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Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

The monitoring of cities' wastewaters for the detection of potentially pathogenic viruses and bacteria has been considered a priority during the COVID-19 pandemic to monitor public health in urban environments. The methodological approaches frequently used for this purpose include deoxyribonucleic acid (DNA)/Ribonucleic acid (RNA) isolation followed by quantitative polymerase chain reaction (qPCR) and reverse transcription (RT)‒qPCR targeting pathogenic genes. More recently, the application of metatranscriptomic has opened opportunities to develop broad pathogenic monitoring workflows covering the entire pathogenic community within the sample. Nevertheless, the high amount of data generated in the process requires an appropriate analysis to detect the pathogenic community from the entire dataset. Here, an implementation of a bioinformatic workflow was developed to produce a map of the detected pathogenic bacteria and viruses in wastewater samples by analysing metatranscriptomic data. The main objectives of this work was the development of a computational methodology that can accurately detect both human pathogenic virus and bacteria in wastewater samples. This workflow can be easily reproducible with open-source software and uses efficient computational resources. The results showed that the used algorithms can predict potential human pathogens presence in the tested samples and that active forms of both bacteria and virus can be identified. By comparing the computational method implemented in this study to other state-of-the-art workflows, the implementation analysis was faster, while providing higher accuracy and sensitivity. Considering these results, the processes and methods to monitor wastewater for potential human pathogens can become faster and more accurate. The proposed workflow is available at https://github.com/waterpt/watermonitor and can be implemented in currently wastewater monitoring programs to ascertain the presence of potential human pathogenic species. published

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model’s ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances, namely local, medium, or distant associations.This paper addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference- and alignment-free, providing additional methodologies for testing, including a highly-flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar.The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multi-threading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided athttps://github.com/cobilab/alcor.

Background Little is known on the landscape of viruses that have taken residence within our cells, nor on the interplay with the host imperative for their persistence. However, a lifetime of interactions conceivably have an imprint on our physiology and immune phenotype. Importantly, current metagenomics-insights on the healthy human virome are derived from bodily fluids, which are only a proxy of the true prevalence of human eukaryotic viruses within tissues. Most significantly, the virome’s systemic composition across the different organs of an individual has thus far remained uncharted. Results In this work, we revealed the genetic make-up of the eukaryotic human DNA virome in the body and showed that each organ (colon, liver, lung, heart, brain, kidney, skin, blood, hair) has unique viral compositions. By integration of quantitative (qPCR) and qualitative (hybrid-capture sequencing) analysis, we identified the DNAs of 17 viruses, primarily herpes-, parvo-, papilloma- and anello-viruses (>80% prevalence), typically persisting in low copies (mean 540 copies/ million cells). The within-sample diversities (a-diversity) were highest in the lung, liver, colon, and kidney. We assembled in total 70 viral genomes (>90% breadth coverage), distinct in each of the individuals, and identified high sequence homology across the organs. Moreover, we detected variations in virome composition and distribution in two individuals with underlying malignant conditions. Conclusions Our findings reveal unprecedented prevalences of viral DNAs in human organs. We showed that bodily fluids, although accessible to sampling, fail to deliver a comprehensive view of the numerous intracellular viruses colonizing our tissues. Our data demonstrate how a multi-organ approach is essential for analyzing differing virome compositions in various disease states. This atlas provides a fundamental ground for the studies of correlates to disease, and for the interpretation of next-generation sequencing data in clinical virology. Ultimately, our findings call for investigation of the crosstalk between human DNA viruses, the host, and other microbes, as it predictably has a significant impact on our health.

The artistic community is increasingly relying on automatic computational analysis for authentication and classification of artistic paintings. In this paper, we identify hidden patterns and relationships present in artistic paintings by analysing their complexity, a measure that quantifies the sum of characteristics of an object. Specifically, we apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors and examine the potential of these information-based measures as descriptors of artistic paintings. Both measures consistently described the equivalent types of paintings, authors, and artistic movements. Moreover, combining the NC with a measure of the roughness of the paintings creates an efficient stylistic descriptor. Furthermore, by quantifying the local information of each painting, we define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques. More fundamentally, this information describes how each author typically composes and distributes the elements across the canvas and, therefore, how their work is perceived. Finally, we demonstrate that regional complexity and two-point height difference correlation function are useful auxiliary features that improve current methodologies in style and author classification of artistic paintings. The whole study is supported by an extensive website (http://panther.web.ua.pt) for fast author characterization and authentication., Comment: Website: http://panther.web.ua.pt 24 Pages; 19 pages article; 5 pages supplementary material

