Read Mapping Research Articles

Efficient Seeding for Error-Prone Sequences with SubseqHash2.

Seeding is an essential preparatory step for large-scale sequence comparisons. Substring-based seeding methods such as kmers are ideal for sequences with low error rates but struggle to achieve high sensitivity while maintaining a reasonable precision for error-prone long reads. SubseqHash, a novel subsequence-based seeding method we recently developed, achieves superior accuracy to substring-based methods in seeding sequences with high mutation/error rates, while the only drawback is its computation speed. In this paper, we propose SubseqHash2, an improved algorithm that can compute multiple sets of seeds in one run by defining orders over all length- subsequences and identifying the optimal subsequence under each of the orders in a single dynamic programming framework. The algorithm is further accelerated using SIMD instructions. SubseqHash2 achieves a 10-50× speedup over repeating SubseqHash while maintaining the high accuracy of seeds. We demonstrate that SubseqHash2 drastically outperforms popular substring-based methods including kmers, minimizers, syncmers, and Strobemers for three fundamental applications. In read mapping, SubseqHash2 can generate adequate seed-matches for aligning hard reads that minimap2 fails on. In sequence alignment, SubseqHash2 achieves high coverage of correct seeds and low coverage of incorrect seeds. In overlap detection, seeds produced by SubseqHash2 lead to more correct overlapping pairs at the same false-positive rate. With all the algorithmic breakthroughs of SubseqHash2, we clear the path for the wide adoption of subsequence-based seeds in long-read analysis. SubseqHash2 is available at https://github.com/Shao-Group/SubseqHash2.

GEMA: A Genome Exact Mapping Accelerator Based on Learned Indexes.

In this article, we introduce GEMA, a genome exact mapping accelerator based on learned indexes, specifically designed for FPGA implementation. GEMA utilizes a machine learning (ML) algorithm to precisely locate the exact position of read sequences within the original sequence. To enhance the accuracy of the trained ML model, we incorporate data augmentation and data-distribution-aware partitioning techniques. Additionally, we present an efficient yet low-overhead error recovery technique. To map long reads more efficiently, we propose a speculative prefetching approach, which reduces the required memory bandwidth. Furthermore, we suggest an FPGA-based architecture for implementing the proposed mapping accelerator, optimizing the accesses to off-chip memory. Our studies demonstrate that GEMA achieves up to 1.36 × higher speed for short reads compared to the corresponding results reported in recently published exact mapping accelerators. Moreover, GEMA achieves up to ∼22 × faster mapping of long reads compared to the available results for the longest mapped reads using these accelerators.

Genomic characterisation and ecological distribution of Mantoniella tinhauana: a novel Mamiellophycean green alga from the Western Pacific.

Mamiellophyceae are dominant marine algae in much of the ocean, the most prevalent genera belonging to the order Mamiellales: Micromonas, Ostreococcus and Bathycoccus, whose genetics and global distributions have been extensively studied. Conversely, the genus Mantoniella, despite its potential ecological importance, remains relatively under-characterised. In this study, we isolated and characterised a novel species of Mamiellophyceae, Mantoniella tinhauana, from subtropical coastal waters in the South China Sea. Morphologically, it resembles other Mantoniella species; however, a comparative analysis of the 18S and ITS2 marker genes revealed its genetic distinctiveness. Furthermore, we sequenced and assembled the first genome of Mantoniella tinhauana, uncovering significant differences from previously studied Mamiellophyceae species. Notably, the genome lacked any detectable outlier chromosomes and exhibited numerous unique orthogroups. We explored gene groups associated with meiosis, scale and flagella formation, shedding light on species divergence, yet further investigation is warranted. To elucidate the biogeography of Mantoniella tinhauana, we conducted a comprehensive analysis using global metagenomic read mapping to the newly sequenced genome. Our findings indicate this species exhibits a cosmopolitan distribution with a low-level prevalence worldwide. Understanding the intricate dynamics between Mamiellophyceae and the environment is crucial for comprehending their impact on the ocean ecosystem and accurately predicting their response to forthcoming environmental changes.

