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Genetic Fitness

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Most cited protocols related to «Genetic Fitness»

1
Quantifying Strain and Gene Fitness
BarSeq reads were converted to a table of the number of times that each bar code was seen in each sample using a custom perl script (MultiCodes.pl). The script requires an exact match to the 8 nucleotides at the beginning of the read that identify the sample (“inline” indexes), or relies on Illumina software for demultiplexing (TruSeq P7 indexes), depending on the primer design (see “BarSeq” above). The script also requires an exact match for the 9 nucleotides upstream of the bar code. We did not check the quality scores for the bar code or the sequence downstream of the bar code (the -minQuality 0 option). However, bar codes that do not match exactly an expected bar code are ignored in later stages of the analysis.
Given a table of bar codes, where they map in the genome, and their counts in each sample, we estimate strain fitness and gene fitness values and their reliability with a custom R script (FEBA.R). Roughly, strain fitness is the normalized log2 ratio of counts between the treatment sample (i.e., after growth in a certain medium) and the reference “time-zero” sample. Gene fitness is the weighted average of the strain fitness, and a t score is computed based on the consistency of the strain fitness values for each gene. Ideally, the time-zero and treatment samples are sequenced in the same lane. Also, we usually have multiple replicates of any given time zero, with independent extraction of genomic DNA and independent PCR with a different index. We sum the per-strain counts across replicate time-zero samples.
Wetmore K.M., Price M.N., Waters R.J., Lamson J.S., He J., Hoover C.A., Blow M.J., Bristow J., Butland G., Arkin A.P, & Deutschbauer A. (2015). Rapid Quantification of Mutant Fitness in Diverse Bacteria by Sequencing Randomly Bar-Coded Transposons. mBio, 6(3), e00306-15.
Publication 2015
Culture Media DNA Replication Genes Genetic Fitness Genome Nucleotides Oligonucleotide Primers Strains
2
Leveraging Protein Fold Recognition for Disorder Prediction
Apart from disorder predictors, many other bioinformatics tools yield implicit or explicit information about order and disorder. In the course of a variety of other protein sequence analysis projects, we realized that there is a clear correlation between the disorder in the target protein sequence, and the presence of gaps in alignments to structurally characterized templates calculated by the protein fold-recognition methods. Although the implementation of a method utilizing this type of information may seem trivial, it was not so straightforward to deal with different types of fold recognition methods. In other words, it was not so obvious which method should be used or, if many methods were used, how to rank them. Additionally, a template-matching method should be able to take into account the fact that matches to homologous proteins have different reliability and in some cases homologous sequences cannot be found. To address all these questions, we compared the results from arbitrary chosen fold recognition methods that were relatively fast and performed well in the framework of CASP: HHSEARCH, FFAS, mGenThreader, PSI-BLAST, PHYRE, and PCONS5 (see Methods for details and references). To optimize the weights assigned to individual methods depending on the alignment quality we used a genetic algorithm implemented in Pyevolve [47 (link)]. The fitness function of the genetic algorithm was designed as a one-dimensional vector of length 24 (8 methods mentioned above multiplied by 3 thresholds for well-, moderately- and poorly-scored templates; see Table 4 for details of the thresholds used). In this way, the weights for all methods were obtained, for the further incorporation into a combined template-matching method. The resulting predictor was tested in CASP9 as a group number 421 (GSmetaDisorder3D).
Kozlowski L.P, & Bujnicki J.M. (2012). MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics, 13, 111.
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Publication 2012
Amino Acid Sequence CASP9 protein, human Cloning Vectors Genetic Fitness Homologous Sequences Nonesterified Fatty Acids Proteins Sequence Analysis, Protein
3
Comprehensive Cancer Cell Line Essentiality
Gene essentiality data derived from CRISPR-Cas9 genome-scale loss-of-function screening of 739 cancer cell lines using a modified Avana library (Doench et al, 2016 (link)) as part of Project Achilles was obtained from the Broad Institute’s DepMap portal (20q1 release; https://figshare.com/articles/DepMap_20Q1_Public/11791698/3). CERES scores (Meyers et al, 2017 (link)) were used to quantify the fitness effect of individual gene loss, with “essentiality” in this article represented as the CERES score multiplied by −1. For example, a highly essential gene might have a CERES fitness effect of −2 and thus an essentiality score of two. The standard CERES gene effect estimate from the Broad’s DepMap portal has undergone several bias adjustments, including removal of confounding principal components, as described (Meyers et al, 2017 (link); Boyle et al, 2018 (link); Dempster et al, 2019 (link)
