RecGraph: recombination-aware alignment of sequences to variation graphs.

MOTIVATION: Bacterial genomes present more variability than human genomes, which requires important adjustments in computational tools that are developed for human data. In particular, bacteria exhibit a mosaic structure due to homologous recombinations, but this fact is not sufficiently captured by standard read mappers that align against linear reference genomes. The recent introduction of pangenomics provides some insights in that context, as a pangenome graph can represent the variability within a species. However, the concept of sequence-to-graph alignment that captures the presence of recombinations has not been previously investigated. RESULTS: In this paper, we present the extension of the notion of sequence-to-graph alignment to a variation graph that incorporates a recombination, so that the latter are explicitly represented and evaluated in an alignment. Moreover, we present a dynamic programming approach for the special case where there is at most a recombination-we implement this case as RecGraph. From a modelling point of view, a recombination corresponds to identifying a new path of the variation graph, where the new arc is composed of two halves, each extracted from an original path, possibly joined by a new arc. Our experiments show that RecGraph accurately aligns simulated recombinant bacterial sequences that have at most a recombination, providing evidence for the presence of recombination events. AVAILABILITY AND IMPLEMENTATION: Our implementation is open source and available at https://github.com/AlgoLab/RecGraph.

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

BACKGROUND: Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. RESULTS: In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. CONCLUSIONS: By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY: The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Use of accelerometers for automatic regional chest movement recognition during tidal breathing in healthy subjects.

Recognition of breathing patterns helps clinicians to understand acute and chronic adaptations during exercise and pathological conditions. Wearable technologies combined with a proper data analysis provide a low cost option to monitor chest and abdominal wall movements. Here we set out to determine the feasibility of using accelerometry and machine learning to detect chest-abdominal wall movement patterns during tidal breathing. Furthermore, we determined the accelerometer positions included in the clusters, considering principal component domains. Eleven healthy participants (age: 21 ±â€¯0.2 y, BMI: 23.4 ±â€¯0.7 kg/m2, FEV1: 4.1 ±â€¯0.3 L, VO2: 4.6 ±â€¯0.2 mL/min kg) were included in this cross-sectional study. Spirometry and ergospirometry assessments were performed with participants seated with 13 accelerometers placed over the thorax. Data collection lasted 10  min. Following signal pre-processing, principal components and clustering analyses were performed. The Euclidean distances in respect to centroids were compared between the clusters (p < 0.05), identifying two clusters (p < 0.001). The first cluster included sensors located at the right and left second rib midline, body of sternum, left fourth rib midline, right and left second thoracic vertebra midline, and fifth thoracic vertebra. The second cluster included sensors at the fourth right rib midline, right and left seventh ribs, abdomen at linea alba, and right and left tenth thoracic vertebra midline. Costal-superior and costal-abdominal patterns were also recognized. We conclude that accelerometers placed on the chest and abdominal wall permit the identification of two clusters of movements regarding respiration biomechanics.

Fractionation and bioavailability of arsenic in agricultural soils: solvent extraction tests and their relevance in risk assessment.

Sequential extraction procedure (SEP) and a physiologically based extraction test (PBET) were performed with the aim to estimate the mobility, bioavailability (for plants and humans) and spatial variation of arsenic in agricultural soils in the Valleys of Arica and Parinacota Region (Northern Chile). For this purpose, 50 topsoil samples with different total arsenic contents in soil (36.2-729 mg kg(-1)) were collected from 10 selected sites in the Valley of Lluta, Azapa and Camarones. The SEP test results showed that arsenic was mainly associated to the least mobile fractions: bound to amorphous and poorly crystalline hydrous oxides of Fe and Al (11.6-44.2%) and well-crystallized hydrous oxides of Fe and Al (24.8-48.9%). Calculated values for arsenic Chronic Daily Intake (CDI), based on the information obtained in the tests of bioaccessibility using PBET (range 1.6-9.6 mg kg(-1)), were in the range of 0.021 to 0.128 µg As kg(-1) d(-1), not exceeding in any case the maximum Reference Dose for Chronic Oral Exposure, RfD = 0.3 µg kg(-1) d(-1), established by USEPA. In general, obtained results, allow us to establish that extraction processes using solvents can be utilized as a source of reliable and useful information for risk assessment of exposure to arsenic from soil, over the direct use of total arsenic contents, which can lead to an overestimation of the toxicity by direct ingestion.