Transcription - Science topic

Do you want advice about the software or about qPCR primer design or both? I can help with qPCR design considerations, but not the software.

Thank you again for your help! I hope my experience can help. did it by improving the quality of the plasmids. I was using the phenol-chloroform method as described in the manual to purify it after linearization, which has a process of freezing the purified plasmid with NaAC and Ethanol for precipitation. It was time-consuming and harmful to my plasmid. Because the plasmid template could somehow inactivate during the freeze-thaw cycle. I finally used the purification kit, which is easier and allows me to use fresh plasmid every time. I am kind of sure thatan this freeze-thaw cycle damaged the plasmid (or maybe by other mechanisms) because once I freeze the plasmid, it won't work for IVT again.

Hi Muhammad,

The relationship between a transcription factor and its target gene can also be determined by reviewing the literature. The Alphafoldserver website can be used to analyze the interaction between transcription factors and target genes.

First, access the Alphafoldserver website and select two modes:

After submission, the results will display the binding affinity between the transcription factor and the target gene.

Hope this helpful, Feel free to reach out for more details.

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.

Thank you very much! Kristina Sharlo

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.

It depends. There are "non-sense mediated decay" pathways that can remove mRNAs in the cell. But sometimes the mRNA expression levels are not changed. You'll have to figure it out empirically for your gene of interest.

Noor Fadhzana Mohd Noor Got it!!! thank you so much!!!

Ok. But, how many focus groups? How many interviews? Length of each? Get a total--an exact number and length if possible How many researchers on the project?

Thank you so much for your suggestions ! Have a great day

First, you need to confirm that apart from the target band, these is no any other bands observed in your PCR product which is larger than 95 bps. If the PCR product is pure, you may need to purify the tRNA transcripts using denaturing gel and verify its molecular weight by Mass spectrometry after purification.

Good luck.

Mouse U6 promoter transcription starts at the +1 position (23 nt after the TATA box), with G as the preferred initiation nucleotide. See this article:

Simultaneous RNA-Seq and proteomics are uncommon due to several technical challenges. First, RNA and proteins require different extraction protocols, and attempting to isolate both from the same sample can compromise yield and quality. RNA and proteins also differ in stability, turnover rates, and abundance, complicating simultaneous extraction. Proteomics is more complex, as proteins undergo post-translational modifications (PTMs) that require specialized mass spectrometry techniques, unlike RNA-Seq. Additionally, quantifying transcripts and proteins involves different methods, making it difficult to correlate their levels. Limited sample availability can further reduce material for both analyses, and the lack of standardized protocols for integrating RNA-Seq and proteomics data adds to the difficulty of adopting this approach.

For transcription software suitable for research in Uganda, consider the following recommendations:

These tools can enhance transcription efficiency for research projects.

As an academic in social work, measuring empathy in asynchronous conversations requires both a qualitative and quantitative approach to analyzing written texts. Since asynchronous conversations are text-based, we can use a number of tools and measures that rely on content analysis.

Sentiment Analysis: Natural Language Processing (NLP) techniques can be used to analyze texts and infer the level of empathy based on the linguistic patterns used. The focus here is on words and phrases that demonstrate emotional understanding and recognition of others’ feelings.

Qualitative Frameworks: A qualitative framework for analyzing written texts can be developed based on empathy theories in social psychology and social work. This is done by identifying indicators of empathy such as expressing appreciation for others’ experiences, or offering emotional support.

Social Interaction Scale: This scale can assess the quality and extent of social interaction between participants, including how responsive they are to others’ feelings and opinions.

Frequency Analysis: Measuring the frequency of use of empathic phrases such as “I understand how you feel” or “I agree with your experience” can be an indicator of the level of empathy.

Additionally, we could design a custom tool to measure empathy in academic discussions based on existing models such as the Davis Interpersonal Reactivity Index and adapt it to fit the analysis of asynchronous texts.

I was wondering if you found a good answer to this question?

don't worry, this happen when there is a lot of material in one position (here 18 and 28S): there is no room for the probe to hybridize so there is no background at this position ... hence the white bands. It happens also with proteins in western blot ....

Dis you mean start nucleotid (any of A,T,C, G bases) and not codon? Start base in transcription can be well upstream of start codon.

Well, creating a semi-permeable transcription bubble can be challenging in the context of the structural stability of DNA as it tends to reanneal to its double-stranded form. Next is the concern of replication, which involves the fact that semi-permanent unwinding can potentially hinder the replication machinery from proceeding with DNA replication. Lastly, to maintain the transcription bubble to its semi-permanent unwound state, RNA polymerase is required to be halted in its activity at the transcription site, which could, in turn, lead to instability and interference in the replication of the plasmid. Considering these aspects, I believe three probable yet theoretical strategies can be adopted in this regard. First is genetically engineering a modified RNA polymerase, which can maintain the plasmid DNA at its single-stranded state by getting associated at a precise plasmid location without hindering the transcription process. Second is implementing genetically engineered single-strand binding (SSB) proteins, which can keep the plasmid DNA at its unwound state without interfering with RNA synthesis. Lastly, chemical molecules such as intercalating agents are introduced, which can develop proximal unwinding by being inserted at the nitrogenous base pairs of plasmid DNA; Molecules that are enhancers or activators of helicases; Hydrogen bond destabilizers like Di-Methyl Sulfoxide (DMSO), Urea or Formamide which can perform denaturation of double-stranded DNA; Cross-linking agents like Psoralens which forms covalent cross-linkages between single-stranded DNA molecules and DNA or RNA polymerases; Ligands which associate with single-stranded DNA such as Peptide Nucleic Acids (PNAs) and nucleic analogs which stabilizes the single-stranded structures of DNA; Alkylating agents such as Nitrogen and Sulfur derivatives of Mustard gas, Ethyl Methanesulfonate (EMS), Methyl Methanesulfonate (MMS), N-Nitrosoureas and Temozolomide. Nevertheless, besides being hypothetical, all these strategies have cons of their own.

I'd recommend Riverside.fm for transcription. Its quite more accurate than other tools I've used.

Hi Maria

We have been using templates of size 500-600 bp and we keep incubation time for 4 hrs. If you keep for longer time you can see degradation of finally synthesized mRNA

are you performing a gene reporter assay?

uhm maybe this paper could help:

"In general, transcripts that are shorter require a longer reaction incubation to allow for the highest yield (weight of RNA), as more RNA polymerase initiation events need to occur to reach the same weight of RNA compared to a longer RNA." - O’Donnell et al., Technical note "Scaling of High-Yield In vitro Transcription Reactions for Linear Increase of RNA Production", New England Biolabs.

Hello Alexandre Luiz Korte de Azevedo

Yes, qPCR can be used. It would be the most direct detection method to measure siRNA-mediated target knockdown. However, while using qPCR, the design of primers is an important consideration since using a primer set targeted to the wrong region may result in false negative results.

I would recommend that you should also perform Western Blot if you have the facility since it will show whether it is having the desired effect on functional protein as well. The qPCR results will show that the mRNA level of each gene is significantly lowered in the transfected cells than control cells. The western blot results can be used to correlate with the qPCR results where the protein band intensity reduction may be seen in siRNA-treated cells.

In this way, you could have valuable insights from protein as well that you will miss if you decide to just look at the transcripts alone.

Best.

İlayda NUR Belen

Please, look at discussion in https://www.researchgate.net/post/MRNA_synthesis_and_purification_T7_MEGAscript_Transcription_Kit_troubleshooting

There is one advice by @Victor G Stepanov that I would recommend too: "Actually, it might be useful to set up the T7 transcription reaction in small scale (~ 20-30 mcl), and then resolve the reaction mix on a gel to see if you had the mRNA synthesized in the first place."

