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Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies and commercial service providers for decades. These DNA archives represent a valuable, yet largely un‐tapped repository of genomic information. With lowering costs of, and increasing access to, high‐throughput sequencing, we predict an increase in retrospective research to explore the wealth of information that resides in these archival samples. However, for this to occur, we need confidence in the integrity of the DNA samples, often stored under suboptimal conditions and their fitness of purpose for downstream genomic analysis.Here, we borrow from a well‐established concept in ancient DNA to evaluate sample integrity, defined as loss of information content in recovered amplicons, of frozen DNA samples and based on the ratio of ⍺‐diversity of short‐ and long‐read 16S rRNA gene sequences.The 16S rRNA variable region of 87 stored DNA samples, extracted from soil, collected from western and southern agricultural regions of Australia between 2001 and 2021 were sequenced using both PacBio full length reads (V1–V9, 1.5 kbp) and Illumina short‐reads (V3–V4, 200–450 bp). When ⍺‐diversity ratios were calculated between the long and short reads to assess DNA degradation, the ratio of ⍺‐diversity did not decrease in older samples versus younger samples.We suggest this as a novel method to confirm integrity of DNA before embarking on large‐scale diversity profiling projects using archival DNA. [ABSTRACT FROM AUTHOR]

Abstract Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies and commercial service providers for decades. These DNA archives represent a valuable, yet largely un‐tapped repository of genomic information. With lowering costs of, and increasing access to, high‐throughput sequencing, we predict an increase in retrospective research to explore the wealth of information that resides in these archival samples. However, for this to occur, we need confidence in the integrity of the DNA samples, often stored under suboptimal conditions and their fitness of purpose for downstream genomic analysis. Here, we borrow from a well‐established concept in ancient DNA to evaluate sample integrity, defined as loss of information content in recovered amplicons, of frozen DNA samples and based on the ratio of ⍺‐diversity of short‐ and long‐read 16S rRNA gene sequences. The 16S rRNA variable region of 87 stored DNA samples, extracted from soil, collected from western and southern agricultural regions of Australia between 2001 and 2021 were sequenced using both PacBio full length reads (V1–V9, 1.5 kbp) and Illumina short‐reads (V3–V4, 200–450 bp). When ⍺‐diversity ratios were calculated between the long and short reads to assess DNA degradation, the ratio of ⍺‐diversity did not decrease in older samples versus younger samples. We suggest this as a novel method to confirm integrity of DNA before embarking on large‐scale diversity profiling projects using archival DNA.

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted U-Net architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences into BRAC, BRAF, and KRAS categories. Our comprehensive methodology includes rigorous data preprocessing, model training, and a multi-faceted evaluation approach. The adapted U-Net model exhibited exceptional performance, achieving an overall accuracy of 0.96. The model also achieved high precision and recall rates across the classes, with precision ranging from 0.93 to 1.00 and recall between 0.95 and 0.97 for the key markers BRAC, BRAF, and KRAS. The F1-score for these critical markers ranged from 0.95 to 0.98. These empirical results substantiate the architecture's capability to capture local and global features in DNA sequences, affirming its applicability for critical, sequence-based bioinformatics challenges.

DNA viruses that produce persistent infections have been proposed as potential causes for the extinction of Neanderthals, and, therefore, the identification of viral genome remnants in Neanderthal sequence reads is an initial step to address this hypothesis. Here, as proof of concept, we searched for viral remnants in sequence reads of Neanderthal genome data by mapping to adenovirus, herpesvirus and papillomavirus, which are double-stranded DNA viruses that may establish lifelong latency and can produce persistent infections. The reconstructed ancient viral genomes of adenovirus, herpesvirus and papillomavirus revealed conserved segments, with nucleotide identity to extant viral genomes and variable regions in coding regions with substantial divergence to extant close relatives. Sequence reads mapped to extant viral genomes showed deamination patterns of ancient DNA, and these ancient viral genomes showed divergence consistent with the age of these samples (≈50,000 years) and viral evolutionary rates (10−5 to 10−8 substitutions/site/year). Analysis of random effects showed that the Neanderthal mapping to genomes of extant persistent viruses is above what is expected by random similarities of short reads. Also, negative control with a nonpersistent DNA virus does not yield statistically significant assemblies. This work demonstrates the feasibility of identifying viral genome remnants in archaeological samples with signal-to-noise assessment. [ABSTRACT FROM AUTHOR]

