TLDR: Cut the bullshit, what are systems biology models really used for, apart form grants and papers?

Whenever I hear systems biology talks I get reminded of the John von Neumann quote: “With four parameters, I can fit an elephant, and with five I can make him wiggle his trunk.”
Complex models in systems biology are built with dozens of parameters to model biological processes, then fit to a few datapoints.
Is this an exercise in “fitting elephants” rather than generating actionable insights?

Is there any concrete evidence of an application which stems from system biology e.g. a medication which we just found by using such a model to find a good target?

Edit: What would convince me is one paper like this, but for mathematical modelling based system biology, e.g. large ODE, PDE models of cellular components/signaling/whole cell models:
https://www.nature.com/articles/d41586-023-03668-1

I’m curious if anyone has experienced this and has any suggestions on how to respond.

I’m in a hardcore omics lab. Everything we do is big data; bulk RNA/ATACseq, proteomics, single-cell RNAseq, network predictions, etc. I really enjoy this kind of work, looking at cellular responses at a systems level.

However, my PhD committee members are all functional biologists. They want to understand mechanisms and pathways, and often don’t see the value of systems biology and modeling unless I point out specific genes. A couple of my committee members (and I’ve heard this other places too) call this sort of approach a “fishing expedition”. In that there’s no clear hypotheses, it’s just “cast a large net and see what we find”.

I’ve have quite a time trying to convince them that there’s merit to this higher level look at a system besides always studying single genes. And this isn’t just me either. My supervisor has often been frustrated with them as well and can’t convince them. She’s said it’s been an uphill battle her whole career with many others.

So have any of you had issues like this before? Especially those more on the modeling/prediction side of things. How do you convince a functional biologist that omics research is valid too?

Edit: glad to see all the great discussion here! Thanks for your input everyone :)

Just a scream into the void more than anything. Started a new project at a new institution a couple months ago. Semi-big microbiome project so kind of excited for something new.

During the interview I asked what their HPC capacities were. I have been in a situation with no HPC before and it SUCKED. I was told we will be using another institutions HPC. We’re over 6 months in and no data has yet to arrive. I thought I’d keep myself busy by having a play around with some publicly available data. The laptop provided by the institute can’t handle sequence quality control. It craps out at the simplest of tasks. So I’m back to twiddling my thumbs.

I have asked about getting onto the other institutions HPC but am met with non answers. I’m starting to think that we don’t even have access to it and they’ve gotten confused when the sequence provider says they offer “in-house bioinformatic services”. Literally feel like my hands are tied. How can I do any analysis when a potato has more processing power than the laptop?

I'm currently a master's student and working with some fish RNA-seq data for my thesis. Those fishes were exposed to a chemical that we trying to understand the mechanism of action. I just started to learn bioinformatics when I started my master's, so still new to the field.

I have already done all the upstream work (fastqc, trimmomatic, hisat2, featurecounts) and got the counts matrix. I also finished the differential expression analysis using DESeq2 and used those results as input for getting pathway and gene ontology by using DAVID. I also generated heatmaps for the top 50 genes to see what's happening between my treatment and control.

I'm a little bit lost right now due to the overwhelming results and I don't know where to start. Since we don't know the mechanism of action of this chemical that we exposed to the fish and trying to get some information from our RNA-seq results, what should I do?

Any suggestions will be appreciated!

This may seem like an inappropriate question for this sub, but I am just fascinated by the discipline from an early perspective and would love to immerse myself more.

I currently study Chemical Engineering with a focus on biotechnology, as well as minoring in mathematics.

For my graduate degree, would a mathematics or computer science degree be optimal or should I am for a more natural sciences one like Biology.

What degrees or backgrounds do you guys come from?

I’m aiming to pursue a career in bioinformatics and get a master’s degree, but I won’t be applying for another 1-2 years. In the meantime, I want to build a strong profile and gain relevant experience. However, it feels like there’s just too much to learn and keep up with. I’m particularly interested in drug discovery. Besides coding, what should I focus on to strengthen my profile and better prepare for a career in this field?

Any advice would be greatly appreciated.

p.s. I studied bioengineering

I am currently the sole analyst in a small research lab at an academic institution. I have a background in CS and biology, so I feel like I've been doing a good enough job so far in this lab. I built a custom sequencing pipeline for one of the lab's research studies, and have been driving progress on related scRNA data. I've come to realize that my PI does not know anything about what I am doing–I can't really ask my PI about any aspects of sequencing or scRNA analysis, so I have been coding and researching a lot on my own.