Preprint). RNAi gene essentiality data from 712 cancer cell lines, encompassing three independent RNAi screening projects (McFarland et al, 2018 (link)), were obtained from (https://figshare.com/articles/DEMETER2_data/6025238/6). Processed RNA-seq, reverse phase protein array, copy number, and metabolomic data were obtained from the DepMap data portal (https://depmap.org/portal/download/). These data are described in detail in Ghandi et al (2019) (link). Cancer cell line drug sensitivity data from the PRISM drug repurposing project are obtainable from https://figshare.com/articles/PRISM_Repurposing_19Q3_Primary_Screen/9393293 (Corsello et al, 2020 (link)). Genome positions and chromosomal band annotations for individual genes were obtained from BioMart (Smedley et al, 2009 (link)). Duplicate gene family data was downloaded from the Duplicated Gene Database (http://dgd.genouest.org/listRegion/homo_sapiens/all%3A0.x/5/) (Ouedraogo et al, 2012 (link)). STRING experimental interactions were obtained from https://string-db.org/cgi/download.pl (Szklarczyk et al, 2019 (link)). mRNA co-expression data from COXPRESdb (Obayashi et al, 2019 (link)) were downloaded from https://figshare.com/files/10975364. GSEA gene sets were obtained from http://software.broadinstitute.org/gsea/msigdb (Liberzon et al, 2015 (link)). CORUM core protein complex member data were obtained from http://mips.helmholtz-muenchen.de/corum/#download (Giurgiu et al, 2019 (link)). Drug-gene interaction data was obtained from the Drug-Gene Interaction DataBase at http://www.dgidb.org/data/interactions.tsv (Cotto et al, 2018 (link)).
Amici D.R., Jackson J.M., Truica M.I., Smith R.S., Abdulkadir S.A, & Mendillo M.L. (2020). FIREWORKS: a bottom-up approach to integrative coessentiality network analysis. Life Science Alliance, 4(2), e202000882.
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Publication 2020
Cancer Screening Cell Lines CERE Chromosome Banding Clustered Regularly Interspaced Short Palindromic Repeats DNA Library Drug Interactions Gene, Cancer Gene Annotation Genes Genes, Duplicate Genes, Essential Genetic Fitness Genome Homo sapiens Hypersensitivity Macrophage Inflammatory Protein-1 Malignant Neoplasms prisma Protein Arrays Proteins RNA, Messenger RNA-Seq RNA Interference Tendon, Achilles
4
Transposon library construction and screening
Library construction was done as described (van Opijnen et al. 2009 (link); van Opijnen and Camilli 2010 ). Note that the magellan6 minitransposon we designed lacks transcriptional terminators, therefore allowing for read-through transcription, which explains why no relevant polar effects were observed by examining fitness of downstream genes (Supplemental Table S1). Additionally, the minitransposon contains stop codons in all three frames in either orientation when inserted into a coding sequence. In vitro selection experiments were done with six independently generated libraries each with a size of ∼8000 transposon insertion mutants covering 88% of nonessential genes. Growth conditions where the carbon source was varied consisted of semi-defined minimal media (SDMM) at pH 7.3 supplemented with 10 mM of one of the following carbon sources: glucose, fructose, mannose, galactose, N-acetylglucosamine (GlcNac), sialic acid, sucrose, maltose, cellobiose, or raffinose. Stress conditions consisted of SDMM with 10 mM glucose at pH 7.3 and one of the following stresses: Metal stress, 0.5 mM of 2,2′-Bipyridyl (Sigma-Aldrich); DNA damage, Methyl methanesulfonate 0.015% (MMS, Fluka); hydrogen peroxide exposure, H2O2 4.5 mM (Sigma-Aldrich); acidic pH stress, pH6; temperature stress, growth at 30°C; antibiotic exposure, norfloxacin 1.5 μg/mL (Sigma-Aldrich); and DNA transformation.
Nasopharynx colonization experiments were done in 17 mice with eight independently generated libraries each with a size of ∼4000 mutants, while lung infection experiments were done in 20 mice with six libraries each with a size of ∼30,000 mutants. Because of differences in the bacterial load, 105–106 colony forming units (cfu) for nasopharynx and 107–108 cfu for lung, smaller libraries were used for the nasopharynx in order to minimize the stochastic loss of mutants. Mice were euthanized after 24 h for lung infection, followed by removal and homogenization of the lungs, and 48 h for nasopharynx colonization, followed by flushing of the nasopharynx with 500 μL of PBS.
van Opijnen T, & Camilli A. (2012). A fine scale phenotype–genotype virulence map of a bacterial pathogen. Genome Research, 22(12), 2541-2551.
Publication 2012
Acetylglucosamine Acids Antibiotics Bipyridyl Carbon Carbon-10 Cellobiose Codon, Terminator DNA Damage DNA Library Fructose Galactose Genes Genetic Fitness Glucose Growth Disorders Infection Jumping Genes Lung Maltose Mannose Metals Methyl Methanesulfonate Mus N-Acetylneuraminic Acid Nasopharynx Norfloxacin Open Reading Frames Peroxide, Hydrogen Raffinose Reading Frames Stress Disorders, Traumatic Sucrose Transcription, Genetic
5
Quantitative Genetic Interaction Mapping
High-throughput SGA analysis, coupled with a new scoring method for normalizing experimental variations in genetic interaction arrays, have enabled us to extract quantitative measurements of genetic interactions. The genetic interactions are modeled after the Fisher definition of epistasis as quantitative deviations from the expected multiplicative combination of independently functioning genes (8 ), and which define genetic interaction strength as follows:

ƒa and ƒb denote the single mutant fitness of gene a and gene b respectively, and ƒab is the fitness of the double knockout mutant of gene a and b. The distribution of ε values follows a distribution such that interactions with higher absolute magnitude and therefore greater interaction strength are rare (Figure 2). Cutoffs for the genetic interactions can be imposed on the ε score or/and on the P-value depicting score confidence. Details of the scoring method are given in (A. Baryshnikova et al., manuscript in preparation) , but briefly, replicate double mutant colonies are used to assess confidence in the interaction measurements. Statistical analysis have shown that both reproducible and functionally informative interactions are determined at P-value <0.05 and |ε| > 0.08, and therefore these are used as the default cutoffs in DRYGIN queries.

Distribution of genetic interaction scores in DRYGIN. fa and fb denote the single mutant fitness of gene a and b, respectively, and fab is the fitness of the double knockout mutant of gene a and b.

Among the query genes screened are 214 temperature-sensitive (TS) query strains, corresponding to 183 ORFs. Also, 120 hypomorphic alleles are constructed by applying the DAmP (decreased abundance by mRNA perturbation) technique to replace the 3′ UTR and lower transcription of the targeted gene (9 (link)). The TS and DAmP query strains enriched the collection with essential ORFs, complementing the value of the genome-wide genetic interaction map with functional information from the essential gene set.
Koh J.L., Ding H., Costanzo M., Baryshnikova A., Toufighi K., Bader G.D., Myers C.L., Andrews B.J, & Boone C. (2009). DRYGIN: a database of quantitative genetic interaction networks in yeast. Nucleic Acids Research, 38(Database issue), D502-D507.
Publication 2009
3' Untranslated Regions Alarmins Alleles Genes Genes, Essential Genetic Diversity Genetic Fitness Open Reading Frames Reproduction RNA, Messenger Strains Transcription, Genetic

Most recents protocols related to «Genetic Fitness»

1
SARS-CoV-2 Spike Protein Docking
Molecular docking of the title compound
with spike protein was performed using a demo version of Genetic Optimization
for Ligand Docking (GOLD). A 3D crystal structure of SARS-CoV-2 spike
protein coupled to angiotensin-converting enzyme 2 (ACE2) (PDB ID: 6M0J) with a resolution
of 2.45 Å was downloaded from the Protein Data Bank to begin
the docking study. The spike protein with the reference molecule associated
with ACE2 was detached from the 3D structure of the receptor using
Discovery studio30 (link) to perform an in-silico
study. A CIF file of the 2EPCU was converted to an acceptable *mol2
format for use in docking processes. The Hermes visualize is an interface
of the GOLD suite that was used to prepare the 3D structure of spike
protein such as missing loops, side, and chains, adding hydrogen to
amino acids, and deleting water. The position of the reference molecule
coupled to the spike protein was used to confirm the active site for
the spike protein. All other parameters were left at their default
levels, and the complexes were subjected to 10 genetic algorithm runs
using the GOLD Score fitness function. The best-docked pose obtained
from the docking method was employed to visualize the interactions
of the complex with the spike protein using the Discovery studio visualization.
Gannouni A., Tahri W., Roisnel T., Al-Resayes S.I., Azam M, & Kefi R. (2023). Single Crystal Investigations, Hirshfeld Surface Analysis, DFT Studies, Molecular Docking, Physico-Chemical Characterization, and Biological Activity of a Novel Non-Centrosymmetric Compound with a Copper Transition Metal Precursor. ACS Omega, 8(8), 7738-7748.
Publication 2023
Acids Angiotensin Converting Enzyme 2 Genes, vif Genetic Fitness Hydrogen Ligands M protein, multiple myeloma Proteins RBPMS protein, human SARS-CoV-2
2
Nonlinear Optimization Algorithms in Civil Engineering
Nonlinear optimisation is performed with optimisation algorithms. Many were developed even before the electronic computer era, but modern computers significantly accelerated the development of new algorithms [47 ]. Often-used optimisation algorithms in the civil engineering field are first-order, second-order, direct, and population methods. Besides single-objective optimisation, as is the case in this paper, algorithms are also developed for multi-objective optimisation, where optimisation is performed simultaneously with respect to several objectives, as in [48 (link)]. Since not all optimisation algorithms always produce optimal results and based on the previous experiences and similar studies from the literature, it was decided to benchmark the performance of the following algorithms:

Sequential Least SQuares Programming (SLSQP) [49 ],

Particle Swarm Optimisation (PSO) [50 ], and

Genetic Algorithm (GA) [51 (link)].

While sequential quadratic programming methods were inspired by Newton’s method for solving systems of nonlinear equations [49 ], the PSO and GA methods are population-based methods. PSO was inspired by animal behaviours, for example, by a bird, which “swarms” randomly through the search space, recording and communicating with other birds about the best solution they have discovered [11 (link)]. GA was developed based on biological evolution, where fitter individuals are more likely to pass on their genes to the next generation. An individual’s fitness for reproduction is inversely related to the value of the objective function at that point [47 ].
When performing the nonlinear optimisation of the FE model, it is desirable that the FEA software can interact with the external programming platform such as MATLAB, Python, and Mathematica, where input files for the analysis are prepared, the FEA job submitted, the FEA results (output files) are checked, and the new input files prepared based on the optimisation algorithm decision. On the other hand, some FEAs, like Ansys [52 ], already include optimisation modules. In this study, Abaqus CAE 2016 [42 ] FEA software was used, with Python 3.7 utilising scipy.optimise.minimise [53 (link)] and pymoo [51 (link)] libraries.
Hekič D., Anžlin A., Kreslin M., Žnidarič A, & Češarek P. (2023). Model Updating Concept Using Bridge Weigh-in-Motion Data. Sensors (Basel, Switzerland), 23(4), 2067.
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Publication 2023
Animals Aves Biological Evolution Genes Genetic Fitness Python
3
Investigating Rhizosphere Colonization Strategies
Seeds of wild soybean, cultivated soybean, rice, and maize were treated with 95% ethanol for 30 s, and surface-sterilized in 17% NaClO (wt/vol) solution for 3 min (with wild soybean seeds pretreated by concentrated sulfuric acid for 3 min), then washed five to seven times using autoclaved deionized water and germinated on 0.8% agar plates at 28 °C in the dark for 48 h. To minimize false negatives in fitness gene screen, the input library was resuspended in 0.85% saline solution at OD600 = 1. The input library was inoculated onto the filter paper of plant culture dish (0.8% agar; low-N nutrient solution medium [45 (link)]). At the same time, 2-day-old seedlings were transferred to these culture dishes. Roots were then harvested at 7 dpi (days post inoculation), pooled, weighed, washed 5 times with 0.85% NaCl solution, and per 10 g were suspended in 15 ml 0.85% NaCl solution. After ultrasound treatment (50 Hz, 30 s for twice), the suspension was incubated in 200 ml TY medium with antibiotics for 32 h to facilitate further Tn-seq library construction for these rhizoplane samples (WS7d, CS7d, R7d, and Z7d). Control samples included 32 h TY cultures of input library (TY) and those TY cultures of filter papers collected from the plant culture dish at 1 hpi (hour post inoculation; F1h) or 7 dpi (F7d). Three independent mariner transposon insertion libraries were used as input libraries in three independent experiments (Fig. 1).Schematic overview for collecting output libraires for the Tn-seq analysis of root colonization arsenal of <i>Sinorhizobium fredii</i> on legume and cereal plants.