Naïve T cells typically express CD62L.

Using a different grade of cells for your positive control could potentially introduce variability or inconsistency into your experiment. It's generally recommended to use the same grade or quality of cells for both experimental and control groups to ensure reliable and accurate results. However, if you have valid reasons for using a different grade of cells for your positive control, it's essential to thoroughly validate and justify this decision to ensure the integrity of your experimental findings.

Are you looking for repositories that make qualitative data available for secondary analysis? If so, you might consider the Qualitative Data Repository at Syracuse University.

Agreed. After first cut in any accessible place on scDNA this DNA will not be anymore supercoiled and be digested as usual.

@Robert Adolf Brinzer The question is, what is the possible structural and functional role of newly discovered SIRC (Short Interrupted Repeat Cassettes) elements in plant genome. The point is that in bacterial genomes there are CRISPR cassettes which are looking similar to SIRC but have nothing common.

Dear Esteemed Colleague,

Greetings. I hope this message finds you well and immersed in the dynamic field of genetic engineering and regulation. Your inquiry regarding the differences between the CRISPR-dCas9 system and traditional transcriptional regulatory methods highlights a pivotal area of interest in contemporary research. Below, I provide a detailed comparison that elucidates the distinct features, applications, and implications of these two approaches to gene regulation.

Overview of CRISPR-dCas9 System

The CRISPR-dCas9 (dead Cas9) system is an innovative adaptation of the CRISPR-Cas9 genome editing technology. In this variant, the Cas9 nuclease is catalytically inactivated (dCas9), rendering it incapable of inducing double-strand breaks. Instead, dCas9 is harnessed as a precise targeting mechanism to modulate gene expression when fused to transcriptional activators or repressors, or when employed to physically block transcription machinery access.

Traditional Transcriptional Regulatory Methods

Traditional methods of transcriptional regulation encompass a broad spectrum of techniques, including the use of small molecules, RNA interference (RNAi), and overexpression or knockdown of transcription factors or co-regulators. These methods typically act by modulating the cellular concentration of regulatory molecules or by directly interfering with the transcriptional machinery.

Key Differences

Conclusion

The CRISPR-dCas9 system represents a significant advancement in the field of genetic engineering, offering specificity, flexibility, and precise control over gene expression that surpasses many traditional transcriptional regulatory methods. However, the choice between CRISPR-dCas9 and traditional methods should be informed by the specific requirements of the research project, including the desired level of control, target specificity, and potential for off-target effects.

Should you have further inquiries or wish to delve deeper into the applications of CRISPR-dCas9 in transcriptional regulation, please do not hesitate to reach out. I am here to support your scientific exploration and contribute to the advancement of genetic research.

Warm regards.

Check out this protocol list; it might provide additional insights for resolving the issue.

I don't work in virology, so there may be some conventions for virus genome annotation of which I'm ignorant. However, in the eukaryotic setting where there are often broad and complex regulatory interactions at play in the transcription of a gene, a variant being within 5kb upstream of the TSS would be sufficiently close to implicate it as potentially relevant to the expression of the gene. With this in mind, I suspect the variant at position 77 is associated with gene-NP because it lies within its 5' UTR, while it is associated with genes-P, -F, and -M because their TSSs are all less than 5kb away from the variant. These annotations alone do not mean the variant at position 77 is at all functionally relevant, let alone relevant to the genes to which it is assigned as a "5' UTR variant" or an "upstream variant".

dentifying the position of a promoter on a plasmid and determining the direction of transcription involves several steps, including sequence analysis and understanding of promoter elements. Here's how you can approach it:

By combining sequence analysis, promoter prediction, and experimental validation, you can identify the position of the promoter on a plasmid and determine the direction of transcription, providing valuable insights into the regulation of gene expression in the plasmid construct.

l This list of protocols might help us better address the issue.