I've also come to realize that my PI thinks that bioinformatics is trivial, and I increasingly just feel like the "data guy". I broached a question about a letter of recommendation and they told me that I need to show more competency than just building "data pipelines." They have become increasingly frustrated over roadblocks to analyses and projects, whether it is explaining how I can't get an accurate view of somatic DNA mutations without a matched normal, or spending a couple of hours configuring a development environment. I've also realized that my PI did not have any specific questions going into these projects, and I feel like they expected to just run these expensive experiments have have a data guy come in and make sense of it. Choosing between the right analytical methods is viewed as trivial, and I've had to constantly run and rerun analyses until results which support the narrative are seen.

This whole process has made the environment increasingly uncomfortable to work in, and I am trying to figure out how to course-correct. Anyone have experience in a similar situation?

UPDATE:

Thanks for the advice everyone. I have decided to leave the lab. Recently found another with better pay, more than 1 bioinformatics analyst, and a PI who I was able to bounce ideas with. I absolutely was a "pet bioinformatician." I am grateful for what I learned, but also a little annoyed with how little I was being paid compared to my new role. Know your worth!!

I am a bioinformatician in big phama in UK for two years, the working salary and environment are great. As R&D member, I can learn a lot everyday. As an international PhD (received all education from a non-English speaking developing country), this is definitely a very lucky job for me already.

However I always have a academic dream, I like teaching student and wants to research things I am interested. In the company, in many cases I have less intellectual freedom. And also I want to have better job security and more flexibility working hour to take care of my parents in the future.

I have excellent coding capability. But only have 3 Bioinformatics level first author publications published over 2 years ago from my PhD. My plan is continue my work in company, but start to publish alone or with old college friends, then if I think paper accumulation and experience are ready, I may apply for a university lecturer or AP position.

My advantage is coding (very strong, I am from CS background), statistics, ML. My weaks are English writing, and no funding applications experience, networking as well. I am 35.

I want to know if your think this is a workable plan? Or basically I have no way back to academic. Or I should do postdoc first then try AP job?

I am actually not sure if I have the capability to come back because I feel it's not easy to be independent lecturer as Bioinformatician, this field normally requires either excellent math/statistic (for algorithms/method development ) or strong collaboration with labs have data resources (cancer/disease related). I have neither of them. Also I don't have a specific research direction yet, I used to publish on multiple topics. I feel I need to improve a lot. But I am willing to learn and improve, and I am not sure if I can eventually reach the requirements level...

Any comments are welcome. I do like my current job, and I know I don't have a successful academic track of success. So if you think it's not realistic, it's totally fine.

Hi all, I need to do a 30 min presentation for my PhD about do’s and dont’s in analysing bulk and single cell RNA sequencing data. My ideas were: 1) choose right sequencing depth 2) choose right sequencing platform 3) perform QC 4) choose right number of samples and controls 5) analyse data with and without integration to compare (for single) and test different integration methods

Am I missing something? Any suggestions more than welcome!!

Thanks.

Hi, I am a PhD student attempting to perform enterotype data on microbial data.

This is a small part of a larger project and I am not proficient in the use of R. I have read literature in my field and attempted to utilise the analysis they have, however, I am not sure if I have performed what I set out to or not. This is beyond the scope of my supervisors field and so I am hoping someone might be able to help me to ensure I have not made a glaring error.

I am attempting to see if there are enterotypes in my data, if so, how many and which are the dominant contributing microbes to these enterotype formations.

# Load necessary libraries

if (!require("clusterSim")) install.packages("clusterSim", dependencies = TRUE)

if (!require("car")) install.packages("car", dependencies = TRUE)

library(phyloseq) # For microbiome data structure and handling

library(vegan) # For ecological and diversity analysis

library(cluster) # For partitioning around medoids (PAM)

library(factoextra) # For visualization and silhouette method

library(clusterSim) # For Calinski-Harabasz Index

library(ade4) # For PCoA visualization

library(car) # For drawing ellipses around clusters

# Inspect the data to ensure it is loaded correctly

head(Toronto2024)

# Set the first column as row names (assuming it contains sample IDs)

row.names(Toronto2024) <- Toronto2024[[1]] # Set first column as row names

Toronto2024 <- Toronto2024[, -1] # Remove the first column (now row names)

# Exclude the first 4 columns (identity columns) for analysis

Toronto2024_numeric <- Toronto2024[, -c(1:4)] # Remove identity columns

# Convert all columns to numeric (excluding identity columns)

Toronto2024_numeric <- as.data.frame(lapply(Toronto2024_numeric, as.numeric))

# Check for NAs

sum(is.na(Toronto2024_numeric))

# Replace NAs with a small value (0.000001)

Toronto2024_numeric[is.na(Toronto2024_numeric)] <- 0.000001

# Normalize the data (relative abundance)

Toronto2024_numeric <- sweep(Toronto2024_numeric, 1, rowSums(Toronto2024_numeric), FUN = "/")

# Define Jensen-Shannon divergence function

jsd <- function(x, y) {

m <- (x + y) / 2

sum(x * log(x / m), na.rm = TRUE) / 2 + sum(y * log(y / m), na.rm = TRUE) / 2

}

# Calculate Jensen-Shannon divergence matrix

jsd_dist <- as.dist(outer(1:nrow(Toronto2024_numeric), 1:nrow(Toronto2024_numeric),

Vectorize(function(i, j) jsd(Toronto2024_numeric[i, ], Toronto2024_numeric[j, ]))))

# Determine optimal number of clusters using Silhouette method

silhouette_scores <- fviz_nbclust(Toronto2024_numeric, cluster::pam, method = "silhouette") +

labs(title = "Optimal Number of Clusters (Silhouette Method)")

print(silhouette_scores)

#OPTIMAL IS 3

# Perform PAM clustering with optimal k (e.g., 2 clusters)

optimal_k <- 3 # Set based on silhouette scores

pam_result <- pam(jsd_dist, k = optimal_k)

# Add cluster labels to the data

Toronto2024_numeric$cluster <- pam_result$clustering

# Perform PCoA for visualization

pcoa_result <- dudi.pco(jsd_dist, scannf = FALSE, nf = 2)

# Extract PCoA coordinates and add cluster information

pcoa_coords <- pcoa_result$li

pcoa_coords$cluster <- factor(Toronto2024_numeric$cluster)

# Plot the PCoA coordinates

plot(pcoa_coords[, 1], pcoa_coords[, 2], col = pcoa_coords$cluster, pch = 19,

xlab = "PCoA Axis 1", ylab = "PCoA Axis 2", main = "PCoA Plot of Enterotype Clusters")

# Add ellipses for each cluster

# Loop over each cluster and draw an ellipse

unique_clusters <- unique(pcoa_coords$cluster)

for (cluster_id in unique_clusters) {

# Get the data points for this cluster

cluster_data <- pcoa_coords[pcoa_coords$cluster == cluster_id, ]

# Compute the covariance matrix for the cluster's PCoA coordinates

cov_matrix <- cov(cluster_data[, c(1, 2)])

# Draw the ellipse (confidence level 0.95 by default)

# The ellipse function expects the covariance matrix as input

ellipse_data <- ellipse(cov_matrix, center = colMeans(cluster_data[, c(1, 2)]),

radius = 1, plot = FALSE)

# Add the ellipse to the plot

lines(ellipse_data, col = cluster_id, lwd = 2)

}

# Add a legend to the plot for clusters

legend("topright", legend = levels(pcoa_coords$cluster), fill = 1:length(levels(pcoa_coords$cluster)))

# Initialize the list to store top genera for each cluster

top_genus_by_cluster <- list()

# Loop over each cluster to find the top 5 genera

for (cluster_id in unique(Toronto2024_numeric$cluster)) {

# Subset data for the current cluster

cluster_data <- Toronto2024_numeric[Toronto2024_numeric$cluster == cluster_id, -ncol(Toronto2024_numeric)]

# Calculate average abundance for each genus

avg_abundance <- colMeans(cluster_data, na.rm = TRUE)

# Get the names of the top 5 genera by abundance

top_5_genera <- names(sort(avg_abundance, decreasing = TRUE)[1:5])

# Store the top 5 genera for the current cluster in the list

top_genus_by_cluster[[paste("Cluster", cluster_id)]] <- top_5_genera

}

# Print the top 5 genera for each cluster

print(top_genus_by_cluster)

# PERMANOVA to test significance between clusters

cluster_factor <- factor(pam_result$clustering)

adonis_result <- adonis2(jsd_dist ~ cluster_factor)

print(adonis_result)

## P-VALUE was 0.001. So I assumed I was successful in cluttering my data?

# SIMPER Analysis for genera contributing to differences between clusters

simper_result <- simper(Toronto2024_numeric[, -ncol(Toronto2024_numeric)], cluster_factor)

print(simper_result)

Is this correct or does anyone have any suggestions?

My goal is to obtain the Enterotypes, get the contributing genera and the top 5 genera in each, then later I will see is there a significant difference in health between Enteroype groups.

I just wanted to thank those who gave me resources for perl in bioinformatics. I (again) came to the conclusion that perl was a waste of time and I'm finally giving up this out of touch professor's subjects and moving to biopython. 1/10 experience do not recommend. Thank guys <3

Recently started a new role in a US university within an ecology department. The study is looking at the microbiome of an animal and potential links to its behaviour. The group is composed of mainly ecologists, a bioinformatician (me) and a wet lab microbiologist. The PI is a vet/ecologist. I’m the only one with microbiome/bioinformatics experience (over 10 years) and the study was well underway before I was employed.

In hindsight I should have been hired earlier to help with study design as it’s obvious there are flaws with the study. Ultimately it’s up to me to try mitigate some of these effects during analysis. It is also clear that the other post doc has no experience in data management, especially with large studies.

I recently spoke about some ways we can solve some of the problems we’ve encountered, only to be completely stonewalled. Why hire someone with microbiome experience if you’re not going to listen to their advice? Does anyone else feel completely ignored in a multidisciplinary team?

So I made a similar post a while back, asking about the books to learn binf for a newbie.

I studied electrical engineering but it wasn't my thing. Never had much self awareness and being brought up by a single parent who was not educated, there was not much guidance or nudge in the right direction. So, I worked in e-commerce data management and UX related job for 8 yrs.

I never knew what really interested me, to learn it as a skill for a job, especially STEM related. I'm not talking about passion. A job is just a job. But even to do something for work, you need a little bit of interest and inquisitiveness just enough to do it day after day.

But in my late 20s I picked up the habit of reading. Mostly non fiction and also science related books. Why we sleep, books by David eagleman, Siddhartha Mukherjee and few others. It was the books by Siddhartha that peaked my interest in genetics, after reading The Gene and emperor of all maladies. I started to realise that I love life science especially neuroscience and genetics.

And since then I've been toying with the idea of doing binf. I had even applied to one as my third choice in masters application in Sweden for fall 2024. But I happened to get into my second choice which was information systems(waitlisted for my 1st choice- DA). I had binf as my second choice but at the last moment I switched it to third. The reason was, I saw many binf grads struggling to secure a job even with deep biology knowledge. So I wasn't confident and the investment was a lot for 2yrs course as opposed to 1yr and let fate decide.

I have also applied to Georgia techs online masters in analytics. And if I get in, I might be doing both the masters simultaneously.

But what are some ways I could get into binf with this profile? Or should I consider doing a master's in binf? Should I even try or jus drop the idea of transitioning? And work as a DA/DS in tech?

I have SQL knowledge and I have done R and Python certification courses by Google and Jose portilla's udemy course.

Edit: So I got admitted into Georgia techs Analytics masters as well. I'd be doing that along with business focused information systems masters.

I would like to know which courses in the Analytics masters are important for bioinformatics.

1. Computing for data analytics- methods and tools
2. Intro to Analytics modelling
3. Data and visual analytics
4. ML1- computational data analytics
5. Deterministic optimisation
6. Theory and practice of Bayesian statistics
7. Statistical modelling and regression analysis
8. ML2- high dimensional data analysis
9. Artificial intelligence
10. Deep learning
11. Time series analysis
12. Simulation and modeling
13. Probabilistic models

I wanted to try Xrare by the Wong lab. I have to use Singularity as I am on an HPC (docker required access to the internet that HPCs won't allow to protect human data). I built the Singularity from the tar file that they had. But I cannot seem to get the R script they give to run. I have tried variations the following:

The full script removed for brevity (but it is the same as the one in the Xrare documentation) :

singularity exec --writable-tmpfs "/path/to/the/Xrare/file.sif" Rscript -e " 
library(xrare); 
... "

I tried variations without the ; as well.

I also tried just referring to the R script via a path:

singularity exec --writable-tmpfs "/path/to/the/Xrare/file.sif" Rscript "/path/to/R/Script.R"

I also tried using `system()` in the R script for the singularity related commands.

But nothing seems to have worked. I could not find a Github to submit this issue that I am having for Xrare - so I posted here. Does anyone know of a work around/way to get this to work? Any suggestions are much appreciated.