Background Viruses are among the shortest yet highly abundant species that harbor minimal instructions to infect cells, adapt, multiply, and exist. However, with the current substantial availability of viral genome sequences, the scientific repertory lacks a complexity landscape that automatically enlights viral genomes’ organization, relation, and fundamental characteristics. Results This work provides a comprehensive landscape of the viral genome’s complexity (or quantity of information), identifying the most redundant and complex groups regarding their genome sequence while providing their distribution and characteristics at a large and local scale. Moreover, we identify and quantify inverted repeats abundance in viral genomes. For this purpose, we measure the sequence complexity of each available viral genome using data compression, demonstrating that adequate data compressors can efficiently quantify the complexity of viral genome sequences, including subsequences better represented by algorithmic sources (e.g., inverted repeats). Using a state-of-the-art genomic compressor on an extensive viral genomes database, we show that double-stranded DNA viruses are, on average, the most redundant viruses while single-stranded DNA viruses are the least. Contrarily, double-stranded RNA viruses show a lower redundancy relative to single-stranded RNA. Furthermore, we extend the ability of data compressors to quantify local complexity (or information content) in viral genomes using complexity profiles, unprecedently providing a direct complexity analysis of human herpesviruses. We also conceive a features-based classification methodology that can accurately distinguish viral genomes at different taxonomic levels without direct comparisons between sequences. This methodology combines data compression with simple measures such as GC-content percentage and sequence length, followed by machine learning classifiers. Conclusions This article presents methodologies and findings that are highly relevant for understanding the patterns of similarity and singularity between viral groups, opening new frontiers for studying viral genomes’ organization while depicting the complexity trends and classification components of these genomes at different taxonomic levels. The whole study is supported by an extensive website (https://asilab.github.io/canvas/) for comprehending the viral genome characterization using dynamic and interactive approaches.

Formalin fixation, albeit an outstanding method for morphological and molecular preservation, induces DNA damage and cross-linking, which can hinder nucleic acid screening. This is of particular concern in the detection of low-abundance targets, such as persistent DNA viruses. In the present study, we evaluated the analytical sensitivity of viral detection in lung, liver, and kidney specimens from four deceased individuals. The samples were either frozen or incubated in formalin (±paraffin embedding) for up to 10 days. We tested two DNA extraction protocols for the control of efficient yields and viral detections. We used short-amplicon qPCRs (63–159 nucleotides) to detect 11 DNA viruses, as well as hybridization capture of these plus 27 additional ones, followed by deep sequencing. We observed marginally higher ratios of amplifiable DNA and scantly higher viral genoprevalences in the samples extracted with the FFPE dedicated protocol. Based on the findings in the frozen samples, most viruses were detected regardless of the extended fixation times. False-negative calls, particularly by qPCR, correlated with low levels of viral DNA (150 base pairs). Our data suggest that low-copy viral DNAs can be satisfactorily investigated from FFPE specimens, and encourages further examination of historical materials.

BackgroundCassava (Manihot esculenta) is an important clonally propagated food crop in tropical and sub-tropical regions worldwide. Genetic gain by molecular breeding is limited because cassava has a highly heterozygous, repetitive and difficult to assemble genome.FindingsHere we demonstrate that Pacific Biosciences high-fidelity (HiFi) sequencing reads, in combination with the assembler hifiasm, produced genome assemblies at near complete haplotype resolution with higher continuity and accuracy compared to conventional long sequencing reads. We present two chromosome scale haploid genomes phased with Hi-C technology for the diploid African cassava variety TME204. Genome comparisons revealed extensive chromosome re-arrangements and abundant intra-genomic and inter-genomic divergent sequences despite high gene synteny, with most large structural variations being LTR-retrotransposon related. Allele-specific expression analysis of different tissues based on the haplotype-resolved transcriptome identified both stable and inconsistent alleles with imbalanced expression patterns, while most alleles expressed coordinately. Among tissue-specific differentially expressed transcripts, coordinately and biasedly regulated transcripts were functionally enriched for different biological processes. We use the reference-quality assemblies to build a cassava pan-genome and demonstrate its importance in representing the genetic diversity of cassava for downstream reference-guided omics analysis and breeding.ConclusionsThe haplotype-resolved genome allows the first systematic view of the heterozygous diploid genome organization in cassava. The completely phased and annotated chromosome pairs will be a valuable resource for cassava breeding and research. Our study may also provide insights into developing cost-effective and efficient strategies for resolving complex genomes with high resolution, accuracy and continuity.

