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1. Minnow : a principled framework for rapid simulation of dscRNA-seq data at the read level

   https://doi.org/10.1093/bioinformatics/btz351  

   Sarkar, Hirak ; Srivastava, Avi ; Patro, Rob ( July 2019 , Bioinformatics)

   With the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data that results from such studies. For example, there exist methods for pseudo-time series analysis, differential cell usage, cell-type detection RNA-velocity in single cells, etc. Most analysis pipelines validate their results using known marker genes (which are not widely available for all types of analysis) and by using simulated data from gene-count-level simulators. Typically, the impact of using different read-alignment or unique molecular identifier (UMI) deduplication methods has not been widely explored. Assessments based on simulation tend to start at the level of assuming a simulated count matrix, ignoring the effect that different approaches for resolving UMI counts from the raw read data may produce. Here, we present minnow, a comprehensive sequence-level droplet-based single-cell RNA-sequencing (dscRNA-seq) experiment simulation framework. Minnow accounts for important sequence-level characteristics of experimental scRNA-seq datasets and models effects such as polymerase chain reaction amplification, cellular barcodes (CB) and UMI selection and sequence fragmentation and sequencing. It also closely matches the gene-level ambiguity characteristics that are observed in real scRNA-seq experiments. Using minnow, we explore the performance of some common processing pipelines to produce gene-by-cell count matrices from droplet-bases scRNA-seq data, demonstrate the effect that realistic levels of gene-level sequence ambiguity can have on accurate quantification and show a typical use-case of minnow in assessing the output generated by different quantification pipelines on the simulated experiment.

   Supplementary data are available at Bioinformatics online.

    

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2. A Fourier-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

   https://doi.org/10.1109/BigDataCongress.2018.00024  

   Aledhari, Mohammed ; Di Pierro, Marianne ; Saeed, Fahad ( July 2018 , IEEE International Congress on Big Data (BigData Congress))

   DNA sequencing plays an important role in the bioinformatics research community. DNA sequencing is important to all organisms, especially to humans and from multiple perspectives. These include understanding the correlation of specific mutations that plays a significant role in increasing or decreasing the risks of developing a disease or condition, or finding the implications and connections between the genotype and the phenotype. Advancements in the high-throughput sequencing techniques, tools, and equipment, have helped to generate big genomic datasets due to the tremendous decrease in the DNA sequence costs. However, the advancements have posed great challenges to genomic data storage, analysis, and transfer. Accessing, manipulating, and sharing the generated big genomic datasets present major challenges in terms of time and size, as well as privacy. Data size plays an important role in addressing these challenges. Accordingly, data minimization techniques have recently attracted much interest in the bioinformatics research community. Therefore, it is critical to develop new ways to minimize the data size. This paper presents a new real-time data minimization mechanism of big genomic datasets to shorten the transfer time in a more secure manner, despite the potential occurrence of a data breach. Our method involves the application of the random sampling of Fourier transform theory to the real-time generated big genomic datasets of both formats: FASTA and FASTQ and assigns the lowest possible codeword to the most frequent characters of the datasets. Our results indicate that the proposed data minimization algorithm is up to 79% of FASTA datasets' size reduction, with 98-fold faster and more secure than the standard data-encoding method. Also, the results show up to 45% of FASTQ datasets' size reduction with 57-fold faster than the standard data-encoding approach. Based on our results, we conclude that the proposed data minimization algorithm provides the best performance among current data-encoding approaches for big real-time generated genomic datasets. 

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3. Maximizing Molecular Data From Low-Quality Fluid-Preserved Specimens in Natural History Collections

   https://doi.org/10.3389/fevo.2022.893088  

   Bernstein, Justin M. ; Ruane, Sara ( June 2022 , Frontiers in Ecology and Evolution)

   Over the past decade, museum genomics studies have focused on obtaining DNA of sufficient quality and quantity for sequencing from fluid-preserved natural history specimens, primarily to be used in systematic studies. While these studies have opened windows to evolutionary and biodiversity knowledge of many species worldwide, published works often focus on the success of these DNA sequencing efforts, which is undoubtedly less common than obtaining minimal or sometimes no DNA or unusable sequence data from specimens in natural history collections. Here, we attempt to obtain and sequence DNA extracts from 115 fresh and 41 degraded samples of homalopsid snakes, as well as from two degraded samples of a poorly known snake, Hydrablabes periops . Hydrablabes has been suggested to belong to at least two different families (Natricidae and Homalopsidae) and with no fresh tissues known to be available, intractable museum specimens currently provide the only opportunity to determine this snake’s taxonomic affinity. Although our aim was to generate a target-capture dataset for these samples, to be included in a broader phylogenetic study, results were less than ideal due to large amounts of missing data, especially using the same downstream methods as with standard, high-quality samples. However, rather than discount results entirely, we used mapping methods with references and pseudoreferences, along with phylogenetic analyses, to maximize any usable molecular data from our sequencing efforts, identify the taxonomic affinity of H. periops , and compare sequencing success between fresh and degraded tissue samples. This resulted in largely complete mitochondrial genomes for five specimens and hundreds to thousands of nuclear loci (ultra-conserved loci, anchored-hybrid enrichment loci, and a variety of loci frequently used in squamate phylogenetic studies) from fluid-preserved snakes, including a specimen of H. periops from the Field Museum of Natural History collection. We combined our H. periops data with previously published genomic and Sanger-sequenced datasets to confirm the familial designation of this taxon, reject previous taxonomic hypotheses, and make biogeographic inferences for Hydrablabes . A second H. periops specimen, despite being seemingly similar for initial raw sequencing results and after being put through the same protocols, resulted in little usable molecular data. We discuss the successes and failures of using different pipelines and methods to maximize the products from these data and provide expectations for others who are looking to use DNA sequencing efforts on specimens that likely have degraded DNA. Life Science Identifier ( Hydrablabes periops ) urn:lsid:zoobank.org :pub:F2AA44 E2-D2EF-4747-972A-652C34C2C09D. 

