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Category: Science

New publication

New publication

Our publication “Validated quantitative trait loci for eggshell quality in experimental and commercial laying hens” has been published as short communication in Animal Genetics.

Summary

Compromised eggshell quality causes considerable economic losses for the egg industry. Breeding for improved eggshell quality has been very challenging. Eggshell quality is a trait that would greatly benefit from marker‐assisted selection, which would allow the selection of sires for their direct contribution to the trait and would also allow implementation of measurements integrating a number of shell parameters that are difficult to measure. In this study, we selected the most promising autosomal quantitative trait loci (QTL) affecting eggshell quality on chromosomes 2, 3, 6 and 14 from earlier experiments and we extended the F2 population to include 1599 F2 females. The study was repeated on two commercial populations: Lohmann Tierzucht Rhode Island Red line (n = 692 females) and a Hy‐Line White Plymouth Rock line (n = 290 progeny tested males). We analyzed the selected autosomal QTL regions on the three populations with SNP markers at 4–13 SNPs/Mb density. QTL for eggshell quality were replicated on all studied regions in the F2 population. New QTL were detected for eggshell color on chromosomes 3 and 6. Marker associations with eggshell quality traits were validated in the tested commercial lines on chromosomes 2, 3 and 6, thus paving the way for marker‐assisted selection for improved eggshell quality.

New Bovine Genome Comparison

New Bovine Genome Comparison

Since 2014 the standard for genome studies in bovine was the UMD 3.1 genome, e.g. for download here:

 

However, a few days ago a new assembly was release, called ARS_UCD1.2.This assembly can be downloaded here:

ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/002/263/795/GCA_002263795.2_ARS-UCD1.2/

Just to get a quick impression, I compared both assemblies and checked for their similarities using LAST http://last.cbrc.jp/ .

First, I created a LAST database like this

lastdb -P0 -uNEAR -R01 $FOLDER/UMD31/UMD31-NEAR $FOLDER/UMD31/UMD3.1_chromosomes.fa

Then, I determined the substitution and gap frequencies

last-train -P0 --revsym --matsym --gapsym -E0.05 -C2 $FOLDER/UMD31/UMD31-NEAR $FOLDER/ARS/ARS_UCD12.fna > $FOLDER/UMD-ARS.mat

After the training, the blasting was performed (here is the parallel part of the slurm script)

FOLDER="/wrk/daniel/References/";
chr=($(ls $FOLDER/ARS/chr*));

lastal -m50 -E0.05 -C2 -p $FOLDER/UMD-ARS.mat $FOLDER/UMD31/UMD31-NEAR ${chr[$SLURM_ARRAY_TASK_ID]} | last-split -m1 > UMD-ARS-$SLURM_ARRAY_TASK_ID.maf

As I ran the blasting parallel for each chromosome, the header of the files needed to be removed

cat *.maf > all.maf
sed '/^#/ d' < all.maf > temp.maf
head -n 22 all.maf > headerLines
cat headerLines temp.maf > alignments.maf

Finally, the merged simple-sequence alignments were discarded, the alignments were converted to tabular format, and alignments with error probability > 10^-5 were discarded:

last-postmask alignments.maf |
maf-convert -n tab |
awk -F'=' '$2 <= 1e-5' > alignments.tab

And for that tab file was then the dotplot created

last-dotplot -x 4000 -y 4000 alignment.tab alignment.png

This is how the dotplot looks like, it seems pretty much the same genome, but has in some areas clearly changed it! (Open it and zoom to the diagonal to see the differences)

 

For the steps, I followed the tutorial here: https://github.com/mcfrith/last-genome-alignments

 

Another update of bitools

Another update of bitools

During the last two weeks, I updated the bitools container twice. Two new tools were added to it:

1. velvet

An easy to apply de-novo assembler that I use for metagenome studies

2. Bandage

A tool to visualize the graphs that are provided from velvet

Great book on statistical inference

Great book on statistical inference

During the last days I noticed a really nice book that gives an updated respective a refreshment on statistical inference. It is ‘Computer Age Statistical Inference: Algorithms, Evidenve and Data Science’ by Bradley Efron and Trevor Hastie

On the webpage of Trevor Hastie is even a download link to the pdf of the book, I can highly recommend it!

Screenshot of web.stanford.edu
New version of the bitools docker container

New version of the bitools docker container

I updated the docker container that I maintain (bitools) to keep all the bioinformatics tools together that I recently use. Yesterday I added the tool FEELnc to it, a tool to detect lncRNAs from RNA-seq data.

UPDATE: Apparently there was an issue with the Forkmanages perl module in the docker container, I fixed that on 7.12.2017 and udated the docker image v0.1.6 on Docker Hub.