All that plots like this are telling you is that there are some genes that contribute more to the variability seen between your various samples than others. In an ideal world these genes will also be differentially expressed between your groups, but since we don't live in an ideal world that might not be the case. In general, the longer the line and the ...
There is a conda package for maxquant that should work on most platforms (I've used it on CentOS and I think it's been used on Ubuntu as well). There's also a docker container version of that that we (Bioconda) have posted to the biocontainers project that should also work.
Please note that large parts of a maxquant analysis are single threaded, regardless ...
As explained in the website the data introduced must be:
Format must be FASTA, Clustal, plain string, or a valid UniProtKB AC
So you need to paste just the protein sequence. Without seeing the data you paste I would guess that you don't use a valid plain string.
As Jonathan Moore explains below a FASTA format is:
The dendrogram summarize the information of a group of values and sort them according to the similarity they have. It can be applied to both, samples and features.
The dendrogram allows to visualize features that are more similar together, usually revealing patterns that wouldn't have been seen otherwise.
In an article usually it is used something like: "...
TMT stands for tandem-mass-tag. The idea is to tag different samples with one of the tags then pool all samples together and run them through the mass spectrometer together. Then the reporter groups are split off and quantified (ideally) for each peptide individually. There are different methods with different numbers of isobaric labels. Isobaric means all ...
These folders are usually generated in a default directory (where the raw files are). It can however be beneficial to set the paths to these folders manually.
that is obviously where temporary files go, so having an SSD or other fast drive will benefit the I/O operations. Set that path to a fast storage partition.
I have been running MaxQuant quite frequently on Linux. I would use conda to create a dedicated environment and install mono.
# create the environment
conda create -n maxquant -c conda-forge mono
# activate the environment
conda activate maxquant
# run any maxquant version
mono /path/to/maxuant/MaxQuantCmd.exe mqpar.xml
This installs a conda environment ...
You can do this with the reutils package, which provides an API to NCBI's E-utilities. Here's an example for your specific question:
# Get universal identifier
uid <- esearch("NP_000029", db = "gene")
# Fetch summary
sm <- esummary(uid, db = "gene")
# Extract specific ...
The kind of normalization depends on what you what to explore. So, there is no absolute answer to this. Different normalizations highlight different aspects of your dataset. There is a nice paper for a higher throughput quantitative study that I would recommend reading:
Nusinow, David P., John Szpyt, Mahmoud Ghandi, Christopher M. Rose, E. Robert McDonald ...
This is one of the major problems with genomic information in todays research. This was highlighted some years ago with the police using publicly available genomic data bases to idenitfy unkown murder suspects.
The full extent of this issue was exemplified in a
paper by Yaniv Erlich from 2018 that should be a good starting point for you.
They claim that ...
Your title says holistic. This is a tad problematic as there's layers upon layers. Say, post-translation regulation, inhibiting metabolites, interacting protein etc. That is why when talking of an enzyme inhibitor, at the biochemical level one speaks in terms of k_i (inhibition constant), while at the cellular level ("holistic") one talks of IC50.
Interesting application. I would suggest using Python's Flask system, which is described in a few pages in O'Reilly's Programming PyTorch, chapter 8. PyTorch in Production. Django is not really recommended (don't say that on Stackoverflow however). O'Reilly do an entire book on Flask, but thats to make the site pretty.
Obviously, the explanation here is ...
PPK provides a great answer, but for question 2 I can provide a different perspective. For shotgun metagenome sequencing (without any enrichment/depletion protocols) it is common for >90% of reads to map to the human genome. There is variation across body sites, for example the gut microbiome is rich and so the % of human reads from a stool sample will be ...
You could try download-refseq-genomes. It will fetch all genomes from the NCBI FTP server that are in a specific subtree of the phylogeny.
For example, downloading the amino acid sequences from all proteins in each genome assembly for all species belonging to Chloroflexi (NCBI taxon ID = 200795):
download-refseq-genomes.pl -t faa 200795
This will ...
The developers answered along these lines:
This might be a normalization artefact. Reporter is normalizing the ratios so that the median is 1. So, if the label is almost not present the noise gets increased. To check that you can turn of the normalization. This function is currently not documented. The developers are working on it though.
This looks like a TMT proteomics experiment. What tools/pipeline did you use to arrive at your quantitative values?
There are a variety of tools that are useful for analyzing this type of data and supply appropriate imputation methods. If you are comfortable programming in R, one I would suggest is the MSstatsTMT R package: http://msstats.org/msstatstmt/
40% missing data is huge. Missing data analysis is complicated on the underlying distribution. If the data set is periodic then missing data periodicity is needed. Non-periodic data can be solved using regression, I think SciPy has an automated method for this, but the volume of missing data makes this approach complex. If you can bring the missing data down ...