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4. A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets

   https://doi.org/10.1109/TBDATA.2018.2805687  

   Aledhari, Mohammed ; Di Pierro, Marianne ; Hefeida, Mohamed ; Saeed, Fahad ( April 2019 , IEEE Transactions on Big Data)

   In the age of Big Genomics Data, institutions such as the National Human Genome Research Institute (NHGRI) are challenged in their efforts to share volumes of data between researchers, a process that has been plagued by unreliable transfers and slow speeds. These occur due to throughput bottlenecks of traditional transfer technologies. Two factors that affect the effciency of data transmission are the channel bandwidth and the amount of data. Increasing the bandwidth is one way to transmit data effciently, but might not always be possible due to resource limitations. Another way to maximize channel utilization is by decreasing the bits needed for transmission of a dataset. Traditionally, transmission of big genomic data between two geographical locations is done using general-purpose protocols, such as hypertext transfer protocol (HTTP) and file transfer protocol (FTP) secure. In this paper, we present a novel deep learning-based data minimization algorithm that 1) minimizes the datasets during transfer over the carrier channels; 2) protects the data from the man-in-the-middle (MITM) and other attacks by changing the binary representation (content-encoding) several times for the same dataset: we assign different codewords to the same character in different parts of the dataset. Our data minimization strategy exploits the alphabet limitation of DNA sequences and modifies the binary representation (codeword) of dataset characters using deep learning-based convolutional neural network (CNN) to ensure a minimum of code word uses to the high frequency characters at different time slots during the transfer time. This algorithm ensures transmission of big genomic DNA datasets with minimal bits and latency and yields an effcient and expedient process. Our tested heuristic model, simulation, and real implementation results indicate that the proposed data minimization algorithm is up to 99 times faster and more secure than the currently used content-encoding scheme used in HTTP of the HTTP content-encoding scheme and 96 times faster than FTP on tested datasets. The developed protocol in C# will be available to the wider genomics community and domain scientists. 

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5. Evaluation of the Effects of Library Preparation Procedure and Sample Characteristics on the Accuracy of Metagenomic Profiles

   https://doi.org/10.1128/mSystems.00440-21  

   Gaulke, Christopher A. ; Schmeltzer, Emily R. ; Dasenko, Mark ; Tyler, Brett M. ; Vega Thurber, Rebecca ; Sharpton, Thomas J. ( October 2021 , mSystems)

   David, Lawrence A. (Ed.)

   ABSTRACT Shotgun metagenomic sequencing has transformed our understanding of microbial community ecology. However, preparing metagenomic libraries for high-throughput DNA sequencing remains a costly, labor-intensive, and time-consuming procedure, which in turn limits the utility of metagenomes. Several library preparation procedures have recently been developed to offset these costs, but it is unclear how these newer procedures compare to current standards in the field. In particular, it is not clear if all such procedures perform equally well across different types of microbial communities or if features of the biological samples being processed (e.g., DNA amount) impact the accuracy of the approach. To address these questions, we assessed how five different shotgun DNA sequence library preparation methods, including the commonly used Nextera Flex kit, perform when applied to metagenomic DNA. We measured each method’s ability to produce metagenomic data that accurately represent the underlying taxonomic and genetic diversity of the community. We performed these analyses across a range of microbial community types (e.g., soil, coral associated, and mouse gut associated) and input DNA amounts. We find that the type of community and amount of input DNA influence each method’s performance, indicating that careful consideration may be needed when selecting between methods, especially for low-complexity communities. However, the cost-effective preparation methods that we assessed are generally comparable to the current gold-standard Nextera DNA Flex kit for high-complexity communities. Overall, the results from this analysis will help expand and even facilitate access to metagenomic approaches in future studies. IMPORTANCE Metagenomic library preparation methods and sequencing technologies continue to advance rapidly, allowing researchers to characterize microbial communities in previously underexplored environmental samples and systems. However, widely accepted standardized library preparation methods can be cost-prohibitive. Newly available approaches may be less expensive, but their efficacy in comparison to standardized methods remains unknown. In this study, we compared five different metagenomic library preparation methods. We evaluated each method across a range of microbial communities varying in complexity and quantity of input DNA. Our findings demonstrate the importance of considering sample properties, including community type, composition, and DNA amount, when choosing the most appropriate metagenomic library preparation method. 

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