ESKEMAP: exact sketch-based read mapping

BackgroundGiven a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a “similar sequence”. Traditionally, “similar sequence” was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold.ResultsIn this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in O(|t|+|p|+ℓ2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O} (|t| + |p| + \\ell ^2)$$\\end{document} time and O(ℓlogℓ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O} (\\ell \\log \\ell )$$\\end{document} space, where |t| is the number of k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers inside the sketch of the reference, |p| is the number of k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers inside the read’s sketch and ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell$$\\end{document} is the number of times that k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.

Determining optimal surveillance techniques for the microbial community on human skin using RNAseq data analysis

Background: The largest organ in the human body, the skin, serves as a physical barrier to the outside world. It also houses millions of bacteria, viruses, and fungi that protect us against invading pathogens or result in altered physiology and disease. These microorganisms can contribute to normal skin health or predispose individuals to pathogenic disorders. Surveillance and observation of the microbiome can give insight into identifying common taxa that compose our skin and indications of new or changing pathogens. However, to do this effciently, it is important to understand what can be detected from skin samples and the differences we may see using various collection methods. Methods: We used publicly available NCBI SRA RNAseq bioprojects that queried “skin,” processing 132 bioprojects with 8,274 samples. Using FastQC, Salmon, KRAKEN2, and MiCXR/Immunarch, we aimed to analyze the quality, human gene transcripts, infections, and B/T- cell responses, respectively. We aimed to identify what skin sampling method provides the best data on the skin microbiome, human gene mapping, and B/T-cell immune repertoire. This project is based on observational methods, not a specific hypothesis. Results: From the KRAKEN2 data, there were 14,383 viruses and 14,691 bacteria assessed through all “skin” BioProjects. From the extensive data pool, we identified unique BioProject signatures for protest (Leishmania), bacteria (Mycoplasma), and viruses (HSV1 dose dependencies, HPV in human warts, Chinese transmitted tick viruses, Human T-cell lymphotropic virus, and the first-ever human case of equine infectious anemia virus). The use of read mapping and coverage is critical to confirming the presence of foreign species, noted through the observation of T4 DNA ligase RNA presence prior to 2018 RNA sequencing. Once pathogens were detected, cellular response mapping was developed, yielding insights into the pathophysiology of multiple viral infections of the skin and sex differences. Conclusion: Detecting a wide variety of microorganisms on the skin can give insight into dermatological pathologies and skin physiology. Using the publicly available skin datasets, we see many unique stories of microorganisms in the skin. This big data mapping strategy highlights the importance of broad observational mapping, possible with cutting-edge sequencing tools. This work was supported by the MI-SAPPHIRE program of MDHHS and the CDC. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

GateKeeper-GPU: Fast and Accurate Pre-Alignment Filtering in Short Read Mapping

GateKeeper-GPU: Fast and Accurate Pre-Alignment Filtering in Short Read Mapping

RecGraph: recombination-aware alignment of sequences to variation graphs.

Bacterial genomes present more variability than human genomes, which requires important adjustments in computational tools that are developed for human data. In particular, bacteria exhibit a mosaic structure due to homologous recombinations, but this fact is not sufficiently captured by standard read mappers that align against linear reference genomes. The recent introduction of pangenomics provides some insights in that context, as a pangenome graph can represent the variability within a species. However, the concept of sequence-to-graph alignment that captures the presence of recombinations has not been previously investigated. In this paper, we present the extension of the notion of sequence-to-graph alignment to a variation graph that incorporates a recombination, so that the latter are explicitly represented and evaluated in an alignment. Moreover, we present a dynamic programming approach for the special case where there is at most a recombination-we implement this case as RecGraph. From a modelling point of view, a recombination corresponds to identifying a new path of the variation graph, where the new arc is composed of two halves, each extracted from an original path, possibly joined by a new arc. Our experiments show that RecGraph accurately aligns simulated recombinant bacterial sequences that have at most a recombination, providing evidence for the presence of recombination events. Our implementation is open source and available at https://github.com/AlgoLab/RecGraph.

Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics.

Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.

Map Reading and Analysis with GPT-4V(ision)

In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore reading and analyzing maps with the latest version of GPT-4-vision-preview (GPT-4V), to fully evaluate its advantages and disadvantages in comparison with human eye-based visual inspections. We found that GPT-4V is able to properly retrieve information from various types of maps in different scales and spatiotemporal resolutions. GPT-4V can also perform basic map analysis, such as identifying visual changes before and after a natural disaster. It has the potential to replace human efforts by examining batches of maps, accurately extracting information from maps, and linking observed patterns with its pre-trained large dataset. However, it is encumbered by limitations such as diminished accuracy in visual content extraction and a lack of validation. This paper sets an example of effectively using GPT-4V for map reading and analytical tasks, which is a promising application for large multimodal models, large language models, and artificial intelligence.

