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Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantifying their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.

The mechanisms by which the genotype interacts with nutrition during development to contribute to the variation of complex behaviors and brain morphology of adults are not well understood. Here we use the Drosophila Genetic Reference Panel to identify genes and pathways underlying these interactions in sleep behavior and mushroom body morphology. We show that early-life nutritional restriction has genotype-specific effects on variation in sleep behavior and brain morphology. We mapped genes associated with sleep sensitivity to early-life nutrition, which were enriched for protein-protein interactions responsible for translation, endocytosis regulation, ubiquitination, lipid metabolism, and neural development. By manipulating the expression of candidate genes in the mushroom bodies and all neurons, we confirm that genes regulating neural development, translation and insulin signaling contribute to the variable response of sleep and brain morphology to early-life nutrition. We show that the interaction between differential expression of candidate genes with nutritional restriction in early life resides in the mushroom bodies or other neurons, and that these effects are sex specific. Natural variation in genes that control the systemic response to nutrition and brain development and function interact with early-life nutrition in different types of neurons to contribute to the variation of brain morphology and adult sleep behavior.

Motivation BRENDA is the largest enzyme functional database, containing information of 84 000 experimentally characterized enzyme entries. This database is an invaluable resource for researchers in the biological field, which classifies enzyme-related information in categories that are very useful to obtain specific functional and protein engineering information for enzyme families. However, the BRENDA web interface, the most used by researchers with a non-informatic background, does not allow the user to cross-reference data from different categories or sub-categories in the database. Obtaining information in an easy and fast way, in a friendly web interface, without the necessity to have a deep informatics knowledge, will facilitate and improve research in the enzymology and protein engineering field. Results We developed the Brenda Easy Search Tool (BEST), an interactive Shiny/R application that enables querying the BRENDA database for complex cross-tabulated characteristics, and retrieving enzyme-related parameters and information readily and efficiently, which can be used for the study of enzyme function or as an input for other bioinformatics tools. Availability and implementation BEST and its tutorial are freely available from https://pesb2.cl/best/. Supplementary information Supplementary data are available at Bioinformatics online.

Recent work suggests that certain places can be more attractive for car theft based how many people regularly visit them, as well as other factors. In this sense, we must also consider the city or district itself where vehicles are stolen. All cities have different cultural and socioeconomic characteristics that influence car theft patterns. In particular, the distribution of public services and places attract a large crowd could play a key role in the occurrence of car theft. Santiago, a city that displays drastic socioeconomic differences among its districts, presents increasingly-high car theft rates. This represents a serious issue for the city, as for any other major city, which –at least for Santiago– has not been analyzed in depth using quantitative approaches. In this work, we present a preliminary study of how places that create social interest, such as restaurants, bars, schools, and shopping malls, increase car theft frequency in Santiago. We also study if some types of places are more attractive than others for this type of crime. To evaluate this, we propose to analyze car theft points (CTP) from insurance companies and their relationship with places of social interest (PSI) extracted from Google Maps, using a proximity based approach. Our findings show a high correlation between CTP and PSI for all of the social interest categories that we studied in the different districts of the Santiago. In particular our work contributes to the understanding of the social factors that are associated to car thefts.

In the 40’s, Erwin Chargaff was the first to observe the parity between Adenines (A) and Timinies (T) and Citosines (C) and Guanines (G), in the molecule of DNA. In the 60’s, Chargaff found a second parity rule. This time in a single strand of DNA. The amounts of A’s and T’s, and the amounts of C’s and G’s is similar. The explanation of the first rule is the complementary nature of the double stranded helix of the DNA molecule. However, for the 2nd rule, a biological explanation has remained a mystery. In the last 40 years, a generalization of the second rule was proposed, to explain the 2nd rule as a particular case. This generalization states that for any given k-mer and its reverse complement (RC), the number of times both are found is similar in a single strand of DNA. Two measures have been proposed to test the generalized Chargaff’s 2nd rule (gC2r), both include an artifact regarding the length of the genomes. This has led the authors to think there is a minimum length of a genome and a maximum k-mer for compliance. We propose a new way to measure the compliance of any given genome to the gC2r. The measure is the proportion of the genome which complies with gC2r. The compliance is measured per pair of kmer/k-merRC, using the natural logarithm of the number of times the k-mer is found, divided by the number of times its reverse complement is found in the genome or ln(#k-mer/k-merRC). This measure is independent of the size of the analyzed k-mer and the size of the genome. This measure has been implemented in a software, ChargaffCracker, which can rapidly analyze sequences and deliver a statistical report. We have generated random genomes based on the proportions and lengths of biological prokaryote genome sequences and compared them. We conclude hypothesizing that: 1. The compliance of the gC2r is a consequence, not cause of the 2nd rule and; 2. Although Chargaff’s 2nd rule might be a consequence of transpositions and inversions, the limits of compliance of the gC2r is a property of the sequence model of genomes, not of the biology of organisms. However, this property might have been selected to fulfill biological needs in genome evolution.

