High-Throughput, High-Precision Colony Phenotyping with Pyphe

Kamrad, Stephan; Bähler, Jürg; Ralser, Markus

doi:10.1007/978-1-0716-2257-5_21

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Finally, colonies from the source plates were transferred onto the assay agarose plates bearing different concentrations of metal salts using the ‘Replicate Many’ program of the Singer ROTOR HD TM with the following settings: recycle = Yes, revisit = Yes, source_pressure = 40%, source_pin_speed = 15 mm/s, source_overshoot = 1.5 mm, target_pressure = 25%, target_pin_speed = 13mm/s, target_overshoot = 1.2 mm, target_max = No, source_mix = No. After 2 days of incubation at 30°C, all plates were scanned on an Epson V800 PHOTO scanner in grey and transmission scanning mode at 600 dpi using pyphe 82 .…”

Section: Methodsmentioning

confidence: 99%

“…Images of agarose plates from the Epson V800 PHOTO scanner were processed using the gitter R library to extract colony size. The .dat output from gitter 101 were combined with the experiment design table and analysed further ( grid normalisation, data aggregation, quality control checks and statistical analysis to obtain effect sizes and P values) using pyphe 58,82 . Only 1% of the negative control positions (footprints) (49 out of 4875 empty spots in total across all agarose plates) were contaminated and no systematic contamination was observed.…”

Section: Analysis Of Growth Screen Of Knock-out Deletion Mutantsmentioning

confidence: 99%

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The molecular landscape of cellular metal ion biology

Aulakh,

Lemke,

Szyrwiel

et al. 2024

Preprint

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Metal ions play crucial roles in cells, yet the broader impact of metal availability on biological networks remains underexplored. We generated genome-wide resources, systematically quantifying yeast cell growth, metallomic, proteomic, and genetic responses upon varying each of its essential metal ions (Ca, Cu, Fe, K, Mg, Mn, Mo, Na, Zn), over several orders of magnitude. We find that metal ions deeply impact cellular networks, with 57.6% of the proteome, including most signalling pathways, responding. While the biological response to each metal is distinct, our data reveals common properties of metal responsiveness, such as concentration interdependencies and metal homeostasis. We describe a compendium of metal-dependent cellular processes and reveal that several understudied genes can be functionally annotated based on their metal responses. Furthermore, we report that metalloenzymes occupy central nodes in the metabolic network and are more likely to be encoded by isozymes, resulting in system-wide responsiveness to metal availability.

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Section: Methodsmentioning

confidence: 99%

Section: Analysis Of Growth Screen Of Knock-out Deletion Mutantsmentioning

confidence: 99%

The molecular landscape of cellular metal ion biology

Aulakh,

Lemke,

Szyrwiel

et al. 2024

Preprint

Self Cite

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“…Plates were scanned and processed using pyphe ( Kamrad et al, 2020 ; Kamrad et al, 2022 ). After processing with grid correction, median fitness and corrected (Benjamini-Hochberg) p-value thresholds (5% median fitness difference with respect to wt and adjusted p-value <0.05) were used to determine phenotypes in benign conditions (YES and EMM at 32 °C).…”

Section: Methodsmentioning

confidence: 99%

Broad functional profiling of fission yeast proteins using phenomics and machine learning

López

Bordin

Lees

et al. 2023

eLife

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Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for all 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 new proteins affected the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1,675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular ageing, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

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“…Plates were scanned and processed using pyphe (58,88). After processing with grid correction, median fitness and corrected (Benjamini-Hochberg) p-value thresholds (5% median fitness difference with respect to wt and adjusted p-value <0.05) were used to determine phenotypes in benign conditions (YES and EMM at 32°C).…”

Section: Colony-based Phenotyping Of Deletion-mutant Librarymentioning

confidence: 99%

Broad functional profiling of fission yeast proteins using phenomics and machine learning

López

Bordin

Lees

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches withSchizosaccharomycespombefor broad cues on protein functions.We assayed colony-growth phenotypes to measure the fitness of deletion mutants for all 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 new proteins affected the resistance to oxidative stress.Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1,675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular ageing, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

show abstract

High-Throughput, High-Precision Colony Phenotyping with Pyphe

Cited by 8 publications

References 18 publications

The molecular landscape of cellular metal ion biology

The molecular landscape of cellular metal ion biology

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Broad functional profiling of fission yeast proteins using phenomics and machine learning

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