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Sztuka, M.

Publications and source records attributed to Sztuka, M..

2 recordsLinked to original sources

Supplementation with effective microorganisms in earthen ponds affects common carp growth but not overall microbial communities

Probiotics are increasingly explored in aquaculture to enhance fish health and growth without leaving harmful residues. However, their efficacy in real-world pond environments remains poorly understood. Here, we conducted a 103-day field experiment to assess the effects of two effective microorganisms commercial products supplementations on microbial communities and growth performance of common carp (Cyprinus carpio). Effective microorganisms were added both to feed and directly to pond water. Microbial diversity was analysed using 16S rRNA and whole-genome shotgun sequencing across three environments - water (three time points), sediment (two time points) and fish intestine (one time point) - from 25 experimental ponds. Bioinformatics processing involved QIIME2 and MG-TK pipeline with taxonomic classification based on the SILVA database. The results showed that although supplemented bacterial families did not establish significantly in pond environments, fish exposed to specific effective microorganisms treatments exhibited improved growth metrics. These findings suggest that effective microorganisms can enhance carp growth in aquaculture without significantly altering resident microbial communities, offering a promising residue-free alternative to traditional additives.

microbiology↗

Nextflow vs. plain Bash: Different Approaches to the Parallelisation of SNP Calling from the Whole Genome Sequence Data

This study compared computational approaches to parallelisation of an SNP calling workflow. Data comprised DNA from five Holstein-Friesian cows sequenced with the Illumina platform. The pipeline consisted of quality control, alignment to the reference genome, post-alignment, and SNP calling. Three approaches to parallelisation were compared: (i) a plain Bash script in which a pipeline for each cow was executed as separate processes invoked at the same time, (ii) a Bash script wrapped in a single Nextflow process, and (iii) a Nextflow script with each component of the pipeline defined as a separate process. The results demonstrated that on average, the multi-process Nextflow script performed 15% to 27% faster depending on the number of assigned threads, with the biggest execution time advantage over the plain Bash approach observed with 10 threads. In terms of RAM usage, the most substantial variation was observed for the multi-process Nextflow, for which it increased with the number of assigned threads, while RAM consumption of the other setups did not depend much on the numbers of threads assigned for computations. Due to intermediate and log files generated, disk usage was markedly higher for the multi-process Nextflow than for the plain Bash and for the single-process Nextflow.

bioinformatics↗