GenArchBench: A genomics benchmark suite for arm HPC processors

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE).This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

Identification of two female-specific DNA markers in Verasper variegatus

Sex-specific DNA markers are powerful tools for genetic sex identification and sex control breeding of fish. Spotted halibut (Verasper variegatus) is a kind of marine flatfish with important nutritional and economic value, which exhibits significant sex-related growth dimorphism that the female individuals grow faster than the male individuals. However, there is no rapid, effective and universal sex-specific DNA marker used for genetic sex identification and sex control breeding in spotted halibut up to date. In the present study, whole genome resequencing was performed on 17 male and 17 female spotted halibut individuals to identify sex-specific markers. A total of 325.36 Gb and 328.99 Gb data were obtained from females and males, respectively. After reads mapping, sequencing depth and coverage analysis, 359 potential female-specific DNA sequences were identified. Then, after two rounds of verification, two female-specific 66 bp and 68 bp insertions were identified as female-specific markers which can accurately identify the genetic sex of spotted halibut through PCR amplification and 2% agarose gel electrophoresis. Subsequently, these two female-specific DNA markers and the PCR method were further validated using 117 spotted halibut individuals from another breeding population to confirm their genetic sex, which was in accordance with their phenotypic sex determined by histological section of the gonads. This may be the first two rapid, accurate and effective female-specific DNA markers used for genetic sex identification in spotted halibut. The identification of these two female-specific DNA markers will not only provide important genetic evidence to elucidate the sex determination mechanism of spotted halibut, but also help to advance the process of sex control breeding in spotted halibut.

Ocrelizumab B cell depletion has no effect on HERV RNA expression in PBMC in MS patients

BackgroundEpstein barr virus (EBV) infection of B cells is now understood to be one of the triggering events for the development of Multiple Sclerosis (MS), a progressive immune-mediated disease of the central nervous system. EBV infection is also linked to expression of human endogenous retroviruses (HERVs) of the HERV-W group, a further risk factor for the development of MS. Ocrelizumab is a high-potency disease-modifying treatment (DMT) for MS, which depletes B cells by targeting CD20. ObjectivesWe studied the effects of ocrelizumab on gene expression in peripheral blood mononuclear cells (PBMC) from paired samples from 20 patients taken prior to and 6 months after beginning ocrelizumab therapy. We hypothesised that EBV and HERV-W loads would be lower in post-treatment samples. MethodsSamples were collected in Paxgene tubes, subject to RNA extraction and Illumina paired end short read mRNA sequencing with mapping of sequence reads to the human genome using Salmon and differential gene expression compared with DeSeq2. Mapping was also performed separately to the HERV-D database of HERV sequences and the EBV reference sequence. ResultsPatient samples were more strongly clustered by individual rather than disease type (relapsing/remitting or primary progressive), treatment (pre and post), age, or sex. Fourteen genes, all clearly linked to B cell function were significantly down regulated in the post treatment samples. Interestingly only one pre-treatment sample had detectable EBV RNA and there were no significant differences in HERV expression (of any group) between pre- and post-treatment samples. ConclusionsWhile EBV and HERV expression are clearly linked to triggering MS pathogenesis, it does not appear that high level expression of these viruses is a part of the ongoing disease process or that changes in virus load are associated with ocrelizumab treatment.

Designing efficient randstrobes for sequence similarity analyses.