There are a myriad examples of this sort of ORF structure in eukaryotic viral systems (particularly RNA ones), where it has evolved to diversify/expand coding capacity and/or regulate viral gene expression. At first pass you can predict relative expression levels by examining the Kozak consensus around each AUG codon. We usually consider the -3 and +4 positions to categorise them as strong, middling and weak. If the first AUG is strong, you typically won't get much of the second product. If the first AUG is medium or weak and the second is strong, you'll get lots of the second product. Confounders to look out for; 5'-UTR length and protein stability. AUGs very close to the 5'-cap are often not translated well. Shorter proteins are harder to detect and if they're too short (or the wrong sequence) to fold up into a coherent structure, they'll be turned over rapidly and can be hard to detect unless (or even if) the proteasome is turned off.

After that, it can get properly complicated with all sorts of ribosomal gymnastics depending on the exact sequence of the transcript.

Bugs - need to consider Shine-Dalgarno (sp?) sequences or lack of, I think. But my undergrad lectures on this were many years ago :)

Cheers

Paul

Sequencing amplification product is always a good option to determine the specificity of the PCR. In addition, you should at least run an end-point PCR with a low number of cycles for an appropriate house-keeping gene as control for your input.

Could it be possible that your primers also amplify genomic DNA, which basically always contaminates your RNA unless you conduct an DNAse treatment step. If so, design exon-exon spanning primers.

Unfortunately, this is not novel. Many have already done that. You're looking for is CRISPRi (silencing) and CRISPRa (promoting).

Dear Prof. Mukaram Bhat

Genome browsers often provide links to external databases that contain additional information about transcripts and genes. You may find links to resources such as NCBI Gene, Ensembl, or RefSeq.

Regards

Jogeswar Tripathy

We leave out the ethanols then tomato ist still present

Hi,

some expression vectors also carry sequences for in vitro cell-free translations. If your vector does not have it, try different one with these additional pieces. It will allow you to have your peptide or, at least, will show that problem with expression (on any level) in original setting. Good luck.

Rosie Hhh, Did you get a solution to this problem? I have a similar situation - be it PDF or Word or RTF, they all show as blank.

It should be fine. Just know that if you didn't do a cleanup step to remove the DNAase etc. then the DNA is still present (just chopped into pieces). What are you planning to do with the transcripts?

Hi Thuy

you can check on the UCSC database, there is a tool named "in-silico PCR", but if it's for human, the issue is well documented in papers since of huge variability in this region, degenerated primers are often used. on UCSC, in hg19, you can also check for qPCR primers designed in former studies (optional track in the expression toolbar).

all the best

fred

I intend to use open-ended questions and target linguistics students, although I haven't yet begun to formulate the questions.

Dear Susanta,

as far as I know, htseq and featureCounts take only sam/bam files as inputs (i.e., alignment files).

What I got is a count matrix on exon level. What I need is a summarization/quantification on transcript level. Or do you know a way to make these counting tools taking count matrices as input?

Best regards

Markus

Dear Andrew J Spiers and Susanta Roy,

Thank you very much for your answers.

Ok, are you trying to clone a new gene into pET29a?

It is also possible that your gene is transcribed at a very low level but translated at a high level from those transcripts.

I have recently been experimenting with artificial intelligence for the interview of qualitative data via ChatGPT, and I am very impressed with the results. In particular, I started by re-analyzing data from two of my previous studies, and I was surprised by how rapidly the program produced the main concepts from those studies.

Just asking a few general questions produced the important key dimensions, and asking follow-up questions gave more detailed information about each of those dimensions. Of course, the program cannot literally "interpret" the results for you, but it certainly could replace a laborious coding process as a tool for locating the core content that you need to interpret.