Three independent mariner transposon insertion libraries of S. fredii CCBAU25509 (SF2) were used as input inoculants in three independent experiments. Their 1h-post-inoculation (F1h) and 7d-post-inoculation (F7d) samples on the filter of plant culture dish were used as control samples to those rhizoplane samples of four test plant species (CS7d, WS7d, R7d, and Z7d). To facilitate Tn-seq library construction, all output samples were subject to cultivation in the TY rich medium for 32 h, and the input libraries were cultivated under the same condition as control (TY).

Ji Y.Y., Zhang B., Zhang P., Chen L.C., Si Y.W., Wan X.Y., Li C., Wang R.H., Tian Y., Zhang Z, & Tian C.F. (2023). Rhizobial migration toward roots mediated by FadL-ExoFQP modulation of extracellular long-chain AHLs. The ISME Journal, 17(3), 417-431.
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Publication 2023
Agar Antibiotics, Antitubercular cDNA Library Cereals Ethanol Fabaceae Genetic Fitness Hyperostosis, Diffuse Idiopathic Skeletal Jumping Genes Nutrients Plant Embryos Plant Roots Plants Rice Saline Solution Seedlings Sinorhizobium fredii Sodium Chloride Soybeans Strains Sulfuric Acids Ultrasonography Vaccination Zea mays
4
Enhancing BPNN Robustness via GA Optimization
The global search ability of a GA helps to optimize the initial weights and thresholds of a BPNN, thereby enhancing the robustness of the BPNN. The process of combining a GA with a BPNN is divided into two steps. The first step is to find the optimal initial weight and threshold of the BPNN by the global searching ability of the GA. In the second step, the optimal weights and thresholds obtained by the GA-BPNN are used to train the network in combination with the BP algorithm. The fitness function of the genetic algorithm considers the mean square error (MSE), which is defined as follows: Fig. 12 shows the process of the GA-BPNN. f(wi,bi)=1n∑h=1n∑m=1kthm-phmwi,bi2

GA-BPNN flow chart.

where thm represents the target value, phm is the predicted value based on the weight wi and the threshold bi, n is the number of training samples, and k is the number of samples for the predicted value.
Hou R, & Hou Q. (2023). Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm. Scientific Reports, 13, 2145.
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Publication 2023
Genetic Fitness
5
Quantifying Genetic Interactions via CRISPR Screens
For each gene, single gene mutant fitness (SMF) was calculated as the mean construct log fold change of gene-control constructs. The control was either non-essential genes or non-targeting gRNAs. For each gene pair, the expected double mutant fitness (DMF) of genes 1 and 2 was calculated as the sum of SMF of gene 1 and SMF of gene 2. The difference between expected and observed DMF, the mean LFC of all constructs targeting genes 1 and 2, was called dLFC.
Next step was calculating a modified Cohen’s D between observed and expected distribution of LFC of gRNAs targeting genes. Expected distribution of gRNAs targeting a gene pair, was calculated using expected mean and expected standard deviation.
 expected_mean =μ1+μ2
 expected_std =(std1)2+(std2)2
Spooled =( expected_std )2+( observed_std )22
 Cohen'sD = expected_mean − observed_mean Spooled 
Where:
In each cell line, the paralog pairs with dLFC < −1 and Cohen’s D > 0.8 were selected as hits. Cohen’s D more than 0.8 indicates large effect size between two groups, meaning that our expected and observed distribution of gRNAs are meaningfully separated. In total 388 paralog pairs were identified as hits across all the studies.
To identify the most consistent method in terms of hit identification, the Jaccard similarity coefficient of every pair of cell lines in each study was calculated by taking the ratio of intersection of hits over union of hits. For the studies that screened more than two cell lines, the final platform weight was the median of the calculated Jaccard coefficients of all pairs of cell lines.
Anvar N.E., Lin C., Ma X., Wilson L.L., Steger R., Sangree A.K., Colic M., Wang S.H., Doench J.G, & Hart T. (2023). Efficient gene knockout and genetic interactions: the IN4MER CRISPR/Cas12a multiplex knockout platform. bioRxiv.
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Publication Preprint 2023
Cell Lines Gene Expression Regulation Genes Genes, Duplicate Genes, Essential Genetic Fitness

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More about "Genetic Fitness"

Genetic fitness is a crucial aspect of genetic research, encompassing the assessment and optimization of an organism's genetic makeup.
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With its AI-driven capabilities and comprehensive data sources, PubCompare.ai is a valuable tool for researchers seeking to optimize their genetic fitness studies and drive advancements in this critical field of genomic research.