Substrings of length k, commonly referred to as k-mers, play a vital role in sequence analysis. However, k-mers are limited to exact matches between sequences leading to alternative constructs. We recently introduced a class of new constructs, strobemers, that can match across substitutions and smaller insertions and deletions. Randstrobes, the most sensitive strobemer proposed in Sahlin (Effective sequence similarity detection with strobemers. Genome Res 2021a;31:2080-94. https://doi.org/10.1101/gr.275648.121), has been used in several bioinformatics applications such as read classification, short-read mapping, and read overlap detection. Recently, we showed that the more pseudo-random the behavior of the construction (measured in entropy), the more efficient the seeds for sequence similarity analysis. The level of pseudo-randomness depends on the construction operators, but no study has investigated the efficacy. In this study, we introduce novel construction methods, including a Binary Search Tree-based approach that improves time complexity over previous methods. To our knowledge, we are also the first to address biases in construction and design three metrics for measuring bias. Our evaluation shows that our methods have favorable speed and sampling uniformity compared to existing approaches. Lastly, guided by our results, we change the seed construction in strobealign, a short-read mapper, and find that the results change substantially. We suggest combining the two results to improve strobealign's accuracy for the shortest reads in our evaluated datasets. Our evaluation highlights sampling biases that can occur and provides guidance on which operators to use when implementing randstrobes. All methods and evaluation benchmarks are available in a public Github repository at https://github.com/Moein-Karami/RandStrobes. The scripts for running the strobealign analysis are found at https://github.com/NBISweden/strobealign-evaluation.

Ефективність методики навчання спортивному орієнтуванню курсантів спеціальності артилерійська розвідка з урахуванням змагальної діяльності спортсменів-орієнтувальників

Success in a modern fast-paced and maneuverable battle requires the use of high-precision weapons, the effectiveness of which is possible with the use of satellite navigation systems, unmanned aerial vehicles, etc. At the same time, the experience of combat operations on the territory of Ukraine has shown that even if they cannot be used, and sometimes even in their absence, the ability to navigate with a conventional map is an important condition for the successful completion of a combat mission. The purpose of the study is to experimentally investigate the effectiveness of the methodology for teaching orienteering to cadets majoring in artillery intelligence, taking into account the requirements of competitive activity in the conditions of real training and competitive process of orienteering athletes. Material and methods of the research. To solve the tasks, we organized and conducted a pedagogical observation to determine the arsenal of means and methods of physical training of orienteering cadets in the process of studying at a military university and a pedagogical experiment to test the effectiveness of the physical training program for cadets majoring in artillery intelligence. There were 20 respondents, including 10 men of the artillery intelligence specialty with a minimum level of orienteering training (EO) and 10 male athletes who have been involved in orienteering for 2–3 years. The respondents are 18-25 years old (average age – 20.8±0.2). Results. It has been established that during the experiment there was a significant increase in the cadets’ fitness indicators compared to the beginning of the study. At the end of the study, the level of training of artillery cadets differs from athletes of the KMU and MSU level at a distance (sprint in the city by 28.9 %; sprint in the park by 37.7 %; extended distance in the forest by 26.4 %). Conclusions. The conducted research and analysis of the results showed that the level of preparedness of cadets specializing in artillery reconnaissance was significantly reduced compared to the beginning of the training process. At the end of the experiment, the training process was aimed more at developing endurance than at developing speed, which is important for performing tasks in real combat operations, and the cadets’ technical skills in map reading, which is necessary for their specialty of artillery reconnaissance, increased.

Large Language Models Are Neurosymbolic Reasoners

A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.

Abstract 3489: MetFlow: Automated nextflow pipeline for Ion AmpliSeq methylation assay designs