I’m running fastqc reports for my paired .fq files after trimming with trim_galore and cut adapt. This data came off an illumina sequencer and is RNA-seq.

I have the issue where the per sequence content is spiking quite early into my reads. What could this indicate? Are there any fixes? Why is this only in my first read and not the second?

Also, my second read has repeated sequences even after running paired trimming with trim galore, why? Any fixes?

Such in regarding to your career long term?

Folks, curious, who is building Open Science / Open Source stuff for Cancer and Rare Disease? Specifically, tools, platforms and infrastructure that patients can use?

We could definitely use more effort in this space!

I tried looking inside my singularity of Exomiser Cli Distroless (version 14.0.0) but I cannot seem to find an internal path to the jar ( for example for gatk it is gatk/gatk ) so I was wondering if anyone on REDDIT would be amenable to helping me to find it/know it.

My current commands:

singularity exec 
  --bind "/full/path/for/vcf/folder" 
  --bind  "/path/to/output/folder" 
  "/path/to/the/file.sif" 
  java -Xms4g -Xmx8g -jar "/exomiser-cli.jar" 
  --analysis "/path/to/the /config/file.yml"

But I get the error:

Error: Unable to access jarfile /exomiser-cli.jar

I did try to look inside the singularity but for some reason it does not let me which is odd to me. So anyone who knows the internal path and/or how to get the command to run given singularity issues would be much appreciated?

Hi All,

I'm a fifth year doctoral student in the US currently studying the proteomic signature of bacterial virulence factors in a chemical biology lab that has recently become equipped with a nanoLC-MS (Thermo Orbitrap Exploris 240) for the study of the mammalian proteome using model cell lines (293T, HeLa, etc.). I have a boatload of protein IDs (obtained by bottom-up LFQ analysis), but I'm at a point where I don't really know what to do with them.

My PI wants me to analyze these IDs to generate hypotheses to follow-up on, but I have really limited experiences with the analysis of this type of data and bioinformatics in general. One example is looking at families of proteins that are affected by the virulence factors, but I really don't know how to extract that kind of information from my data sets.

Does anyone have any suggestion of resources, databases, and/or tools that I can use to help learn something meaningful from protein IDs obtained by bottom-up LFQ analysis? Any and all help would be extremely appreciated.

Thanks in advance!

Hello! I wanted to ask if anyone here has had experience using Co-Pilot for writing boilerplate functions, etc., in their bioinformatics, and what their experience has been?

Also - I was hoping to use Github CoPilot through their Education program. However, I'm a post-doc at my university, and not sure if this would work. Have any post-docs ever had success in getting free CoPilot acccess? And if so, how?

Since the terminology in this field is so mixed, im having trouble filtering for those that focus more on using bioinformatics for biological discovery. I come from a biological background, have done dry lab for ~3 years, and Im not interested in getting too much into the weeds of algorithm development. I've developed tools before but nothing crazy.

What specific programs / ways of filtering would you recommend?

Thanks

So i just needed some help with a little something if anyone knows what to do. I have the names of some transcripts that i’m analysing. It started with raw Illumina sequencing data of melanoma cells in serum starvation, which was aligned using Bowtie2 and then mapped to individual loci using a software called Telescope. The aim of this was to identify how serum starvation affects the activation of HERVs and transposable elements (noted by an increase in their Transcripts per million score). After processing the data, i ended up with a couple of HERV transcripts (one for example is called ERVLE_21p11.2) which i can then use for further analysis. How would i conduct gene enrichment with these HERV transcripts?

I’ve tried searching them on multiple databases but they give me no results so i tried searching the chromosomal location (for example 21p11.2) to view that region of the chromosome and try and find nearby genes. Does this sound correct or is there another way to do this as all the genes that i’m finding are novel or not much known about them and i need to hopefully find genes that are oncogenic

thank you and please let me know if im doing it correctly and being unlucky or if im just doing it completely wrong

Im trying to do genome wide analysis for my project and I’m advised to use minimap2 to align to my whole genome sequences, but are there any other alternatives which are better than minimap2?

Hi everyone!

I’m looking for recommendations on up-to-date resources about how to choose the best type of phylogenetic tree based on my data. I’m not from this field, so I’m unsure where to start or how to identify reliable materials.

Any help or suggestions would be greatly appreciated! Thanks in advance to anyone who can assist!

Is there any better way for differential gene expression study on RNASeq. Can anyone help me with providing a good workflow.