Like any other approach to qualitative analysis, it does require familiarity with your data (you can't just throw anything at it), but beyond that, the program has a strong potential for being an alternative to existing techniques for the initial stages of working with qualitative data.

quality control, just to be sure samples are not degraded too much that could disable the best amplification.

on the other hand, you can also use DMSO at 5% to enhance PCR

fred

Abhijeet Singh Thank you for your response and mentioning my earlier post! My belief is that researchers would know tools that are missing based on the fact that they would run into such problem often during their research. If there is some manual analysis task that researchers can automate, I believe that PeptiCloud can be the perfect platform to develop and make those tools publicly available. (For instance, PeptiCloud has a unique feature that allows users to further alter codon sequence of each amino acid after codon optimization with respect to a specific bacterial strain). With that being said, if you could check out PeptiCloud for yourself and see if anything could be added or improved, that would be greatly appreciated!

Hello Ushnish,

If your intention is to show a summary of all possible pairwise comparisons among 18 conditions (= 153 comparisons), that will be a very full tabular display no matter what you do!

If only a few of the comparisons meet your criterion for statistical significance, you might restrict your display and summary to just those comparisons, and note that all others were nonsignificant.

One relevant point is that the Duncan test is likely not the best choice for several technical reasons. Consider something along the lines of the Benjamini-Hochberg method or the Ryan-Einot-Gabriel-Welsh method.

Finally, to address your question about letters, if the Duncan output showed 11 "homogeneous subsets," then yes, you may attach letters a-k as appropriate to show instances of nonsignificant differences (subject to the implied concern above about density of your display).

Good luck with your work.

There are many but Sonix and Rev are recommended

1) Check mycoplasma contamination.

Only after number should you proceed to optimizing transfection efficiency because mycoplasma contamination inhibits transfection (having experienced it myself). It is more common than thought.

Can you provide a bit more detail? Because...I am really not getting what you're trying to do, or why.

You have a 29kb mRNA that you want to produce via in-vitro transcription, so is your gene currently...on a IVT-compatible plasmid?

Are you proposing to clean up your transcripts after IVT? If so, how?

And then are you proposing to use them in an in-vitro translation system, too?

And if so...well, why? Could you not just express it in mammalian cells and let them do all the work for you? Mammalian cells would have no issues transcribing a gene 29kb long (they can handle genes 2.3Mb long, after all).

Have a look at the ingredients in the buffer supplied with the DNase and compare it to the Reverse Transcriptase buffer composition. Are they similar? Each enzyme will be optimised to work in specific buffer conditions and it's possible that the DNase buffer will be inhibiting the Reverse transcription reaction.

Similar to the poster above, I would recommend an additional cleanup, using a Zymo column to remove the DNase buffer, and any residual DNAse. The columns have a good RNA recovery rate, and I think you can ask for free samples ;-).

Thanks for the help and support! Yes I have done a blast of my primers and they match with my target gene.

Do you think I am "safe", or do I have to sequence the dna in the band to be 100% sure?

Hi Sehrish Sarfraz ,

I come a bit late but maybe this could be useful for others.

You totally can amplify both transcripts with the same set of primer.

For this, you need to design a forward primer targeting the 5'UTR of your pre-messenger RNA, and the reverse targeting the 3'UTR. If, as you said, both your transcripts are spliced from the same pre messenger RNA they have to share those features.

Run the PCR products on an agarose gel.

Assuming that both your transcripts isoforms don't have the same lenght, you can extract both band with a gel cleaning kit and then send them for SANGER sequencing as a verification.

Hope it helps,

Philippe.

One specific limitation mentioned is the difficulty in accurately identifying and quantifying isoforms expressed from a gene, especially for very long or highly variable transcript isoforms. This is particularly relevant in the human transcriptome, where transcripts can vary greatly in length, ranging from 186 base pairs to 109 kilobases. The presence of such long and variable isoforms poses challenges for proper annotation and quantification.

This applies to not only this question but for all the question you asked...

Please focus on your class/notes/lectures. Get the basic and fundamental knowledge. Asking random questions here would not help.