Abstract Introduction: DNA methylation plays a crucial role in development and progression of cancer. Identification of DNA methylation profiles in cancer is indispensable for early detection. To address this research need, Ion AmpliSeqTM offers targeted next-generation sequencing (NGS) methylation community amplicon panels for DNA methylation profiling in cancer-associated genes. However, the process of designing custom methylation amplicon panels using the Ion AmpliSeq primer design pipeline is intricate, labor-intensive, and time-consuming. Here, we developed a one-step, fully automated, faster, and reproducible nextflow-based primer design pipeline (MetFlow) for development of custom Ion AmpliSeqTM methylation panels. MetFlow streamlines the development of custom methylation panels for cancer-related genes, and thus significantly reduces the time and effort traditionally required for amplicon design. Methods: The fully automated MetFlow pipeline is implemented using Nextflow. MetFlow necessitate an input configuration file for the execution of methylation Ion AmpliSeqTM designs. The configuration file specifies the essential parameters, including target CpG input, reference genome, pool number, and amplicon lengths, which are required for designing the amplicons. The MetFlow pipeline designs candidate amplicons for CpG targets on both Watson and Crick strands, facilitating the subsequent creation of files required for the generation of a methylation panel. Results: The fully automated MetFlow pipeline has been used for designing the customized Ion AmpliSeqTM methylation amplicon panel(s) for cancer and age-related genes. The amplicons lengths in these AmpliSeqTM methylation panels were ranged from 125 bp to 175 bp for formalin-fixed, paraffin-embedded (FFPE) research samples. These customized methylation panels were run on the Ion GeneStudioTM S5 sequencer followed by performance analysis using Ion TorrentTM Suite. The published data for these custom amplicon panels suggest that the Ion AmpliSeqTM has sufficient read depth and mapping efficiency, higher sensitivity and accuracy for methylation measurement, and strong correlation (r=0.993) for methylation profiling among sample replicates. Conclusion: We present MetFlow, an advanced automated primer design pipeline for designing the custom Ion AmpliSeqTM methylation amplicon panels tailored to targeted NGS applications in cancer research. The custom designed Ion AmpliSeqTM methylation panels exhibited sufficient read depth, higher sensitivity and accuracy, and reproducibility. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Renesh Bedre, Drishti Kaul, Anupma Sharma, Andrew Hatch, Loni Pickle, Na Li, Fiona Hyland. MetFlow: Automated nextflow pipeline for Ion AmpliSeq methylation assay designs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3489.

Abstract 326: Matched fresh frozen and FFPE patient tissues reveal the enhanced sensitivity and data quality of a novel DNA library prep method

Abstract In cancer genomics, a common source of DNA is formalin-fixed, paraffin-embedded (FFPE) tissue from patient surgical samples, where in most cases high quality fresh or frozen tissue samples are not available. FFPE DNA poses many notable challenges for preparing NGS libraries, including low input amounts and highly variable damage from fixation, storage, and extraction methods. It is difficult to obtain libraries with sufficient coverage and the sequencing artifacts arising from damaged DNA bases confound somatic variant detection. Additionally, many laboratories process FFPE tumor samples alongside matched, high quality, normal DNA and many library prep workflows are not readily compatible with both sample types. We developed a novel NGS library prep method compatible with both high quality and very low quality FFPE DNA samples, employing a novel enzymatic DNA repair mix, enzymatic fragmentation mix, and PCR master mix. To validate this workflow on real patient sample sets, we obtained DNA extracted from matched tumor and normal tissue of various tissue types preserved by both fresh frozen and FFPE methods. The FFPE DNA samples ranged in quality from DNA integrity number (DIN) 1.5 to 6.8, including samples stored for nearly 3 decades. We prepared libraries using this method compared against other library prep workflows and sequenced them by WGS and target capture. We assessed the performance of these workflows by library yield, library quality metrics, depth and evenness of coverage, and somatic variant calling with fresh frozen-extracted DNA providing the gold standard for library quality and mutation content. This new enzymatic fragmentation-based library prep workflow not only reduced the false positive rate in somatic variant detection by repairing damage-derived mutations in FFPE DNA samples but also improved the library yield, library quality metrics (including mapping, chimeras, and properly-paired reads), library complexity, coverage depth and uniformity, and hybrid capture library quality metrics. Comparing the variant calls from matched FFPE and frozen tissues revealed an improved sensitivity and accuracy of variant calling using this library prep method compared to mechanical shearing and other enzymatic fragmentation library prep approaches. This new suite of enzyme mixes allows even highly damaged FFPE samples to achieve high quality libraries with a greater sensitivity for somatic variant identification. The workflow is robust and flexible, compatible with both FFPE DNA and matched high quality DNA samples as well as automation-friendly for convenience in sample processing. Citation Format: Margaret R. Heider, Jian Sun, Brittany Sexton, Bradley W. Langhorst, Laura Blum, Pingfang Liu. Matched fresh frozen and FFPE patient tissues reveal the enhanced sensitivity and data quality of a novel DNA library prep method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 326.

Updated single cell reference atlas for the starlet anemone Nematostella vectensis.