Cassava (Manihot esculenta) is an important clonally propagated food crop in tropical and subtropical regions worldwide. Genetic gain by molecular breeding has been limited, partially because cassava is a highly heterozygous crop with a repetitive and difficult-to-assemble genome.Here we demonstrate that Pacific Biosciences high-fidelity (HiFi) sequencing reads, in combination with the assembler hifiasm, produced genome assemblies at near complete haplotype resolution with higher continuity and accuracy compared to conventional long sequencing reads. We present 2 chromosome-scale haploid genomes phased with Hi-C technology for the diploid African cassava variety TME204. With consensus accuracyQV46, contig N5018 Mb, BUSCO completeness of 99%, and 35k phased gene loci, it is the most accurate, continuous, complete, and haplotype-resolved cassava genome assembly so far. Ab initio gene prediction with RNA-seq data and Iso-Seq transcripts identified abundant novel gene loci, with enriched functionality related to chromatin organization, meristem development, and cell responses. During tissue development, differentially expressed transcripts of different haplotype origins were enriched for different functionality. In each tissue, 20-30% of transcripts showed allele-specific expression (ASE) differences. ASE bias was often tissue specific and inconsistent across different tissues. Direction-shifting was observed in2% of the ASE transcripts. Despite high gene synteny, the HiFi genome assembly revealed extensive chromosome rearrangements and abundant intra-genomic and inter-genomic divergent sequences, with large structural variations mostly related to LTR retrotransposons. We use the reference-quality assemblies to build a cassava pan-genome and demonstrate its importance in representing the genetic diversity of cassava for downstream reference-guided omics analysis and breeding.The phased and annotated chromosome pairs allow a systematic view of the heterozygous diploid genome organization in cassava with improved accuracy, completeness, and haplotype resolution. They will be a valuable resource for cassava breeding and research. Our study may also provide insights into developing cost-effective and efficient strategies for resolving complex genomes with high resolution, accuracy, and continuity.

Background Advances in sequencing technologies have enabled the characterization of multiple microbial and host genomes, opening new frontiers of knowledge while kindling novel applications and research perspectives. Among these is the investigation of the viral communities residing in the human body and their impact on health and disease. To this end, the study of samples from multiple tissues is critical, yet, the complexity of such analysis calls for a dedicated pipeline. We provide an automatic and efficient pipeline for identification, assembly, and analysis of viral genomes that combines the DNA sequence data from multiple organs. TRACESPipe relies on cooperation among 3 modalities: compression-based prediction, sequence alignment, and de novo assembly. The pipeline is ultra-fast and provides, additionally, secure transmission and storage of sensitive data. Findings TRACESPipe performed outstandingly when tested on synthetic and ex vivo datasets, identifying and reconstructing all the viral genomes, including those with high levels of single-nucleotide polymorphisms. It also detected minimal levels of genomic variation between different organs. Conclusions TRACESPipe’s unique ability to simultaneously process and analyze samples from different sources enables the evaluation of within-host variability. This opens up the possibility to investigate viral tissue tropism, evolution, fitness, and disease associations. Moreover, additional features such as DNA damage estimation and mitochondrial DNA reconstruction and analysis, as well as exogenous-source controls, expand the utility of this pipeline to other fields such as forensics and ancient DNA studies. TRACESPipe is released under GPLv3 and is available for free download at https://github.com/viromelab/tracespipe.

Advancement of protein sequencing technologies has led to the production of a huge volume of data that needs to be stored and transmitted. This challenge can be tackled by compression. In this paper, we propose AC, a state-of-the-art method for lossless compression of amino acid sequences. The proposed method works based on the cooperation between finite-context models and substitutional tolerant Markov models. Compared to several general-purpose and specific-purpose protein compressors, AC provides the best bit-rates. This method can also compress the sequences nine times faster than its competitor, paq8l. In addition, employing AC, we analyze the compressibility of a large number of sequences from different domains. The results show that viruses are the most difficult sequences to be compressed. Archaea and bacteria are the second most difficult ones, and eukaryota are the easiest sequences to be compressed.