The end of the long winter tunnel

The end of the long winter tunnel

It seems we finally reached the end of winter! Today we have almost 20 degrees, we can sit outside and have an afternoon Espresso. I managed to submit a grant application and now a few free days during Mayday are ahead, while the weather forecast promises plain, blue sky with 15 and more degrees. That is truly something I am looking forward to now! Certainly there are still small tasks here and there, but the main job will be to get the garden ready for summer, maybe even pitching the pool already ūüôā

Introducing the Fluidigm R- Package

Introducing the Fluidigm R- Package

Our Fluidigm R-package was just released on Cran. The package is designed to streamline the process of analyzing genotyping data from Fluidigm machines. It offers a suite of tools for data handling and analysis, making it easier for researchers to work with their data. Here are the key functions provided by the package:

  1. fluidigm2PLINK(...): Converts Fluidigm data to the format used by PLINK, creating a ped/map-file pair from the CSV output received from the Fluidigm machine.
  2. estimateErrors(...): Estimates errors in the genotyping data.
  3. calculatePairwiseSimilarities(...): Calculates pairwise similarities between samples.
  4. getPairwiseSimilarityLoci(...): Determines pairwise similarity loci.
  5. similarityMatrix(...): Generates a similarity matrix.

Users can choose to run these functions individually or execute them all at once using the convenient fluidigmAnalysisWrapper(...) wrapper function.

Finding the Closest Variants to Specific Genomic Locations

Finding the Closest Variants to Specific Genomic Locations

In the field of genomics, we often need to find the closest variants (e.g., SNPs, indels) to a set of genomic locations of interest. This task can be accomplished using various bioinformatics tools such as bedtools. In this blog post, we will walk through a step-by-step guide on how to achieve this.

Prerequisites

Before we start, make sure you have the following files:

  1. A BED file with your locations of interest. In this example, we’ll use locations_of_interest.bed
  2. A VCF file with your variants. In this example, we’ll use FinalSetVariants_referenceGenome.vcf

Step 1: Sorting the VCF File

The first issue we encountered was that the VCF file was not sorted lexicographically. bedtools requires the input files to be sorted in this manner. We can sort the VCF file using the following command:

(grep '^#' FinalSetVariants_referenceGenome.vcf; grep -v '^#' FinalSetVariants_referenceGenome.vcf | sort -k1,1 -k2,2n) > sorted_FinalSetVariants_referenceGenome.vcf

This command separates the header lines (those starting with #) from the data lines, sorts the data lines, and then concatenates the header and sorted data into a new file sorted_FinalSetVariants_referenceGenome.vcf.

Step 2: Converting VCF to BED and Finding the Closest Variants

The next step is to find the closest variants to our locations of interest. However, by default, bedtools closest outputs the entire VCF entry, which might be more information than we need. To limit the output, we can convert the VCF file to a BED format on-the-fly and assign an additional feature, the marker name, as chr_bpLocation (which is the convention we use for naming our markers). We can also add the -d option to get the distance between the location of interest and the closest variant. Here is the command:

awk 'BEGIN {OFS="\t"} {if (!/^#/) {print $1,$2-1,$2,$4"/"$5,"+",$1"_"$2}}' sorted_FinalSetVariants_referenceGenome.vcf | bedtools closest -a locations_of_interest.bed -b stdin -d

This command uses awk to read the VCF data, convert it to BED format, and write the result to the standard output. The pipe (|) then feeds this output directly into bedtools closest as the -b file. The keyword stdin is used to tell bedtools to read from the standard input.

Conclusion

With these two steps, we can efficiently find the closest variants to a set of genomic locations of interest. This approach is flexible and can be adapted to different datasets and requirements.

New research published

New research published

Have you ever wondered how the cells in the cow’s udder react to bacterial infection? In our latest study, we used a novel method to investigate the gene expression of primary bovine mammary epithelial cells (pbMECs) after challenge with Escherichia coli, a common cause of mastitis in dairy cattle. Mastitis is a costly and painful disease that affects both animal welfare and milk production. By understanding the molecular mechanisms of the immune response, we hope to find new ways to prevent and treat mastitis, and to breed cows that are more resistant to infection.

We extracted pbMECs from fresh milk samples and cultured them in the laboratory. Then we exposed them to heat-inactivated E. coli and measured the changes in gene expression at 3 and 24 hours post-challenge using RNA sequencing. We found that E. coli challenge induced a strong inflammatory response in the pbMECs, involving genes and pathways related to cytokine signaling, toll-like receptor signaling, and antigen presentation. We also identified some novel candidate genes that have not been previously reported to be associated with mastitis in cattle.

Our study demonstrates that milk-derived pbMECs can be used as a non-invasive, in vitro model to study mastitis resistance in cattle. This approach has several advantages over traditional methods, such as avoiding animal suffering, reducing costs, and enabling high-throughput screening. Our results provide new insights into the molecular basis of the udder immune response and suggest potential targets for future interventions. To learn more, you can read our full paper here: Iso-Touru et al. Veterinary Research (2024) 55:13.