The recent combination of genomics and single cell transcriptomics has allowed to assess a variety of non-conventional model organisms in much more depth. Single cell transcriptomes can uncover hidden cellular complexity and cell lineage relationships within organisms. The recent developmental cell atlases of the sea anemone Nematostella vectensis, a representative of the basally branching Cnidaria, has provided new insights into the development of all cell types (Steger et al Cell Rep 40(12):111370, 2022; Sebé-Pedrós et al. Cell 173(6):1520-1534.e20). However, the mapping of the single cell reads still suffers from relatively poor gene annotations and a draft genome consisting of many scaffolds. Here we present a new wildtype resource of the developmental single cell atlas, by re-mapping of sequence data first published in Steger et al. (2022) and Cole et al. (Nat Commun 14(1):1747, 2023), to the new chromosome-level genome assembly and corresponding gene models in Zimmermann et al. (Nat Commun 14, 8270 (2023). https://doi.org/10.1038/s41467-023-44080-7 ). We expand the pre-existing dataset through the incorporation of additional sequence data derived from the capture and sequencing of cell suspensions from four additional samples: 24h gastrula, 2d planula, an inter-parietal region of the bodywall from a young unsexed animal, and another adult mesentery from a mature male animal. Our analyses of the full cell-state inventory provide transcriptomic signatures for 127 distinct cell states, of which 47 correspond to neuroglandular subtypes. We also identify two distinct putatively immune-related transcriptomic profiles that segregate between the inner and outer cell layers. Furthermore, the new gene annotation Nv2 has markedly improved the mapping on the single cell transcriptome data and will therefore be of great value for the community and anyone using the dataset.

ScPathoQuant: a tool for efficient alignment and quantification of pathogen sequence reads from 10× single cell sequencing datasets.

Currently there is a lack of efficient computational pipelines/tools for conducting simultaneous genome mapping of pathogen-derived and host reads from single cell RNA sequencing (scRNAseq) output from pathogen-infected cells. Contemporary options include processes involving multiple steps and/or running multiple computational tools, increasing user operations time. To address the need for new tools to directly map and quantify pathogen and host sequence reads from within an infected cell from scRNAseq datasets in a single operation, we have built a python package, called scPathoQuant. scPathoQuant extracts sequences that were not aligned to the primary host genome, maps them to a pathogen genome of interest (here as demonstrated for viral pathogens), quantifies total reads mapping to the entire pathogen, quantifies reads mapping to individual pathogen genes, and finally integrates pathogen sequence counts into matrix files that are used by standard single cell pipelines for downstream analyses with only one command. We demonstrate that scPathoQuant provides a scRNAseq viral and host genome-wide sequence read abundance analysis that can differentiate and define multiple viruses in a single sample scRNAseq output. The SPQ package is available software accessible at https://github.com/galelab/scPathoQuant (DOI 10.5281/zenodo.10463670) with test codes and datasets available https://github.com/galelab/Whitmore_scPathoQuant_testSets (DOI 10.5281/zenodo.10463677) to serve as a resource for the community.

The transcriptomic insight into the differential susceptibility of African Swine Fever in inbred pigs

African swine fever (ASF) is a global threat to animal health and food security. ASF is typically controlled by strict biosecurity, rapid diagnosis, and culling of affected herds. Much progress has been made in developing modified live virus vaccines against ASF. There is host variation in response to ASF infection in the field and under controlled conditions. To better understand the dynamics underlying this host differential morbidity, whole transcriptome profiling was carried out in twelve immunized and five sham immunized pigs. Seventeen MHC homozygous inbred Large white Babraham pigs were sampled at three time points before and after the challenge. The changes in the transcriptome profiles of infected animals were surveyed over time. In addition, the immunization effect on the host response was studied as well among the contrasts of all protection subgroups. The results showed two promising candidate genes to distinguish between recovered and non-recovered pigs after infection with a virulent African swine fever virus (ASFV) pre-infection: HTRA3 and GFPT2 (padj < 0.05). Variant calling on the transcriptome assemblies showed a two-base pair insertion into the ACOX3 gene closely located to HTRA3 that may regulate its expression as a putative genomic variant for ASF. Several significant DGEs, enriched gene ontology (GO) terms, and KEGG pathways at 1 day and 7 days post-infection, compared to the pre-infection, indicate a significant inflammation response immediately after ASF infection. The presence of the virus was confirmed by the mapping of RNA-Seq reads on two whole viral genome sequences. This was concordant with a higher virus load in the non-recovered animals 7 days post-infection. There was no transcriptome signature on the immunization at pre-infection and 1 day post-infection. More samples and data from additional clinical trials may support these findings.