Questions related to Bioinformatics
What are good resources for an undergraduate student to start getting familiar with bioinformatics and, if possible, get some practical experience? Any favorite websites, blogs, videos, etc?
Hello. We understand that a volcano plot is a graphical representation of differential values (proteins or genes), and it requires two parameters: fold change and p-value. However, for IP-MS (immunoprecipitation-mass spectrometry) data, there are many proteins identified in the IP (immunoprecipitation group) with their intensity, but these proteins are not detected in the IgG (control group)(the data is blank). This means that we cannot calculate the p-value and fold change for these "present(IP) --- absent(IgG)" proteins, and therefore, we cannot plot them on a volcano plot. However, in many articles, we see that these proteins are successfully plotted on a volcano plot. How did they accomplish this? Are there any data fitting methods available to assist in drawing? need imputation? but is it reflect the real interaction degree?
Hi, I am working on protein-protein interaction studies, specifically on antibody-antigen interaction. I would like to observe the changes in interaction if there's mutation occurs in the protein. Could anyone suggest a tool that can be used to induce substitution mutation to a targeted amino acid of a 3D protein and tools to validate that the mutation is not a nonsense mutation that produces truncated protein?
I want to find the UTR sequence of mRNA sequence of bacteria protein. Can anyone suggest a insilico process for that
I'm on the lookout for remote bioinformatics and computational biology opportunities where I can actively contribute to research projects. Compensation is not a priority for me; my main focus is to gain hands-on experience in these fields.
I am beginner in "Bioinformatics" and want to learn " how to analyse bacterial and fungal genomic data?". Would you suggest me some materials and sources so that I can devleop myself?
Note: My interest is now on " Bacterial and fungal genome and proteome analysis by using bioinformatics"
I am trying to construct phylogenetic tree of HIV-1. I downloaded sequences from few neighbor countries from Los Alamos HIV database. After aligning and trimming the length of sequences is usually 722 nucleotides. I can't trim less, because there are a lot of gaps within alignment file. When I construct Maximum Liklehood tree in FastTree or PhyML, the branches look very short. What could be a possible reason for it?
If 722 nucleotides length sequences can be used for constructing reliable phylogenetic tree?
I am beginner in "Bioinformatics" and want to learn " how to analyse bacterial and fungal genomic data?". Would you suggest me some materials and sources so that I can devleop myself?
Note: My interest is now on " Bacterial and fungal genome and proteome analysis by using bioinformatics"
Hello fellow researchers,
I wanted to start a discussion on the exciting topic of the future of bioinformatics and its evolution. Bioinformatics has come a long way in recent years, but there are undoubtedly new frontiers to explore and challenges to overcome. What are your thoughts on the current trends, emerging technologies, and the potential impact of bioinformatics in the years to come? I'm eager to hear your insights and predictions on the future of this rapidly evolving field.
I am reaching out to #researchers in the field of #Biochemistry, #Biophysics and #Bioinformatics, for collaborative partnership in scientific research. The researcher should be academic staff at the tertiary institutions in following listed countries:
#Democratic People's Republic of Korea
#Democratic Republic of the Congo
#Lao People's Democratic Republic
#Micronesia (Federated States of)
#Moldova (Republic of)
#SaintVincent and the #Grenadines
#SaoTome and #Principe
#Syrian Arab Republic
#Tanzania (United Republic of)
Interested researcher should kindly email to email@example.com with the subject: Research Collaboration from "your country".
Toluwase H. Fatoki
Visionary @ Heze-Sapience International, Nigeria.
Lecturer @ Department of Biochemistry, Federal University Oye-Ekiti, Nigeria.
Our lab have a bioinformatics project about developing a functional enrichment software. We have several ideas but we realize we need real feedback from wet lab researchers as well to make sure our functional enrichment web application will be reliable and useful for all of you.
Therefore, if you are a wet lab scientist who have experience using functional enrichment software (such as Metascape, DAVID, etc), what kind of questions do you want to address in the functional enrichment result? Are there any information that they are still unable to give to you?
