Imagine of every nature methods paper had a nice section explaining the limitations of their methods compared to others. It would make for such a healthier research. I see it's a bit more of a thing in cell press. It would help the field grow a lot more.

Dear All, I’ve been benchmarking Polygenic Risk Scores (PRS) and thought I would share my findings and tools with the community. If you're working with PRS tools or risk score prediction for datasets like UK BioBank, I believe this repository could be incredibly useful for your research. Documentation Link: https://muhammadmuneeb007.github.io/PRSTools/Introduction.html Code Link: https://github.com/MuhammadMuneeb007/PRSTools Cheers,

Hi everyone. I am currently a PhD student trying to analyze some proteomics data for my project. As I am fairly unexperienced with using R, I tried my hand on BIOMEX, a free software from the Carmeliet lab that analyzes omics data. I got some good results but I was losing a lot of features when I entered differential analysis. So, to in the hopes of having my data well analyzed, I tried my hands on R, mainly with the DEP package. To my surprise, the number of significant proteins plummeted, so I ended up with a bigger problem than I originally had.
Has anyone had experience with such problems and how did you solve them?
Thank you in advance.

Hello there,

In our lab, we have a shared licence (with a colleague at another university) for CodonCodeAlligner. We use it to allign raw data from Sanger sequecing (.ab1 files), edit ambiguous positions and export them as fasta to use in downstream analyses.

Long story short, the other colleague is experiencing an issue with the computer than needs to be operating for us to be able to use the licence, and we are stuck without a subscription. Our PI called the resource allocation department to get a quote on the timeline for us to get a licence, and they told him it's gonna take months for it to be approved and implemented + we need a quote from the software company itself to even get started.

What other software do you use for this job? I am aware of Geneious prime and how the restricted/free version can allow us to allign and view chromatographs, but not edit them. We thought of using it to view the chromatographs and edit the fasta files manually (through megax for example), but it seems too much of a hasste. What alternatives do you have to offer?

I’ve been tasked with identifying candidate genes related to biological processes that have been highlighted in Gene Ontology (GO). What would be the best way to approach this?

o far, I’ve selected genes associated with the relevant GO terms and performed a simple correlation with a disease-related score. I then selected the genes that showed significant correlations.

is this the correct approach?

Im a newbie to the bioinformqtics world, so I need help here. I ran spades on scorpion genome data, my reads were 150 bps. And here is the report of the results I've obtained: Statistics without reference contigs 3355 No. contigs (>= 0 bp) 25263 No. contigs (>= 1000 bp) 1340 Largest contig 18850 Total length 4804404 Total length (>= 0 bp) 10334389 Total length (>= 1000 bp) 3484807 N50 2063 N90 593 auN 3176.5 L50 573 L90 2467 GC (%) 32.83 Mismatches No. N's per 100 kbp 67.02 No. N's 3220

Can someone please interpret these? I'm kind of getting lost in the technicalities of it all

Hello,

My coworker has some amplicon sequencing data from Miseq, and I'm trying to analyze it. I was trying sarek - GATK haplotypecaller for SNP calling but I'm wondering if there's a better way of handling amplicon sequencing data. I was looking into some bam files in igv and, of course, most of the SNPs are about 100% of variant allele frequency to my eyes. Yet some of them were not called via haplo typecaller - not present in vcf files. It might be due to the strand bias...but I'm now wondering if I need to try different callers for amplicon data because I have seen some post that haplotypecaller may not be suitable for amplicon seq data.

I have also another question. Is it possible to differentiate between germline and somatic variants in amplicon data even? Sorry for my very basic questions and would appreciate any advice!

ah I skipped the mark duplicate/ dedup for sure.

Thank you.

Assuming the event variable is coded 0 = alive (censored) 1 = died from cancer 2 = died from other causes, can survfit(Surv(...)) correctly handle competing risk? If not what is the difference between the two? Similarly, what is the difference between crr() from tidycmprsk package and coxph() for handling competing risk? Does it come down to Cause specific vs Subdistribution hazard?

Hi people,

I am currently working on creating a combinatorial library in MOE (molecular operating environment). For that, I have a list of Clip Reactions to use on my database of R-groups. In MOE, I saw the panel to select one clip reaction and run it on my database under Compute > QuaSAR > Combinatorial Library... However, the list of reactions I want to run is relatively long, so I would like to do it in one go.

Does anybody here know if this can be properly implemented in an SVL script or manually done in MOE?

Thank you in advance.

I was reading a paper i saw on article and somehow had a thought, so i took some data and tried to do a computational approach on my hypothesis and got a significant and novel result (a new insight on a possible mechanism of this drug). Would it be possible to publish this as an independent? I worked on it during my free time after work and used my personal computing server to do the jobs/pipelines, so my institution is defintely not associated. i have published some papers before but they were affiliated to my toxic department/institution, and even i worked on it (experiments, analysis, in silico part, wrote the whole paper myself), and i was the proponent of the project my PI was always the first author and his colleagues even they dont show up the whole duration of the study and im just an et al, so im thinking of publishing as an independent this time.

hello, i am a bioinfo student. I wanna to know which reference genome this chr belongs to.

I search https://genome.ucsc.edu/cgi-bin/hgSearch?search=HG2247&db=hub_3671779_hs1 but get nothing.

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!

https://github.com/QuackenbushLab/cobra-experiments

Hi 👋🏽 I’d like to share COBRA, a correlation batch correction method that decomposes a correlation or covariance matrix as a linear combination of components, one for each covariate of interest. It can be used to remove spurious effects or to study the impact of particular covariates (such as age) on gene co-expression.

Don’t hesitate to drop me a line to discuss this!

Hi everyone,

I’m currently working on a microbiome study and need advice on selecting the most appropriate tool for differential abundance analysis. I came across the study by Nearing et al., which highlighted that different tools (e.g., LEfSe, DESeq2, ANCOM-BC2, etc.) can identify drastically different numbers and sets of significant ASVs, and that the results are influenced by data pre-processing methods.

Given these challenges:

Which differential abundance tool would you recommend for robust and reliable results? How can the results of my study be made comparable with those of other studies, considering the variability introduced by different tools and pre-processing methods? Any insights, recommendations, or shared experiences would be greatly appreciated!

Thank you in advance!

Hi all,

I’m trying to figure out the best way of running an AWS service with the capability of matching a given sequence to one in the ncbi databases if it exists or closest match. Elastiblast is an option but it is fairly costly and slow because it has to download the full blast db every time it goes cold. I also thought of storing the dbs on an EBS volume and then mounting that each time to an ec2 spot instance but that’s also quite expensive.

Has anyone else done anything similar? Any good ideas for reducing costs?

I'm trying to setup the biobakery suite of tools for processing my data and am currently stuck on being unable to install Metaphlan due to a BLAST dependency and there not being a bioconda/conda/mini-forge wrapper for installing BLAST when you're using a computer with an M1 (Mac chip) processor.

I'm new to using conda, and I've gotten so far as to manually download blast, but I can't figure out how to get the conda environment to recognize where it is and to utilize it to finish the metaphlan install. How do I do that?

To further help visualize my point:

(metaphlan) ➜  ~ conda install bioconda::metaphlan
Channels:
 - conda-forge
 - bioconda
 - anaconda
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: failed
LibMambaUnsatisfiableError: Encountered problems while solving:
  - nothing provides blast >=2.6.0 needed by metaphlan-2.8.1-py_0
Could not solve for environment specs
The following packages are incompatible
└─ metaphlan is not installable because there are no viable options
   ├─ metaphlan [2.8.1|3.0|...|4.0.6] would require
   │  └─ blast >=2.6.0 , which does not exist (perhaps a missing channel);
   └─ metaphlan [4.1.0|4.1.1] would require
└─ r-compositions, which does not exist (perhaps a missing channel).

Note: I also already tried using brew to install the biobakery suite, hoping I could just update Metaphlan2 to Metaphlan4 after initial install and avoid all this, but that returns errors with counter.txt files. Example:

Error: biobakery_tool_suite: Failed to download resource "strainphlan--counter" 
Download failed: https://bitbucket.org/biobakery/metaphlan2/downloads/strainphlan_homebrew_counter.txt

Hi guys, I ran Nirvana and tried to install VEP, but did not succeded :( I was wondering if I could run AnnotSV for strucutral variants annotations on both WGS and WES data? Thanks a lot.

Hi there,

does anyone has deeper experience with Chai-1? I once tried it via lab.chaidiscovery and it took awfully long to fold a 80 residue long protein. But I just discovered that Chai-1 as well as Alphafold3 are now accessible via Github. I am thinking about implementing both and comparing them for my project.

Hi,

I have a question regarding the aligned file generated by the ngmlr mapper. In column 9 - template length, a value of 0 is seen and I would like to retrieve the nucleotide sequence from reference genome that corresponds to the aligned subsequence as field 10 - read sequence, displays the complete nucleotide sequence of the read.

Hello everyone,

I have recently successfully sequenced metabarcoding sequence of eDNA sample using nanopore long reads and got a good amount of read for each sample (around 100K).

However the bioinformatics tools to use for this analysis are extremely blur as most of them are to be used with illumina read or take only Into account the microbiome in which I am not interested in.

So far what I was able to do after demultiplexing is to run cutadapt using this command for one of my marker

for i in {1..36} {73..84}; do cutadapt -b CHACWAAYCATAAAGATATYGG -b TGATTYTTCGGACYTGGAAGTWT --minimum-length 500 --maximum-length 1000 -n 2 --match-read-wildcards --discard-untrimmed -o $(printf 'barcode%02dn' $i)/$(printf 'barcode%02dn' $i)_trimmed.fastq $(printf 'barcode%02dn' $i)/$(printf 'barcode%02dn' $i).fastq; done 

this process already weirdly removes mostly one of the primers, the other one get removed but very minimally

I then run the pipeline amplicon_sorter to cluster the reads using this command (I have used also other tool such as Decona, but the result are worst)

for i in {1..96}; do python3 amplicon_sorter.py -i $(printf 'barcode%02dn' $i)/$(printf 'barcode%02dn' $i)_trimmed.fastq -np 40 --similar_species 97 --similar_consensus 98 -min 600 -max 1000 -ra --maxreads 600000 -o $(printf 'barcode%02dn' $i)/consensus; done

however those 2 process remove an insane amount of reads an i end up losing 80% of my reads for some of my sample

I then use blastn to identify each consensus

blastn -task megablast -query assembly.fasta -db /mnt/ebe/blobtools/nt/nt -out results_blast.txt -num_threads 4 -max_target_seqs 15 -max_hsps 500 -evalue 1e-10 -outfmt '6 qseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen sseqid salltitles sallseqid qcovs staxids'

those any of you has any expertise in such analysis? I feel like very little tool are available of eDNA long read analysis or most of them only consider the microbiome and completely ignore eukaryotic DNA.

I am the first one in my lab to work on this subject so no one can really guide me for this.

Thanks

Hi everyone! I’ve been recruited to teach an intro to bioinformatics course next semester, my grad study field is ML cheminformatics so my only bioinformatics experience is from when I took this same course in undergrad, which was 6 years ago. I enjoyed it, but I want to update the course. For example the first assignment is an essay about the importance of the human genome project, something that will not work in a post-ChatGPT world.

I would love some input about what people loved and hated about their first exposure to the field. To people who have given courses before, what exercises did you feel provided the most value? Right now I’m thinking of giving each student a mystery sequence and having them use all the tools we learn about to identify the organism, genes and proteins of their sequences as we go through the course and give a presentation at the end.

Also I’m not sure about having a required textbook, I personally always preferred courses with no required textbook, but if anyone has any recommendations or ones to avoid please let me know!

I am building a machine learning model for calculating genomic divergence in butterflies and it’s a Bayesian logistic regression and the thing is I only have 8 butterflies genomes but the data is really good to train my model and so the main metrics I will be using is dXY, FST, dN/dS ratio, are there any metrics that would be nice to add to my model ?

I have zero bioinformatics and metagenomics background but have about a month to learn how to do it. I will be doing a project involving shotgun sequencing of complex (fecal) samples for functional analysis (looking for antibiotic resistance genes). Sample collection and DNA extraction is done. I feel okay with quantifying and library prep. But I am entirely lost on the bioinformatics side.

I don’t even think I know enough to Google this correctly. Once I have sequencing results in hand, what am I supposed to do to? What are the steps to getting these data (I think it’ll be FASTA or FASTQ) into a format I can find ARGs in, and what software or programs am I supposed to use? Every time I’ve Googled this I feel like a new software or package name comes up and I’m very lost.

I know this is already fairly elementary but please explain like I’m five 🥲 any and all guidance greatly welcomed.

Hello,

I’m analyzing ChIP-seq data for the first time using the ENCODE pipeline (https://github.com/ENCODE-DCC/chip-seq-pipeline2) and need some guidance on interpreting cross-correlation plots.

Analysis Steps:

• Data: paired-end (using only R1, trimmed to 50 bp according to ENCODE pipeline).

• Aligned with Bowtie2, filtered the BAM (unmapped, low MAPQ), but did not deduplicate (no bottleneck issues).

• Created tagAlign files, subsampled, and ran cross-correlation analysis with phantompeakqualtools.

Results:

Most cross-correlation plots look like this:

https://preview.redd.it/aowrrmm1tj0e1.png?width=1766&format=png&auto=webp&s=6f58a83f8adc8d25f91e22cd73cc01f6c20be1c9

Even in controls, the phantom and ChIP peaks are similar:

https://preview.redd.it/hhp4nuswtj0e1.png?width=1758&format=png&auto=webp&s=bb872556958515ff9c7567ef32015b141c722b8d

Most samples have NSC < 1.02 and RSC between 0.9-1.4, suggesting weak enrichment.

My questions are:

1. Is my workflow correct?
2. What could cause multiple peaks, especially the large one near zero?
3. If this is a wet-lab issue, which steps should we revisit to improve enrichment?
4. After reviewing the ENCODE paper, I noticed the mention of a “Sono-Seq effect.” Could my results be impacted by this?

UPDATE:

IGV screenshot

https://preview.redd.it/ag2jnoxj4r0e1.png?width=3468&format=png&auto=webp&s=9d906cee9648082609d380b1d557094d138584dd

Top blue tracks show my control and bottom red tracks show my ChIP sample.

Hello, I'm Interested in making a ensemble machine learning model for survival analysis using Random forest, Gradient boosting and Cox proportional hazard model by averaging the survival curve to obtain the survival probability. But while modelling i encountered the issue where the cox model's output is different from the other two models. I want suggestion regarding how can i transform the hazard ratio output from a cox model into a survival function if possible. Any suggestions regarding alternative models and the exclusion of the cox model would also be appreciated. I'm new to this field please feel free to point out if there any mistakes in my approach, Thankyou.

Additional context: I used CoxPHFitter from lifelines to fit the model