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1. Improved contiguity of the threespine stickleback genome using long-read sequencing

   https://doi.org/10.1093/g3journal/jkab007  

   Nath, Shivangi; Shaw, Daniel E; White, Michael A (February 2021, G3 Genes|Genomes|Genetics)

   Macqueen, D (Ed.)

   Abstract While the cost and time for assembling a genome has drastically decreased, it still remains a challenge to assemble a highly contiguous genome. These challenges are rapidly being overcome by the integration of long-read sequencing technologies. Here, we use long-read sequencing to improve the contiguity of the threespine stickleback fish (Gasterosteus aculeatus) genome, a prominent genetic model species. Using Pacific Biosciences sequencing, we assembled a highly contiguous genome of a freshwater fish from Paxton Lake. Using contigs from this genome, we were able to fill over 76.7% of the gaps in the existing reference genome assembly, improving contiguity over fivefold. Our gap filling approach was highly accurate, validated by 10X Genomics long-distance linked-reads. In addition to closing a majority of gaps, we were able to assemble segments of telomeres and centromeres throughout the genome. This highlights the power of using long sequencing reads to assemble highly repetitive and difficult to assemble regions of genomes. This latest genome build has been released through a newly designed community genome browser that aims to consolidate the growing number of genomics datasets available for the threespine stickleback fish. 

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2. Sketching and sampling approaches for fast and accurate long read classification

   https://doi.org/10.1186/s12859-022-05014-0  

   Das, Arun; Schatz, Michael C. (December 2022, BMC Bioinformatics)

   Abstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read. Results Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy. Conclusions The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching . 

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3. Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors

   https://doi.org/10.3390/biom13060934  

   Sahoo, Bikram; Ali, Sarwan; Chen, Pin-Yu; Patterson, Murray; Zelikovsky, Alexander (June 2023, Biomolecules)

   The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-19 pandemic. Although long-read sequencing has been crucial in understanding the evolution and transmission of the virus, the high error rate associated with these reads can lead to inadequate genome assembly and downstream biological interpretation. In this study, we evaluate the accuracy and robustness of machine learning (ML) models using six different embedding techniques on SARS-CoV-2 error-incorporated genome sequences. Our analysis includes two types of error-incorporated genome sequences: those generated using simulation tools to emulate error profiles of long-read sequencing platforms and those generated by introducing random errors. We show that the spaced k-mers embedding method achieves high accuracy in classifying error-free SARS-CoV-2 genome sequences, and the spaced k-mers and weighted k-mers embedding methods are highly accurate in predicting error-incorporated sequences. The fixed-length vectors generated by these methods contribute to the high accuracy achieved. Our study provides valuable insights for researchers to effectively evaluate ML models and gain a better understanding of the approach for accurate identification of critical SARS-CoV-2 genome sequences. 

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4. Technology dictates algorithms: recent developments in read alignment

   https://doi.org/10.1186/s13059-021-02443-7  

   Alser, Mohammed; Rotman, Jeremy; Deshpande, Dhrithi; Taraszka, Kodi; Shi, Huwenbo; Baykal, Pelin Icer; Yang, Harry Taegyun; Xue, Victor; Knyazev, Sergey; Singer, Benjamin D.; et al (December 2021, Genome Biology)

   Abstract Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today’s diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology. 

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5. Long Reads Are Revolutionizing 20 Years of Insect Genome Sequencing

   https://doi.org/10.1093/gbe/evab138  

   Hotaling, Scott; Sproul, John S; Heckenhauer, Jacqueline; Powell, Ashlyn; Larracuente, Amanda M; Pauls, Steffen U; Kelley, Joanna L; Frandsen, Paul B (August 2021, Genome Biology and Evolution)

   Hoffmann, Federico (Ed.)

   Abstract The first insect genome assembly (Drosophila melanogaster) was published two decades ago. Today, nuclear genome assemblies are available for a staggering 601 insect species representing 20 orders. In this study, we analyzed the most-contiguous assembly for each species and provide a “state-of-the-field” perspective, emphasizing taxonomic representation, assembly quality, gene completeness, and sequencing technologies. Relative to species richness, genomic efforts have been biased toward four orders (Diptera, Hymenoptera, Collembola, and Phasmatodea), Coleoptera are underrepresented, and 11 orders still lack a publicly available genome assembly. The average insect genome assembly is 439.2 Mb in length with 87.5% of single-copy benchmarking genes intact. Most notable has been the impact of long-read sequencing; assemblies that incorporate long reads are ∼48× more contiguous than those that do not. We offer four recommendations as we collectively continue building insect genome resources: 1) seek better integration between independent research groups and consortia, 2) balance future sampling between filling taxonomic gaps and generating data for targeted questions, 3) take advantage of long-read sequencing technologies, and 4) expand and improve gene annotations. 

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