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted U-Net architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences into BRAC, BRAF, and KRAS categories. Our comprehensive methodology includes rigorous data preprocessing, model training, and a multi-faceted evaluation approach. The adapted U-Net model exhibited exceptional performance, achieving an overall accuracy of 0.96. The model also achieved high precision and recall rates across the classes, with precision ranging from 0.93 to 1.00 and recall between 0.95 and 0.97 for the key markers BRAC, BRAF, and KRAS. The F1-score for these critical markers ranged from 0.95 to 0.98. These empirical results substantiate the architecture's capability to capture local and global features in DNA sequences, affirming its applicability for critical, sequence-based bioinformatics challenges. [ABSTRACT FROM AUTHOR]

Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator–prey studies of small‐sized animals (<1 mm) have met the problem of overabundant predator DNA in dietary samples by adding blocking primers/peptide nucleic acids. These primers aim to limit the PCR amplification and detection of the predator DNA but may introduce bias to the prey composition identified by interacting with sequences that are similar to those of the predator. Here we demonstrate the use of an alternative method to explore the prey of small marine copepods using whole‐body DNA extracts and deep, brute force metabarcoding of an 18S rDNA fragment. After processing and curating raw data from two sequencing runs of varying depths (0.4 and 5.4 billion raw reads), we isolated 1.3 and 52.2 million prey reads, with average depths of ~15,900 and ~120,000 prey reads per copepod individual, respectively. While data from both sequencing runs were sufficient to distinguish dietary compositions from disparate seasons, locations, and copepod species, greater sequencing depth led to better separation of clusters. As computation and sequencing are becoming ever more powerful and affordable, we expect the brute force approach to become a general standard for prey metabarcoding, as it offers a simple and affordable solution to consumers that is impractical to dissect or unknown to science. [ABSTRACT FROM AUTHOR]

Lithology identification is the research basis in oil and gas reservoir exploration and is critical for the formation characterization and reservoir development. Traditional lithofacies identification methods rely on the knowledge and experience of geologists and are usually done manually. With the development of deep learning technology and its application in the field of geophysics, lithofacies identification based on deep-learning approach has attracted great attention in recent years. Well logging data have obvious sequence characteristics. Therefore, we propose to use a bidirectional long and short-term memory (BiLSTM) neural network to learn long-term information for more effective lithology facies classification. In addition, we also perform correlation analysis on the input well logging curves and conduct median filter at different scales according to the correlation degree to extract the geological features within data itself and discard the interference of noise. The raw data-based lithofacies identification can reflect the noise resistance of the neural network model to some extent, while the filtered data are more beneficial for the model to extract the geological features correlated with lithofacies and provide accurate classification results. We validate our proposed framework by applying it to a case study from the Council Grove gas reservoir located in Kansas. Furthermore, we compare the effect of input data and network model on the identification results. The experimental results show that the proposed lithofacies identification method has higher classification accuracy. [ABSTRACT FROM AUTHOR]

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low-utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this article, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely, discovering high-utility sequential patterns with the seqPro structure (HUSPSP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns' utilities and upper-bound values. Furthermore, a new upper bound on utility, namely, tighter reduced sequence utility and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability. [ABSTRACT FROM AUTHOR]

Abstract Prey metabarcoding has become a popular tool in molecular ecology for resolving trophic interactions at high resolution, from various sample types and animals. To date, most predator–prey studies of small‐sized animals (