The SV40 (Simian virus 40) promoter is a strong viral promoter commonly used for driving gene expression in various experimental systems. While the presence of a TATA box upstream of the transcription start site is a common feature in many promoters, the SV40 promoter is unique in that it lacks a canonical TATA box.

The SV40 promoter utilizes an alternative mechanism for transcription initiation called the "TATA-less" promoter. Instead of relying on a TATA box, it utilizes other elements and transcription factors to initiate transcription. The absence of a TATA box in the SV40 promoter does not necessarily impair its ability to drive gene expression.

Therefore, in your current cloning strategy where the transcription start site is located just 8bp from the end of the SV40 promoter, it is likely that the expression can still occur without the presence of a TATA box. The SV40 promoter contains other regulatory elements and transcription factor binding sites that can facilitate transcription initiation.

However, it's worth noting that the exact transcriptional activity may depend on the specific context and the downstream sequence elements present in your plasmid. Experimental verification, such as measuring the expression levels of your gene of interest, can help confirm the functionality of the modified SV40 promoter in your specific system.

There are several characterization techniques for RNA that can be used to assess the success of transcription:

In your case, gel electrophoresis and spectroscopy can be used to assess the success of your RNA transcription. Gel electrophoresis can help confirm the size of the RNA strand, while spectroscopy can be used to assess RNA purity.

These video playlists might be helpful to you:

No alkylating agent (with the possible exception of those containing a targeting moiety) can be considered selective it is more of a general preference (see Coles, B. Effects of modifying structure on electrophilic reactions with nucleophiles. Drug Metab. Rev. 1985, 15, 1307−1334.) MNNG in addition to targeting the O-6 position of guanine, will cause depurination, strand breaks, alkylate phosphate etc. etc. For a more detailed discussion see

MNNG will do what you seek, but you must be aware of it's other targets, which are not restricted to the O-6 position of guanine in DNA, or even nucleic acids, or macro molecules.

Bita Faraji thanks a lot for these recommendations! I will check them out.

Hi,

You can do qPCR to check the expression of the target genes.

Dear Mohd,

the term transcript variants refers to the different mature mRNA products from the transcription of the same gene. On the other hand the term isoform refers to highly similar proteins that are products of the same gene (usually).

During transcription there can be different mRNA products due to the utilization of different start codons within the sequence of the gene and also due to the utilization of different sets of promoters upstream the coding sequence. Additionally during pre-mRNA maturation (splicing) different mature mRNAs may occur due to alternative splicing sites. Furthermore, mutations can create new starting codons, and also influence the splicing and the maturation of pre-mRNAs. Altogether, these mechanisms (the major mechanisms) produce the different variants (transcript and splicing variants) that you see in the bibliography.

Different mRNA products (transcript and splicing variants) of the same gene can produce proteins with high similarity that together constitute the different isoforms.

I hope that was helpful

Kind regards

That is a complicated question that comes down to the mechanism used by RNA polymerase to initiate transcription. Fundamentally RNA polymerases do not need primers because they have evolved to be able to initiate transcription at specific sites (promoters) that are thermodynamically favorable for initiation.

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)

If by "polymerisation of coding region" you mean "mRNA synthesis", then honestly, there is no fixed answer to this question (for eukaryotes, certainly).

Many genes have enormous lengths of intronic sequence, all of which must be transcribed, only to then be spliced down to a comparatively small mRNA for translation. Dystrophin, for example, is 2.3 million bases long, but is spliced down to ~14,000 (i.e. 99.5% of the transcribed nucleotides are spliced out and recycled).

Here transcription clearly requires a greater energy input on a 1:1 mRNA:protein basis, whereas other shorter (or intronless) genes might be closer to parity.

However you also need to factor in energy requirements for recruitment of initiation factors, unwinding of the transcriptional start site, scanning, abortive initiation, etc, which will also vary from gene to gene. Some genes are permissive, other are less so.

