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Book ; Online: An experimental sorting method for improving metagenomic data encoding

Pratas, Diogo / Pinho, Armando J.

2024

Abstract: Minimizing data storage poses a significant challenge in large-scale metagenomic projects. In this paper, we present a new method for improving the encoding of FASTQ files generated by metagenomic sequencing. This method incorporates metagenomic ... ...

Abstract	Minimizing data storage poses a significant challenge in large-scale metagenomic projects. In this paper, we present a new method for improving the encoding of FASTQ files generated by metagenomic sequencing. This method incorporates metagenomic classification followed by a recursive filter for clustering reads by DNA sequence similarity to improve the overall reference-free compression. In the results, we show an overall improvement in the compression of several datasets. As hypothesized, we show a progressive compression gain for higher coverage depth and number of identified species. Additionally, we provide an implementation that is freely available at https://github.com/cobilab/mizar and can be customized to work with other FASTQ compression tools.
Keywords	Computer Science - Information Theory ; Quantitative Biology - Genomics
Publishing date	2024-01-03
Publishing country	us
Document type	Book ; Online
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Article ; Online: AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.

Silva, Jorge M / Qi, Weihong / Pinho, Armando J / Pratas, Diogo

GigaScience

2023 Volume 12

Abstract: Background: 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, ... ...

Abstract	Background: 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. Findings: This article 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. Conclusions: 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 multithreading 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 at https://github.com/cobilab/alcor.
MeSH term(s)	Humans ; Proteomics ; Sequence Analysis, DNA/methods ; Repetitive Sequences, Nucleic Acid ; Computer Simulation ; Genome, Human
Language	English
Publishing date	2023-12-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	2708999-X
ISSN	2047-217X ; 2047-217X
ISSN (online)	2047-217X
ISSN	2047-217X
DOI	10.1093/gigascience/giad101
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Article ; Online: Persistent minimal sequences of SARS-CoV-2.

Pratas, Diogo / Silva, Jorge M

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 21, Page(s) 5129–5132

Abstract: Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis and therapeutics are of great ... ...

Abstract	Motivation: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 14 million cases and more than half million deaths. Given the absence of implemented therapies, new analysis, diagnosis and therapeutics are of great importance. Results: Analysis of SARS-CoV-2 genomes from the current outbreak reveals the presence of short persistent DNA/RNA sequences that are absent from the human genome and transcriptome (PmRAWs). For the PmRAWs with length 12, only four exist at the same location in all SARS-CoV-2. At the gene level, we found one PmRAW of size 13 at the Spike glycoprotein coding sequence. This protein is fundamental for binding in human ACE2 and further use as an entry receptor to invade target cells. Applying protein structural prediction, we localized this PmRAW at the surface of the Spike protein, providing a potential targeted vector for diagnostics and therapeutics. In addition, we show a new pattern of relative absent words (RAWs), characterized by the progressive increase of GC content (Guanine and Cytosine) according to the decrease of RAWs length, contrarily to the virus and host genome distributions. New analysis shows the same property during the Ebola virus outbreak. At a computational level, we improved the alignment-free method to identify pathogen-specific signatures in balance with GC measures and removed previous size limitations. Availability and implementation: https://github.com/cobilab/eagle. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	COVID-19 ; Humans ; Protein Binding ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus
Chemical Substances	Spike Glycoprotein, Coronavirus
Keywords	covid19
Language	English
Publishing date	2020-07-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa686
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Article: Neuroendocrine breast carcinoma.

Guerra, Laura Pratas / Simões, Joana / Sá, Diogo Carvalho / Polónia, José / Araújo, António

Autopsy & case reports

2024 Volume 14, Page(s) e2024484

Abstract: Neuroendocrine breast cancer (NEBC) is a rare and heterogeneous entity. It most commonly presents a luminal phenotype and a worse prognosis. When diagnosed in an advanced stage, metastasis from another neuroendocrine tumor should be excluded. This case ... ...

Abstract	Neuroendocrine breast cancer (NEBC) is a rare and heterogeneous entity. It most commonly presents a luminal phenotype and a worse prognosis. When diagnosed in an advanced stage, metastasis from another neuroendocrine tumor should be excluded. This case features a premenopausal woman with an oligometastatic breast large cell neuroendocrine carcinoma, estrogen receptor (ER) positive, and human epidermal growth factor receptor 2 (HER2) negative. Since the patient was very symptomatic at the presentation of the disease, chemotherapy was started. Complete radiological response of the metastatic disease was achieved, and the patient was then submitted to radical breast surgery and bilateral oophorectomy. She subsequently underwent radiation therapy. Since then and to date, she has been under endocrine therapy (ET) and a CDK4/6 inhibitor (CDK4/6i), with no evidence of malignant disease. Evidence to guide the choice of treatment for these tumors is currently scarce. In cases with oligometastatic disease, radical treatment should be considered. Given that this entity is rare, its reporting should be encouraged.
Language	English
Publishing date	2024-03-21
Publishing country	Brazil
Document type	Case Reports
ZDB-ID	2815488-5
ISSN	2236-1960
ISSN	2236-1960
DOI	10.4322/acr.2024.484
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Article ; Online: Unmasking the tissue-resident eukaryotic DNA virome in humans.

