1.0 Figures. What you are showing and what you are not.
Hopefully, you started your project trying to answer an important question in your field.
Chances are that things did not go as planned.
The important thing at this point is to separate what you can tell directly by looking at your data from the interpretation of what your data could mean in a bigger context.
The line separating one from the other can be blurry, and it will become clear when you write your results. But first, you need to work on your figures and panels.
2.0 Results.
Here is where you go linking your results, in the form of panels, in a logical sequence.
In an ideal world, your results should build upon each other, and there should not be too many holes or a big hole in your line of reasoning.
This chain of reasoning will define what you can tell directly from your data and the strength of your manuscript.
To begin, write the results sections linking each panel from your figures using the following template paragraph.
We know this ___ (background). This is what we think is going on ____ (hypothesis). To test this, we did that (methods). We found this ___ (fig. Xy)(results). We conclude that ___(conclusion).
Followed by a similar template paragraph
Now we know this ___ (background). So now we believe what is going on is this ____ (hypothesis). To check if this is the case, we did this other thing (methods). We found this ___ (fig. Xz)(results). Taken together these results support this ___ (conclusion).
And so on.
You will need to expand or cut parts of this template depending on the importance of each result.
e.g., Instead of saying:
The RNA-seq data show this upregulated gene. So we believe this must be true. To test this, we perform qPCR. We found that the gene was still going up. Therefore we conclude that the RNA-seq data was fine.
Just say:
We validated the RNA-seq results by qPCR.
If the result is central to your line of reasoning, be explicit by including all parts of the template.
At this point is important to keep the background as lean as possible, to focus the attention on what you have found.
For this to be effective, you should define all the concepts needed to understand your results in the introduction.
3. Introduction.
Now that you know what you can reasonably infer from your data and what you cannot, you can write your introduction.
The first paragraph should define your system, what is not known, and what you hypothesize is going on that could explain what is not known.
The middle of the introduction should be used to define the concepts that will be used to introduce the background in the result section.
Refrain at this point from introducing concepts that are not used in the results but will be used in the discussion.
The final part of your introduction should explain what you actually did and can tell from your results and one or two final more speculative sentences saying what the implications are.
Do not speculate too much at this point, and make it explicit that this is something that you are suggesting.
You can expand more on this in the discussions.
But before this, you can write the abstract.
4. Abstract. The abstract is a compressed version of the first and last paragraphs of the introduction.
It should include, what you are studying, what is not known about it, what you hypothesize is missing in the current models, what you did to test this, what you found, and how you interpret your findings.
Does it sound familiar?
Yes, it is again the Background, hypothesis, methods, results, and conclusion, but for your whole manuscript.
5. The title. Here you should look at the abstract and summarize the results in one sentence.
6. Discussion. Here is where you put your findings back in the context of your first big question.
Start by restating the problem, what you did and what you got.
Then introduce all concepts that will help you interpret your findings in a bigger context.
Finally, conclude by interpreting your findings in the context of these other findings. And, importantly, state how your findings change the way you look at your system.
7. Methods. The section can usually be written at any point, possibly in parallel with the others, or even better after finalizing each experiment.
Well, that was the first draft!
Now you should go through steps 1 to 7 again at least 10X times to bring cohesiveness to the manuscript.
To summarize.
1.0. Put together your panels 2.0 Link then with a narrative (results) 3.0 Define concepts to understand the results background (Introduction) 4.0 Summarize your narrative in one paragraph (Abstract) 5.0 Spell out your main result in a sentence (Title)
6.0 Put your results in a bigger context (Discussion) 7.0 Explain how you did everything (Methods)
I hope you like this method. Please bear in mind that these are just tools and not rules. And things can be done differently depending on your needs.
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The first paper of our lab is out! We investigate the processing of a mouse coronavirus RNA during infection. The viral RNA can exist in at least two states, with the transition between them being mediated by the poly(A) tail uridylases TUT4/7. [Thread] rdcu.be/dascE
Coronaviruses are positive-strand RNA viruses featuring a 3’ poly(A) tail. The poly(A) tail changes in size within the cell, but we know little about this process.
(Kim et al., Cell, 2020)
In 2018 @ericmiskalab showed that poly(A) uridylation of viral RNAs is a conserved defense mechanism. In collaboration, we found that TUT4/7 uridylate the mRNA but not the genomic RNA of the influenza virus, leading to a delay in viral replication. @ericmiska (LePen, NSMB, 2018)
Bioinformatics can be fun, but with multiple steps and dependencies, things can get complex fast. And then the despair. How did I generate this intermediate file? What parameters did I use to run this script? Anxiety
The solution: A workflow manager
A 🧵 on how to get started
What is a workflow manager to begin with?
In short, it is a language to build (bio)informatics pipelines.
Essentially you provide a list of input files, steps to execute, and output files, and the workflow runs the steps necessary to get the output files from the input files.
The advantage is that with one click, you can replicate the whole pipeline. Essentially you avoid doing this manually, which is not only tedious but also error-prone.
If you are in the job market for a principal investigator position in a human health-related field, I strongly recommend you apply to the NIH Stadtman program. irp.nih.gov/careers/facult…
A 🧵 about why I believe so.
Full disclosure, I am currently a Stadtman investigator at the National Institute of Environmental Sciences (NIEHS).
First, how did I get here?
In 2018, when I was looking for jobs, I came across the Stadtman program, which looked amazing; Tenure-track position; fully funded; salaries for multiple postdocs, biologist, and myself covered; and you get to work at the NIH!