You also need to factor similar variables to translation: targeting of mRNA, recruitment of translation initiation factors, unwinding and scanning of 5' UTR, etc.

And finally, this all assumes you are comparing mRNA and protein on a 1:1 basis, which is entirely inaccurate. A single mRNA can be translated many, many times, so a large energy investment in one transcript might ultimately pale in comparison to the vast energy investment in making 10,000 proteins from that one transcript. Alternatively, a single mRNA might be degraded without ever being translated even once (some 'immediate early' genes are transcribed continuously, 'just in case', but then degraded if not needed).

If you want to know (more generally) 'what uses more energy in a cell, transcription or translation?', then the answer is almost always translation: translation (i.e. protein synthesis) consumes about 50% of the energy budget of a proliferating cell. Ribosomes are not very efficient, and cells contain LOTS of them (~80-85% of cellular RNA is just ribosomes). They are always in use.

I wouldn't worry about asking questions like this, by the way: it's an excellent question and exposes quite how many complexities there are to even such an ostensibly simple consideration.

Hi

the main point is to know wether you used total RNA or polyA+ RNA. Since in total RNA you'll get mature and non-mature templates, it's not surprising. on the other hand, in regards to a previous work where I sequenced a targeted gene after 3'RACE-PCR (full length sequencing), it's not surprising to find new exons and transcripts. you just need to count each molecules and see if it has sense in your study to go further.

best new year

fred

ELAN, software allows you to store video/ audio file, annotate and transcribe files. at the end it can do language analysis as well. parts of speech etc...

The average length of transcripts on the mouse chromosome Y is generally shorter than those on other chromosomes due to the unique characteristics of the chromosome Y.

Chromosome Y is the smallest and least gene-dense chromosome in the mouse genome, with only a few hundred protein-coding genes. Most of these genes are involved in male fertility and the development of male characteristics. In addition, chromosome Y lacks most of the repetitive sequences and transposable elements that are present on other chromosomes, which can lead to the production of longer transcripts.

It is also worth noting that the gene expression patterns on chromosome Y are different from those on other chromosomes. Chromosome Y is generally expressed at lower levels than other chromosomes, and the genes on chromosome Y are often expressed in a tissue-specific manner. This may contribute to the shorter average length of transcripts on chromosome Y.

Overall, the shorter average length of transcripts on chromosome Y can be attributed to the unique characteristics of the chromosome and the patterns of gene expression on the chromosome.

Alright, I will try to share what tips/tricks I can.

Honestly, while RNA is vastly more labile than DNA, it isn't really some sort of mystic-grade vulnerability, and you don't need utterly RNAse free environments to isolate perfectly viable RNA. They will help, obviously, but just starting with RNAse-free stuff, using careful pipetting and not making obvious mistakes will usually be sufficient.

So: use filter tips. Here the filter is primarily protecting your sample from whatever gunk might be hiding up in your pipette barrel. Use filter tips for everything (1000ul, 200ul, 10ul).

Use RNAse-free microcentrifuge tubes (most prepacked tubes should be certified RNAse free): keep a dedicate bag for RNA work, keep the top sealed/folded over when not in use, and only fish out tubes with gloved hands. If you put an ungloved hand into the bag, then assume the bag is now no longer good for RNA work (or use at your own risk).

Use RNAse free water for everything: either buy it, or make your own using DEPC or DMPC: add DEPC to 0.1%, shake vigorously and leave at 37degrees overnight with the lid of the bottle slightly loose. Autoclave, then close the lid tight.

Take small aliquots for working (I tip out 50ml at a time into a falcon tube) so you're not constantly dipping in and out of your stock. If an aliquot gets contaminated, or you suspect it's contaminated, throw it away, make another.

Use a bench area you trust: this doesn't mean you need a dedicated area, but use common sense (if a genomic DNA extraction protocol involves 'add 100ul of RNAse H', for example, go do that protocol somewhere else).