Cemetery soils most likely contain degradative bacteria which possibly have beneficial potencies. However, the bacterial exploration in these potencies is still limitedly conducted in Indonesia. The raw sequence data of total bacteria in the cemetery soils through metagenomic analysis have been revealed. The data were obtained by collecting soil samples from six spots of two major Cemetery areas, which were Pracimaloyo (P) and Bonoloyo (B), in Surakarta City, Central Java, Indonesia. The six sample spots consisted of two samples from P area with respectively 20 cm and 140 cm depths and four samples of each two samples from B area with 20 and 40 cm depths. The total DNA was subsequently extracted from the collected soils using ZymoBIOMICS DNA Miniprep Kit. The total DNA then was amplified using a couple of 16S rRNA primers through Illumina HiSeq 2500 PE250 (Novogen, Korea) environment system. The raw sequence data has been submitted to the National Center for Biotechnology Information (NCBI) with project ID PRJNA997385. The archived sequence can be accessed in the NCBI website with the following URLs https://www.ncbi.nlm.nih.gov/sra/PRJNA997385. A brief analysis of the sequence data showed that the most common phyla in 20 cm-depths were Proteobacteria (29.5%), Actinobacteria (21.6%), and Firmicutes (19.2%), while Actinobacteria were the most found in 140 cm-depths with 34.2% followed by Proteobacteria (21.9%) and Firmicutes (16.6%). This data would be the first report of total bacterial sequence from cemetery soils in Indonesia.

DNA viruses that produce persistent infections have been proposed as potential causes for the extinction of Neanderthals, and, therefore, the identification of viral genome remnants in Neanderthal sequence reads is an initial step to address this hypothesis. Here, as proof of concept, we searched for viral remnants in sequence reads of Neanderthal genome data by mapping to adenovirus, herpesvirus and papillomavirus, which are double-stranded DNA viruses that may establish lifelong latency and can produce persistent infections. The reconstructed ancient viral genomes of adenovirus, herpesvirus and papillomavirus revealed conserved segments, with nucleotide identity to extant viral genomes and variable regions in coding regions with substantial divergence to extant close relatives. Sequence reads mapped to extant viral genomes showed deamination patterns of ancient DNA, and these ancient viral genomes showed divergence consistent with the age of these samples (≈50,000 years) and viral evolutionary rates (10−5 to 10−8 substitutions/site/year). Analysis of random effects showed that the Neanderthal mapping to genomes of extant persistent viruses is above what is expected by random similarities of short reads. Also, negative control with a nonpersistent DNA virus does not yield statistically significant assemblies. This work demonstrates the feasibility of identifying viral genome remnants in archaeological samples with signal-to-noise assessment.

In this paper, we present a novel face-based random walk method aimed at addressing the 3D semantic segmentation issue. Our method utilizes a one-dimensional convolutional neural network for detailed feature extraction from sequences of triangular faces and employs a stacked gated recurrent unit to gather information along the sequence during training. This approach allows us to effectively handle irregular meshes and utilize the inherent feature extraction potential present in mesh geometry. Our study's results show that the proposed method achieves competitive results compared to the state-of-the-art methods in mesh segmentation. Importantly, it requires fewer training iterations and demonstrates versatility by applying to a wide range of objects without the need for the mesh to adhere to manifold or watertight topology requirements. • Segment 3D meshes using a hybrid architecture, gated recurrent units, and 1D convolutional neural networks. • Extraction of geometric features from 3D mesh faces using mathematical algorithms; random walks. • Apply deep learning algorithms to irregular 3D data without considering whether each mesh is a manifold or closed. [ABSTRACT FROM AUTHOR]

This paper introduces a video dataset for semantic segmentation of road potholes. This dataset contains 619 high-resolution videos captured in January 2023, covering locations in eight villages within the Hulu Sungai Tengah regency of South Kalimantan, Indonesia. The dataset is divided into three main folders, namely train, val, and test. The train, val, and test folders contain 372 videos for training, 124 videos for validation, and 123 videos for testing, respectively. Each of these main folders has two subfolders, ``RGB'' for the video in the RGB format and ``mask'' for the ground truth segmentation. These videos are precisely two seconds long, containing 48 frames each, and all are in MP4 format. The dataset offers remarkable flexibility, accommodating various research needs, from full-video segmentation to frame extraction. It enables researchers to create ground truth annotations and change the combination of videos in the folders according to their needs. This resource is an asset for researchers, engineers, policymakers, and anyone interested in advancing algorithms for pothole detection and analysis. This dataset allows for benchmarking semantic segmentation algorithms, conducting comparative studies on pothole detection methods, and exploring innovative approaches, offering valuable contributions to the computer vision community., (© 2024 The Author(s).)