Pyöriä, Lari / Pratas, Diogo / Toppinen, Mari / Hedman, Klaus / Sajantila, Antti / Perdomo, Maria F

Nucleic acids research

2023 Volume 51, Issue 7, Page(s) 3223–3239

Abstract: Little is known on the landscape of viruses that reside within our cells, nor on the interplay with the host imperative for their persistence. Yet, a lifetime of interactions conceivably have an imprint on our physiology and immune phenotype. In this ... ...

Abstract	Little is known on the landscape of viruses that reside within our cells, nor on the interplay with the host imperative for their persistence. Yet, a lifetime of interactions conceivably have an imprint on our physiology and immune phenotype. In this work, we revealed the genetic make-up and unique composition of the known eukaryotic human DNA virome in nine organs (colon, liver, lung, heart, brain, kidney, skin, blood, hair) of 31 Finnish individuals. By integration of quantitative (qPCR) and qualitative (hybrid-capture sequencing) analysis, we identified the DNAs of 17 species, primarily herpes-, parvo-, papilloma- and anello-viruses (>80% prevalence), typically persisting in low copies (mean 540 copies/ million cells). 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 in two individuals with underlying malignant conditions. Our findings reveal unprecedented prevalences of viral DNAs in human organs and provide a fundamental ground for the investigation of disease correlates. Our results from post-mortem tissues 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.
MeSH term(s)	Humans ; DNA, Viral/genetics ; DNA, Viral/analysis ; Eukaryota/genetics ; Virome ; Viruses/genetics ; Genome, Human ; Organ Specificity
Chemical Substances	DNA, Viral
Language	English
Publishing date	2023-03-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad199
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Article ; Online: AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.

Silva, Milton / Pratas, Diogo / Pinho, Armando J

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 5

Abstract: Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data ... ...

Abstract	Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2-9% and 6-7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences' input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.
Language	English
Publishing date	2021-04-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23050530
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Article ; Online: The complexity landscape of viral genomes.

Silva, Jorge Miguel / Pratas, Diogo / Caetano, Tânia / Matos, Sérgio

GigaScience

2022 Volume 11

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Base Composition ; Genome, Viral ; Genomics/methods ; Humans ; Viruses/genetics
Language	English
Publishing date	2022-08-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2708999-X
ISSN	2047-217X ; 2047-217X
ISSN (online)	2047-217X
ISSN	2047-217X
DOI	10.1093/gigascience/giac079
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Article ; Online: AC2

Milton Silva / Diogo Pratas / Armando J. Pinho

Entropy, Vol 23, Iss 530, p

An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models

2021 Volume 530

Abstract	Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2–9% and 6–7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences’ input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.
Keywords	lossless data compression ; protein sequence compression ; context mixing ; neural networks ; mixture of experts ; coronavirus ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	612
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A semi-automatic methodology for analysing distributed and private biobanks.

Almeida, João Rafael / Pratas, Diogo / Oliveira, José Luís

Computers in biology and medicine

2020 Volume 130, Page(s) 104180

Abstract: Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent ... ...

Abstract	Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.
MeSH term(s)	Biological Specimen Banks ; COVID-19 ; Humans ; Public Health ; SARS-CoV-2
Language	English
Publishing date	2020-12-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2020.104180
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Zs.A 653: Show issues

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Article ; Online: Efficient DNA sequence compression with neural networks.

Silva, Milton / Pratas, Diogo / Pinho, Armando J

GigaScience

2020 Volume 9, Issue 11

Abstract: Background: The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data ... ...

Abstract	Background: The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution-tolerant context models. Findings: We benchmark GeCo3 as a reference-free DNA compressor in 5 datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, 2 compilations of archaeal and virus genomes, 4 whole genomes, and 2 collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of $2.4\%$, $7.1\%$, $6.1\%$, $5.8\%$, and $6.0\%$, respectively. To test its performance as a reference-based DNA compressor, we benchmark GeCo3 in 4 datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in $12.4\%$, $11.7\%$, $10.8\%$, and $10.1\%$ over the state of the art. The cost of this compression improvement is some additional computational time (1.7-3 times slower than GeCo2). The RAM use is constant, and the tool scales efficiently, independently of the sequence size. Overall, these values outperform the state of the art. Conclusions: GeCo3 is a genomic sequence compressor with a neural network mixing approach that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3.
MeSH term(s)	Algorithms ; Base Sequence ; High-Throughput Nucleotide Sequencing ; Neural Networks, Computer ; Sequence Analysis, DNA ; Software
Language	English
Publishing date	2020-11-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2708999-X
ISSN	2047-217X ; 2047-217X
ISSN (online)	2047-217X
ISSN	2047-217X
DOI	10.1093/gigascience/giaa119
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