I already know the pathway but want to know the upstream lncRNAs that regulates that pathway using the datasets and bioinformatics.
In their website they mentioned it's IF is 5.8. But in the JIF2022 report, I did not find. Is it because of its inclusion in the Emerging Source Citation Index? and because of not included in the "Science Citation Index Expanded" Please help.
From where can I get valid IF. One more thing, this journal is not included in BioxBio, have checked.
Could someone explain to me why the p-value in the right column of the forest plot is different than the p-value in the test for effect in the subgroup?
I thought that these two p.values should be the same.
Hello, I've recently been studying Ancestral Sequence Reconstruction (ASR), attempting to infer ancestral sequences of viruses. I understand that this inference is constrained by factors like sample size and models, and represents a plausible sequence that may have existed. However, I'm curious about whether directly comparing these inferred ancestral sequences holds biological significance. Can they reflect the differences among the extant sequences from various lineages that were used to infer them?
Ph.D. full-time position in Bangalore with fellowship:
Eligibility: M.Sc. Chemistry/Biochemistry/Biotechnology/Microbiology/Bioinformatics with first class of 60%.
GATE or UGC-NET or UGC-CSIR or SLET or JRF should be qualified.
RS 25,000 per month for full three years will be given.
For further details, contact me on: +919182864256. Call or what's app me for further details.
I am trying to analyse mutation data for endometrial cancer obtained from different studies within several databases (COSMIC, cBioportal, Intogen). I have collated the data and grouped the mutations by gene. The focus of the analysis are non-synonymous coding mutations - because these mutations are most likely to cause a change in the normal protein function.
The aim of the study is to understand the mutational landscape of Endometrial cancer. The main objectives of the study are to find the commonly mutated genes in endometrial cancer, to find significantly damaging gene mutations in endometrial cancer and to create an updated list of genes comparable to commercial gene panels.
I have created this table with the collated data:
- Gene name
- Number of samples with coding mutations
- Frequency ( number of samples with coding mutations / total number of samples with coding mutation)
- CDS length
- Total number of unique coding mutations
- Number of unique coding: synonymous mutations
- Number of unique coding: non-synonymous mutations
- Mutation burden (number of unique coding: non-synonymoys mutations / CDS length)
- Composite score [(frequency of samples * 0.7) + (mutation burden * 0.3)]
The idea here is to use mutation burden to imply damaging effects of the genes' mutations in endometrial cancer. We then created a composite score to use as a comparable figure between the genes.
At the moment, our list of genes is at 16,000+. We are currently trying to think of a way to narrow down the list of genes to only focus on those significantly mutated compared to the other genes by way of statistics. Any advice is greatly appreciated.
We had sent some phytoplankton samples for sequencing. And we had just received the generated sequences, and the next step was to do BLAST to identify what the phytoplankton that we sent is. Basically DNA Barcoding.
To give some context, when we send our samples for sequencing to the sequencing facility, they send us back two files, one for the forward sequence and another for the reverse sequence, based on the primers (forward and reverse) we gave.
So, the initial step involves us checking the quality of the sequences, specifically looking for any signs of low quality, ambiguity, or overlapping signals in the chromatograph.
Now, I'm a bit uncertain about the next steps.
The following step would be sequence trimming. To do this, I need to identify the start of each sequence by locating the primer sequence. This means finding the forward primer sequence in the generated forward sequence and doing the same for the reverse primer in the reverse sequence.
Afterward, I perform reverse complementation on the reverse sequence.
Following that, I conduct a pairwise alignment between the generated forward and reverse sequences and subsequently generate the consensus sequence.
My questions are, as I am a bit stumped with this (I apologize in advance, I'm a bit new with bioinformatics), (1) what if neither of the generated sequences have the primer sequences? Would that mean the sequences generated were of bad/low quality? and (2) Is this approach correct, or have I missed a crucial step?