Use common sense in general: just be aware that the primary source of RNAse activity is the investigator: we are covered in bacteria all the time, and all of those are robust RNAse sources.

Wear gloves. Wear them basically all the time. If you think the gloves are dirty, change the gloves.

Next up: practical tips/tricks and when to be most careful.

If you can, freeze tissue. Freeze everything until you need it not to be frozen. RNA inside a sample frozen at -80 will endure far better than RNA inside fresh tissue, and while its frozen, it cannot be broken down by RNAses (they're frozen too).

Try to keep tissue frozen RIGHT up until you lyse/denature everything.

Frozen tissue is safe.

Lysis: I use trizol (or trizol equivalent) methods for almost everything. Almost nothing survives the addition of large amounts of chaotropic salts dissolved in phenol: a frozen sample covered in RNAses can still be used for RNA extraction if you dump it straight into trizol, because the RNAses will unfold and denature right along with everything else.

I typically freeze tissue in liquid nitrogen, store it at -80, crush to to powder under liquid nitrogen (i.e. never let it defrost) and then add trizol directly to the frozen powder. The first time the tissue melts, it's melting in phenol.

RNA inside trizol suspension will endure, and can indeed be frozen at -80 for longer-term storage.

RNA in trizol is safe.

Once you add chloroform to initiate phase separation: THAT'S when you need to start being extra careful. The aqueous phase is RNA in solution, and it's essentially unprotected. Collect aqueous phases one at a time, tilting the tube to minimise stuff falling into it. Cap tubes as soon as you're done transferring.

I typically use isopropanol precipitation rather than columns, because I like to see the size of my pellets, but all downstream stuff from phase separation is extra-careful-time. Precipitated RNA itself is actually fairly safe, since RNAses can't really degrade a solid chunk of dry RNA (accordingly, you can also freeze pelleted RNA at -80 for some weeks).

If you're going down column-based preps, then all the on-column stuff is largely out of your hands. Keep the columns wrapped up and clean (most come individually wrapped, but if they're in a bag, treat that bag as for tubes, above: gloves for all the things, seal up when not in use).

Isolated RNA should be either frozen immediately, or kept on ice for spectrophotometry/bioanalyser, and THEN frozen.

Try to make it into cDNA as soon as possible, and try to minimise freeze thaw: better to make a lot of cDNA in one batch than to keep dipping into it for multiple one-step reactions.

I would contact your chairperson or IRB representative and ask to be sure. They are the ones who decide what the requirements are, not APA. My requirement was 10 font, Times New Romans, single spaced. It does not matter how many pages it is, but double space would make your transcripts look like and encyclopedia.

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.

If you have the genome sequence then I would do an RNA-Seq from your organism, trying to get as representative and as much RNA as possible. Map all the reads to your genome and the start sites should become clear as the most 5' end of a transcript.

Hi Oleg

performing in silico PCR on UCSC, your primers did well perform amplification at the good position. issues are therefore not coming for the primers (see attachment). maybe you could try a touch-down PCR in a range of 10°c and add some DMSO to get more accessible target.

all the best

fred

there is a new software on the radar

DOTE is a new type of transcription software developed by the BigSoftVideo team. It is tailored for transcribing social conduct, conversation and multimodal interaction for research purposes. DOTE has been designed to support two specific standards of transcription for qualitative research, which are commonly used in conversation analysis, for example. DOTE has some of the features commonly found in other software -- such as video playback, a timeline and a visual waveform, synced playback -- but these features are streamlined and easier to use in DOTE. Moreover, it has enhanced features that do not exist in any legacy software so far, including transcript parsing, smart auto-completion, transcript heuristics, 360 video support, video-cues, export to publishable document (and subtitles) and version control. There are many more features and enhancements planned for the future. Our motto is make transcription fun again!😜