This chapter describes methods currently available for visualizing results from systems genetics experiments. Here, we abstract from the statistical methods used for genetic mapping, which are dependent on the specific resource being used, i.e. F2, RILs, or outbred populations among others. We use a public dataset with results from a mouse eQTL experiment for three examples of visualization: genome-wide dot plots of marker-by-gene association, karyotype-like plots, and circos plots. Dot plots give a first overview of the results from eQTL mapping, allowing detecting genome-wide patterns of cis- and trans-genetic association to transcription level. Karyotype-like plots provide chromosomal context and allow integrating multiple tracks of information in a single plot. Circos plots can, in addition, display long-range interactions to provide an overview of genetic connectivity at the genome level. All examples are developed and explained using R code, an open-source language with powerful statistical and graphical capabilities. The principles reviewed here, however, can be applied with other software options, organisms, and to any type of molecular phenotype that can be assigned to a genomic position.

This chapter describes methods currently available for visualizing results from systems genetics experiments. Here, we abstract from the statistical methods used for genetic mapping, which are dependent on the specific resource being used, i.e. F2, RILs, or outbred populations among others. We use a public dataset with results from a mouse eQTL experiment for three examples of visualization: genome-wide dot plots of marker-by-gene association, karyotype-like plots, and circos plots. Dot plots give a first overview of the results from eQTL mapping, allowing detecting genome-wide patterns of cis- and trans-genetic association to transcription level. Karyotype-like plots provide chromosomal context and allow integrating multiple tracks of information in a single plot. Circos plots can, in addition, display long-range interactions to provide an overview of genetic connectivity at the genome level. All examples are developed and explained using R code, an open-source language with powerful statistical and graphical capabilities. The principles reviewed here, however, can be applied with other software options, organisms, and to any type of molecular phenotype that can be assigned to a genomic position.

Chargaff’s second parity rule holds that for each of the two DNA strands in a genome, the %A is similar to %T and %G is similar to %C. Although the validity of the second rule is still in debate and the biological cause is unknown, a generalized form of the second parity rule has already been proposed. The generalization states that the frequency of a string of a particular length is similar to the frequency of its reverse complement in the same strand. In a previous work, we have developed a statistical hypothesis test for the generalized second Chargaff parity rule for any particular string in a genome. One obstacle to test all available genomes with this statistical test was the efficiency of the computational implementation. In this work, we circumvent this issue, implementing our statistical test in an efficient and multi-processing computer program. The development was carried out in Python, using packets for handling sequences in FASTA format and the required calculations. For each input genome, a database SQLite is generated holding the absolute frequencies of all existing strings in the genome up to a user defined length, and their reverse complements. Multiple sequences are accepted as input, each sequence being analyzed in one CPU. Thus, a bacterial genome, ~4M characters, with 4 sequences takes about 14 seconds to process entirely in 4 CPUs. This computer program will allow our test to be carried out in all available completely sequenced genomes and assess the validity of Chargaff’s second parity rule and its generalized version.

Summary: Visualizing genomic data in chromosomal context can help detecting errors in data generation or analysis and can suggest new hypotheses to be tested. Here we report a new tool for displaying large and diverse genomic data in idiograms of one or multiple chromosomes. The package is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiples tracks of data, each of potentially different nature. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, points connected by lines, line segments, barplots or histograms for representing data. chromPlot reads data from tables in BED format which are imported in R using its builtin functions. The information necessary to draw chromosomes for mouse and human is included with the package. Chromosomes for other organisms are downloaded automatically from the Ensembl website or can be provided by the user. We present common use cases here, and a full tutorial is included as the packages's vignette.Availability: chromPlot is distributed under a GLP2 licence at Genomed Lab: http://genomed.med.uchile.cl.Contact:raverdugo@u.uchile.cl