I have extensively searched google scholar but I am struggling to find any groups who have previously used Rosetta to conduct ab-initio structure modelling of single-pass or membrane anchored proteins and I'm specifically not talking about homology modelling just ab-initio.
Please let me know if you have read any papers or know anyone who has done this,
2nd year PhD student at University of Liverpool.
I'm working on the finer details of my experimental design, and have some questions regarding bridging channels for TMT based experiments.
I have two conditions to test, across nine biological replicates, in order to run as one 18-plex TMT-pro experiment.
I am aware of the use of one or more bridging channels being used with pooled samples to combine multiple TMT mixtures, however a colleague has mentioned that a bridging channel should also be considered for normalisation if only one set is used.
Does anyone have any experience using a bridging channel for normalisation in a single mixture? Is it worth sacrificing one or more biological replicates for?
I will be using MSstatsTMT for normalisation and summarisation.
I'm searching for reliable bioinformatics/immunoinformatics tools for predicting the immunogenicity of B-Cell Epitopes. Your expertise is invaluable! Could you kindly recommend any devices that have proven effective in this area? Your insights will significantly contribute to advancing our understanding of immunogenicity prediction.
Thank you in advance for your suggestions!
Are you familiar with Research4Life? It's a program that provides free or low-cost access to scientific research in low-income countries. Research4Life has two eligibility lists: Group A and Group B. Group A includes countries with the lowest gross domestic product, lowest human development index, and other factors that indicate lower-income countries. As an immunoinformatics, Bioinformatics and Molecular Modelling researcher, I'm calling on researchers from Research4Life's Group A countries to join me in collaborative research efforts. By working together and utilizing the program's valuable resources, we can advance our research and make a difference in the world. Best of all, with this collaboration, it will be completely free. #Research4Life #immunoinformatics #bioinformatics #molecularmodelling #collaboration
Hello everyone; I am new to R programming. I want to calculate the firmicutes to Bacteroides ratio from my OTU table. I couldn't find the command and don't know how to do it. Please guide me on this.
I put an example of my OTU table.
I measured the distance between two centers of mass during a MD run using gmx distance. Even though the -oall file shows me that the distance changed over time the histogram file -oh puts 100% of probability on the last bin.
As this makes no sense does anyone have an idea on what happened?
Both files are attached
Thank you very much in advance and have a nice day!
I have been trying to dock a certain protein with nd ion i downloaded from rcsb but after i add it to pyrx and try to convert it to ligand i get the following error. I tried converting the sdf file to pdb using pymol, chimeraX, avogadro, open babel but even then when i open the file it gives me this error: ligand: :UNK0:Nd and ligand: :UNK0:Nd have the same coordinates. Could someone please help?
Update: I want to dock an unbound protein with the neodymium metal ion which i downloaded from rcsb in sdf format and later tried to convert it to pdb using the aforementioned softwares for autodock to accept it but i can't get it to be accepted by autodock as a proper ligand. Apparently I am unable to get any of the rare earth elements to be accepted properly as ligands.
I know many websites have simple tools like transcription and translation available, but are there any analysis tools that researchers need that either do not exist or are not publicly available? It could be anything from algorithms to visuals. Thanks!
I am very new to bioinformatics and biological data , please bare with my question.
I have differential expression data of three, Parental cellines(drug sensitive ) and 10 isoforms (made resistant to the drug) by these three parental cells.
Is the data enough to generate a coexpression network.?
I Have tried constructing it using GWENA , and was also successful but I am not confident about it because of two reasons one number of samples and second can isoforms be treated as samples or not.
I would really appreciate any suggestions and anr reading resource that can be helpful in this regard.
In recent years, number of vaccine have been approved to fight against Covid-19, list of approved is available at FDA site. We are looking for sequence of these vaccine (RNA sequence in case of mRNA vaccines and amino acid sequence in case of protein based vaccines. I will highly appreciate help of community in searching sequence of vaccines.
I have recently isolated a new E.coli phage and during the assessment of its host range, I discovered that this particular phage was effective against Pseudomonas aureginosa and staphylococcus aureus in wet lab experiments. However, upon examining the complete genome of the phage on NCBI, I noticed that it did not exhibit any similarities with known P. aureuginosa and S. aureus phages. Additionally, when I performed a blastp analysis on all the phage proteins in NCBI, I could not identify any homology with the aforementioned P. aureuginosa and S. aureus phages. Normally, I would expect to observe some degree of homology, especially in proteins responsible for recognition, such as tail proteins or lytic proteins.
My question is how I can determine the wide host range of the phage based on its genome. It appears that bioinformatic tools should provide information regarding the extent of the phage's host range. I would greatly appreciate your comments and recommendations on this matter.
I want to annotate each gene in the Homo sapiens taxon with its respective GO terms and its hierarchical parent terms in the GO database. How can I systematically do that? While I am aware that the obo file contains information such as "is a," "part of," and "regulates," it lacks a comprehensive hierarchy from child GO terms to all their parent terms. Is there an existing method available to achieve this systematic annotation, or do I need to develop a custom script to extract this information from the obo file?
Dear ResearchGate Community,
I am currently engaged in single-cell analysis for my research project and would greatly appreciate your insights and experiences regarding the use of Seurat and ScanPy.
I have been exploring both Seurat and ScanPy as tools for analyzing single-cell RNA sequencing (scRNA-seq) data. However, I would like to gather more information about these packages directly from researchers who have bioinformatic hands-on experience with them.
Specifically, I would be grateful if you could share your thoughts on the following:
1. Which package (Seurat or ScanPy) have you used for scRNA-seq analysis, and what were your primary reasons for choosing it? Is it depending on familiarity with programming languages (R for Seurat and Python for Scanpy)?
2. What are the notable features, strengths, or advantages of the packages you have worked with?
3. Were there any challenges or limitations you encountered while using the packages, and how did you address them?
4. Have you encountered any specific use cases or applications where one platform outperformed the other?
5. Are there any particular resources, tutorials, or best practices you found helpful when working with Seurat or ScanPy?
Your firsthand experiences and insights would be immensely valuable in helping me make an informed decision about which package to choose and understanding potential considerations for my single-cell analysis workflows.
Thank you in advance for taking the time to share your expertise. I look forward to hearing from you and benefiting from your valuable insights.
Emil Lagumdzic Institute of Immunology Department of Pathobiology
University of Veterinary Medicine Vienna
Is the hierarchical structure observed in the Gene Ontology (GO) OBO-basic file limited to the 'is a' relationship, or do the relationships 'has part' and 'regulates' also exhibit a similar hierarchical nature and can be propagated to the root?
I am looking for data from mammals ideally, but I will take anything to be honest. I am getting to grips with bioinformatics and need a practice data set with which I can go through the steps of filtering and trimming and mapping to a reference genome etc..
If anyone also has any advice on tools used subsequently for analysis such as MethylKit that would be awesome.
I prefer to join 2 drug molecules (cocktail) using bioinformatics approach. Are there any tools available for it? Any software available where one can submit the individual structure of the drug molecules and receive the merged drug molecules?
I have a protein sequence with two cysteine residues and I would like to predict if those cysteins will form disulfide bonds.
I am looking for user-friendly tools to do this, either online tools or some other kind of easy to use software, since I am not well-versed in bioinformatics.
I'm comparing the arrangement of a gene complex across different species to try and find clues about its evolutionary history. In some cases genes appear to have jumped around and switched positions, but I do not know if this is the result of recombination, or due to the orientation in which the chromosome has been assembled?
I'm taking data from the NCBI genome browser using ref seq chromosome level assemblies in each case. Does anyone know if there a standard direction that homologous chromosomes have to be uploaded in?
I imagine this is perfectly possible to do if you consider the positions of conserved genes at each end of the chromosome, but I would rather not have to do this myself if I know that it has already been accounted for...
If I have a sequence (genome.fasta). And I want to check the gene located in 400nt -500nt.
What bash script (I have WSL in my windows) I should use or are there any conda packages ?
Thank you in advanced
Is there any server or tools (bioconda, java, etc.) to exclusively annotate membrane protein only (similar to dbCAN for polysaccharides) from a bacterial genome?
Thank you in advanced!
Hi - I'm currently working with two RNA-Seq studies; one has RNA extracted from whole blood, the other PBMCs. Eventually we want to combine these data and perform some cell-specific deconvolution to look at DEGs.
Are there any recommended methods for batch correcting these data from different sources?
I am interested in predicting the protein structure of my protein of interest. Using NCBI BLAST, I found an experimental structure that corresponds to a domain of my protein, showing 24% query coverage and 100% similarity. My question is whether I can confidently use this experimental structure as a template for homology modeling, or if I should explore alternative techniques such as threading, ab initio modeling, or any other suitable approach. I would also appreciate recommendations for relevant servers or software that can assist in this case.
Thank you for your insights and suggestions.
What to do if ChimeraX software doesn't recognise the .chimerax file downloaded from SwissDock after docking?
Besides, the zip file of prediction done was empty.
I have an issue that drives me crazy this evening...
I have a list of gene vectors, downregulated in different transgenic plants and I want to make a Venn diagram to visualize it and to show the intersections between plants.
But! The results from any package I used (in R) gaves me something like this (the uploaded picture 1)...
What's bothering me:
1. The numbers on "clear" (not intersected) parts of a diagram are lower, than the gensets I have. And I tried to use factor instead of character vectors, to remove possible duplications, to remove symbols (like space) that could cause software misunderstanding - all gaves me nothing... same result.
2. The intersection of vectors is not true - on the picture you can see that the intersection of 2 datasets (of 365 and 154 genes) - is 1133 genes!! How could that be?
The manual usage of intersect function on the same dataset gaves pretty correct results.
Maybe I am misunderstanding about Venn diagrams? Because in a web I found many examples of such strange mistakes - on the second picture from Datanovia you can see that the intersection of the red elliplse (of 58) and yellow (of 144) is 66!
It seemes logical to me that the intersection of 2 vectors cannot be greater than the length of a smaller vector. What am I doing wrong or misunderstanding?
I am not good at R so I am trying to find solutions for my problems through the internet. I have been stuck on a problem. I couldn't find a way to compare the means of groups separated by facet function. Maybe I should not have put x axis as it is now but I wanna make sure. Here is the shorter version of my code for you to have a look at:
my_comparisons <- list( c("Hybrid","Single"))
ggplot(data = rpkms_new2, aes(x = strand, y = log2(RPKM), fill=strand, label = strand))+
geom_violin(scale = "count", alpha=0.5)+
facet_grid(~Trans, switch = "x", scales = "free_x", space = "free_x") +
theme(plot.title = element_text(hjust=0.5))+
theme(panel.spacing = unit(0, "lines"),
strip.background = element_blank(),
strip.placement = "outside") +
stat_compare_means(ref.group = "None", aes(label = ..p.signif..), method = "wilcox")+
stat_compare_means(comparisons = my_comparisons, aes(label = ..p.signif..), method = "wilcox")+
geom_text(data = mean_ranks, aes(x = strand, y = -Inf, label = round(rank, 0)), size = 3, vjust = -1)
How should I modify my code to be able to compare all the subgroups(single and hybrid) with the "None" group ?
My data looks like below:
STRAND TRANS VALUES:
sense hybrid 2
sense hybrid 2
sense single 3
sense single 7
antisense hybrid 10
antisense hybrid 12
antisense single 1
antisense single 2
none none 1
none none 4
I am currently an Indonesian high school student passionate about bioinformatics and its potential to drive impactful innovations in the fields of biology and medicine. I am eager to participate in the Regeneron International Science and Engineering Fair and showcase a research project that can make a significant contribution to the scientific community.
Considering the vast possibilities within the realm of bioinformatics, I would greatly appreciate any suggestions, ideas, or insights for a research project that aligns with the following criteria:
- Impactful Innovation: I am looking for a research topic that has the potential to make a significant impact in the biology or medical world. It could involve the development of new algorithms, computational tools, or methodologies that address critical challenges in these domains.
- Bioinformatics Focus: The research should predominantly involve bioinformatics techniques, such as data analysis, data mining, machine learning, genomics, proteomics, or other computational approaches. It should leverage the power of data and computational tools to gain insights into biological processes or contribute to medical advancements.
- Feasibility for a High School Student: As a high school student, I have certain limitations in terms of resources, time, and expertise. Therefore, I am seeking research ideas that are feasible for a high school-level project. While the topic should be challenging enough to meet the standards of the Regeneron ISEF, it should also be manageable within the scope of a high school research project.
Thank you in advance for your valuable suggestions and insights.
Hello everybody, I'm a master degree student. I'm working with 16S data on some environmental samples. After all the cleaning, denoising ecc... now I have an object that stores my sequences, their taxonomic classification, and a table of counts of ASV per sample linked to their taxonomic classification.
The question is, what should I do with the counts for assessing Diversity metrics? Should I transform them prior to the calculation of indexes, or i should transform them according to the index/distance i want to assess? Where can I find some resources linked to these problems and related other for study that out?
I know that these questions may be very simple ones, but I'm lost.
As far as I know there is no consensus on the statistical operation of transforming the data, but i cannot leave raw because of the compositionality of the datum.
I'm interested in studying specific missense mutations in a human gene. My goal is to determine whether the mutated region of the protein is conserved across various species. Could you please guide me on how I can use in silico tools to find homologous protein sequences and identify their conserved regions?
Thank you very much
Hi, I am a beginner in bioinformatics and I would like to identify CRISPRs in my MAGs fasta files. Can someone recommend an up-to-date good tool that can be easily installed through the Conda environment, please? Thank You in advance
If anyone is interested in reviewing manuscript on multiepitope vaccine design. Please provide your following details:
Note: Reviewers from India, Pakistan, Egypt & Saudi Arabia are not eligible for this manuscript.
Institutional E-mail id:
Can an MD simulation be performed by adding other salts by varying their concentration inside the box?
"The result shows absence of intragenomic variation among 16S rDNA gene and presence of variable regions among the 16S rDNA sequences (intergenomic variation), noticing for example high variability around 800, 900, and 1000 bp and a large conserved region between 1150 and 1350 bp. This information allowed us to discard the restriction enzymes FnuII, AsuI, FokI, Eco57I that recognized some restriction sites contained within variable regions, since they are more susceptible of acquiring future nucleotidic variations and with this, the potential generation of different band patterns." 
I add that the article mentioned that these discarded enzymes were targeting conserved sites in the study species.
Mandakovic D, Glasner B, Maldonado J, Aravena P, González M, Cambiazo V, Pulgar R. Genomic-Based Restriction Enzyme Selection for Specific Detection of Piscirickettsia salmonis by 16S rDNA PCR-RFLP. Front Microbiol. 2016 May 9;7:643. doi: 10.3389/fmicb.2016.00643. PMID: 27242682; PMCID: PMC4860512.
Is my reading right that the article implies that there is such potential? If yes, what are the possible mechanisms?
More important, what's the time frame of this "future nucleotidic variation", is it an evolutionary time frame that could take thousands of years?
Edit: i think my question can be thought of as: How common are new 16s rRNA gene variants in bacterial species?
Dear Friends and connection
I believe in the power of community. So, I post this,
I am excited to explore the possibility of collaborating with someone who works on network pharmacology. As, network pharmacology is an interdisciplinary field that combines principles of network analysis, bioinformatics, and pharmacology to investigate drug-target interactions and predict the therapeutic effects of drugs.
I have some projects related to bioinformatics and I believe that our collaboration can result in significant progress in this exciting field.
I am looking forward to hearing from you and exploring our collaboration for network pharmacology.
I've recently been using the NCI's Cancer Genome Atlas to find datasets and perform basic clinical correlation analyses. I think it's a fantastic tool, even for people with a limited bioinformatics background, so it made me curious if there are similar resources for people who study non-cancer diseases.
I was wondering if people are aware of any other databases/repositories/webtools that serve a similar purpose for non-cancer diseases. If anyone has recommendations/suggestions, please comment/link them down below.
Thanks in advance for your input!
"Is there any in-silico methods for studying the effect of up-regulation and down-regulation of the same genes?"
If yes, please suggest me the name/article.....Thank you
What bioinformatics tools are available to help analyze and interpret large-scale molecular data generated from crop research?
We all know that nanobody development is time and money consuming, it nearly needs a grant. I'm wondering if there is any bioinformatics tool or a method to predict nanobody sequence against certain antigen using this antigen sequence as an input ? Something like you put in the antigen sequence and that tool could predict how the nanobody against this antigen could be, in term of sequence, structure, etc?
I am running an MD simulation on a protein-protein complex.
After seeing a similar question on research gate, I checked the amino acids rtp file in my force fields folder, and as expected from this error, the HD1 atom was not present in the HSE entry. The atom HD2 is however present in that entry. So I figured replacing the HD1 atoms in my PDB file with HD2 should solve the error.
And it did. For the time being.
To reaffirm, I made changes in Histidine's hydrogen atoms in the PDB file. When I went ahead with the energy minimization step, I got an error that said there's an Infinite Force on an atom. It turns out that the atom was "HD2" of some Histidine in the PDB file.
I saw online that the reason behind this error was due to atom overlap. Hence, just for seeing if that was the case for me, I changed the coordinates of that atom a little bit (this was just for checking, I can't do this for the actual work). When I ran the EM step again, I got the same error, but for a HD2 of a different Histidine molecule. So yes, overlapping of the atoms is the reason for this particular error. I cannot solve it by changing coordinates of all the HD2 atoms of the Histidines. So it all boils down to the main fatal error that I mentioned.
How do I approach this?
1. Changing the atom name (as in HD1 -> HD2 is not working due to the subsequent error)
2. I do not know if I should add the atom HD1 in the HSE entry in the rtp file (I tried this and got several warnings).
3. I cannot (or should I?) use -ignh because mine is not an NMR structure. I have modelled my proteins on Modeller and refined them online.
Any suggestions/solutions will help me a lot. Thank you in advance!
I've been trying to know more about bioinformatics pipelines for whole genome shotgun sequencing data to use for the samples of animal fecal microbes diversity and identify pathogenic microorganisms (both of DNA and RNA).
I have tried to separate a direct coculture of MSCs (mesenchymal stromal cells) and macrophages to do bulk RNA seq on macrophages, as I want to find out how MSCs change the genetic expression on macrophages. I have tried different methods to separate the coculture as much possible, but I can only manage to retrieve a cell population with 95% macrophages, and 5% MSCs still present.
Therefore, I want to know if anyone has experience with analyzing data when the population is not completely pure with one cell type and how do I handle such data?
Is it wise to proceed with bulk RNA seq when 5% of my cells are still MSCs, well aware that the expressed genes observed could come from the 5% MSCs?
Risk of bias assessment (sometimes called "quality assessment" or "critical appraisal") helps to establish transparency of evidence synthesis results and findings. and it is mandatory to have it in your systematic review!
if you know any tools or used ones, can you please share it/them with me?
or if you have extra information regarding the risk of basis assessments, can you share it with me?
Hello every body. I need HELP. PLEASE
in basecalling fast5 files, using genomicpariscentre/guppy image in docker, I try many flowcell and kit names which are available in guppy(I print them using this code: guppy_basecaller --print_workflows) but I recieve this error: could not find matching workflow for flowcell XXX and kit XXX.
What can I do to fix it?