of not properly validating the qPCR assays and reference genes.

These are possible reasons that come into my mind. There may be other reasons.

U can mail me here nkrdas2@gmail.com

depending on cell types, but average amount of RNA is 2-10pg by cell...

all the best

fred

Hello Antony, which protocols have you used? In all of them you get the same result shown in the photo?

GTEX, gnomAD, COSMIC, TCGA, Protein Atlas, ensemble

The large fold-change indicates that the gene is likely not expressed to a high level. Under control conditions the experession can be almost zero, and a slightly larger expression under treatment conditions will result in a very large fold-change.

Low-expressed genes give only low or no counts in RNA seq. It might be that genes with no or very few counts are filtered out from the analysis, because the counts are not reliable. If the gene is not at all detected under control conditions (0 counts in all control samples), it is not possible to calculate any (finite) fold-change at all.

Hi Yeogha,

Firstly, what proportion of your cells do you expect to be epithelial? From my understanding of human lungs, a large proportion of the cell types are mesenchymal, immune, or endothelial - so your results might not be that unreasonable.

Secondly, have you tried any of the canonical markers of the epithelial subtypes? p63, KRT5, MUC5AC, SCGB1A1, FOXJ1 for example?

In saying that, I know very little about scRNA-seq too, so I'm sure someone else will have a much more sensible answer!

All the best,

Sam

Hi,

you need to compare the disease versus control samples when doing the statistical test. I didn't fully get if the problem is that you lack the information of which samples belongs to which category or it is a coding problem. As for the former, usually datasets have a metadata file in which the sample names you find in the gene expression table are present, and the treatment information is included. If it is a coding problem, you can index the sample names to divide the data into Asthmatic, Healthy and COPD.

You can store RNA at room temperature as an ethanol precipitate. If you have RNA in solution, take some known amount - say 10-20 ug- and add 1/10 vol. NaAcetate and 2.5 volumes ethanol. The precipitate is stable and can be mailed. Upon receiving, spin down in a microcentrifuge and resuspend the RNA pellet in a specific volume of dH2O or whatever buffer you need.

Hmm, I'm not entirely sure how to run that myself, but since I lack coding experience I've been using the LatchBio platform. Through their RNA-seq and DeSeq2 workflows, I've analyzed and visualized a lot of the data at my lab. Hope that helps!

Sheikk I share you this guide:

Is under development, but I consider that is pretty good.

Also I think you must check the quality of your assembly, and in the guide that I passed you is very clear, but you can see an expanded explanation in Trinity's Github: