Genomics | Final Year Project Repository | Machine Learning library

I followed the tutorial A Primer on Deep Learning in Genomics - Public.ipynb in colab but got TypeError: Cannot convert a symbolic Keras input/output to a numpy array... as I tried to execute the step 4.Interpret at line sal = compute_salient_bases(model, input_features[sequence_index]).

I am trying to crosscheck a large body of data with a specific website(https://icis.corp.delaware.gov/Ecorp/EntitySearch/NameSearch.aspx). I am just in the beginning stage and quite new to the whole VBA process. The goal later on is to search for many company names based on a larger list in excel and get their founding dates. But for now I am starting out with just a single name to get it running, I am having trouble in my main code as there is no inherent input value in the HTML code:

I have watched a few youtube tutorials, but I can't seem to find one for exactly what I would like to do so thought I would post on here!

I have an excel document with the results of a genomics experiment (I am looking for which genes are present or absent in certain bacterial groups). I have 29 columns and they each belong to one of four distinct groups. The information below each column is either filled in with a particular unique code if the gene is present or left blank if it is absent, but each code is a mixture of letters and numbers and is unique to each column. So, I would like to set the conditional formatting based on the cells being filled in or blank. I would like to make the cell green if the cell is filled in (meaning the gene is present) between all four groups, red if it is only present in one of the groups and then something like yellow if it shared between Group 1 (data in columns O-Y) and 2 (Z-AI), orange if between 1 (O-Y) and 3 (AJ-AM), dark orange if between 2 (Z-AI) and 3 (AJ-AM) and left white if it is shared between any of the groups and group 4 (AN-AQ).

Unsure if it is possible or if the above makes sense but would appreciate any tips/tutorial links/help! The first image is of the four groups and as you can see they are all filled in because all the groups share these genes Then we start to see some gaps as the genes are not shared between all the groups anymore, the slight issue is that not all of the members in the group will have all of the same genes but even if one of the members has it, I would need it to be conditionally formatted according to the rules Sorry, I couldn't copy over the table as text from the website you suggested, but hope these screenshots are useful!

This is where I am up to, the different colours are there but not in the right cells as some of the blank cells have been coloured

I would like to able to do something equivalent to this using dbplyr.

I have two tables:

1. An assoc.logistic file from PLINK (https://www.cog-genomics.org/plink/1.9/formats#assoc_linear) which I have edited to have the columns using awk (just printing different columns). The number/letters in the SNP column refer to the CHROM/POS/REF/ALT columns in table 2.

   ...

I need to extract the journal titles from a bibliography list. The titles are all within quotation marks. So is there a way to ask R to extract all text that is within parenthesis?

I have read the list into R as a text file:

"data <- readLines("Publications _ CCDM.txt")"

here are a few lines from the list:

Andronis, C.E., Hane, J., Bringans, S., Hardy, G., Jacques, S., Lipscombe, R., Tan, K-C. (2020). “Gene validation and remodelling using proteogenomics of Phytophthora cinnamomi, the causal agent of Dieback.” bioRxiv. DOI: https://doi.org/10.1101/2020.10.25.354530 Beccari, G., Prodi, A., Senatore, M.T., Balmas, V,. Tini, F., Onofri, A., Pedini, L., Sulyok, M,. Brocca, L., Covarelli, L. (2020). “Cultivation Area Affects the Presence of Fungal Communities and Secondary Metabolites in Italian Durum Wheat Grains.” Toxins https://www.mdpi.com/2072-6651/12/2/97 Corsi, B., Percvial-Alwyn, L., Downie, R.C., Venturini, L., Iagallo, E.M., Campos Mantello, C., McCormick-Barnes, C., See, P.T., Oliver, R.P., Moffat, C.S., Cockram, J. “Genetic analysis of wheat sensitivity to the ToxB fungal effector from Pyrenophora tritici-repentis, the causal agent of tan spot” Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-019-03517-8 Derbyshire, M.C., (2020) Bioinformatic Detection of Positive Selection Pressure in Plant Pathogens: The Neutral Theory of Molecular Sequence Evolution in Action. (2020) Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2020.00644 Dodhia, K.N., Cox, B.A., Oliver, R.P., Lopez-Ruiz, F.J. (2020). “When time really is money: in situ quantification of the strobilurin resistance mutation G143A in the wheat pathogen Blumeria graminis f. sp. tritici.” bioRxiv, doi: https://doi.org/10.1101/2020.08.20.258921 Graham-Taylor, C., Kamphuis, L.G., Derbyshire, M.C. (2020). “A detailed in silico analysis of secondary metabolite biosynthesis clusters in the genome of the broad host range plant pathogenic fungus Sclerotinia sclerotiorum.” BMC Genomics https://doi.org/10.1186/s12864-019-6424-4

I have a pandas dataframe that I have also written to file. I have also created a schema for the data in json format. I have this stored as a python dictionary, and also written to file.

I've tried uploading using to_gpq and using the command line, and in both instances, I get an error about having a repeated field, the same field.

This is info about the data:

code

The count-min sketch is a probabilistic data structure for lossy storage of counts in a multiset. It receives updates (i, c) where i is an element of a set and c is a non-negative quantity for that element, then does clever things with hash functions. It is widely discussed on SO and elsewhere; here is the original paper (PDF) and the Wikipedia article. Based on the application I am considering it for -- lossy storage of count data from single-cell genomics experiments -- let's assume i and c are both integers. The pair i,c means that in a given biological cell, gene i was detected c times.

My question is about how much memory the count-min sketch takes compared to sparse matrix formats more commonly used for this type of data. For a simple example of an alternative, consider a hash table -- say, a Python dictionary -- storing each distinct value of c with the sum of the corresponding values of i. If n distinct genes are observed in a given cell, then this takes O(n) space. This answer explains that, to store counts of n distinct genes, the count-min sketch also takes O(n) space. (Identifiers for the genes are stored separately as an array of strings.)

I don't understand why anyone would introduce so much complexity for what seems to be no improvement in compression. I also don't understand what's special about this application that would render the count-min sketch useless when it's useful for lots of other purposes. So:

• For this application, does the count-min sketch save space over typical sparse matrix storage schemes?
• Is there any application for which the count-min sketch saves space over typical sparse matrix storage schemes? If so, what is the key difference from this application?

I'm creating a Snakemake workflow that will wrap up some of the tools in the nvidia clara parabricks pipelines. Because these tools run on GPU's, they typically can only handle one sample at a time, otherwise the GPU will run out of memory. However, Snakemake shoves all the samples through to Parabricks at one time - seemingly unaware of the GPU memory limits. One solution would be to tell Snakemake to process one sample at a time, thus the question:

How do I get Snakemake to process one sample at a time?

Because parabricks is a licensed product (and therefore not necessarily reproducible), I will show an example of the parabricks rule I am trying to run (pbrun fastq2bam), as well as a minimal reproducible example using open source software (fastqc) which we can work on/from

Snakefile: