Read Alignment Research Articles

Abstract 1122: Pan-cancer analysis reveals roles of retrotransposon-fusion RNAs

Abstract Transposons comprise half of the human genome and some create novel insertions in human germ cells and somatic tissues of healthy and diseased individuals. These genetic elements may be an abundant yet underexplored source of immunogenic molecules that create chimeric transcripts composed of transposons and non-transposons. However, it remains challenging to accurately identify such transposon-fusion events from short RNA-seq reads mainly due to the repetitive nature of transposon sequences and error-prone read alignment near exon-intron junctions, and there are few existing methods for such detection. We developed a computational pipeline, rTea (RNA Transposable Element Analyzer) to detect transposons-fusion transcripts from RNA-seq data. Using rTea, we performed a pan-cancer analysis of 10,257 cancer samples across 34 cancer types as well as 2,994 normal tissue samples from the TCGA/ICGC, our unpublished colorectal cancer cohort, and the GTEx consortia. Among normal human tissues, higher fusion loads were found most notably in the testis and to a lesser extent in other tissues that have been characterized as immune-privileged. Somatic fusions found in cancer were enriched in known cancer genes, implicating their contribution to tumorigenesis. We also found distinct epigenetic and tumorigenic mechanisms underlying fusions from different transposon families. Through in silico immunogenicity modeling and experimental validation, we confirmed the MHC-I binding and CD8+ T cell activation by peptides derived from transposon fusions to the extent comparable to EBV viruses. Our findings highlight endogenous retroelements as novel therapeutic targets and a significant source of neoantigens. Citation Format: E. Alice Lee, Boram Lee, Yoonjoo Choi, Junseok Park, Adam Voshall, Eduardo Maury, Yeeok Kang, Yeon Jeong Kim, Jin-Young Lee, Hye-Ran Shim, Si-Cho Kim, Hoang Bao Khanh Chu, Da-Won Kim, Minjeong Kim, Eun-Ji Choi, Kyungsoo Ha, Jung Kyoon Choi, Yongjoon Kim, Woong-Yang Park. Pan-cancer analysis reveals roles of retrotransposon-fusion RNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1122.

Abstract 5247: The effect of aspirin on the transcriptional landscape of the colon

Abstract Colorectal cancer (CRC) remains the second leading cause of cancer mortality in the United States. Aspirin is the most promising CRC prevention agent with significant data supporting long-term use with a reduction in the incidence of adenomatous polyps and colorectal cancer. The adoption of aspirin as a preventive agent has been limited by an incomplete understanding of aspirin’s mode of action in humans. To elucidate the mechanism of aspirin prevention, we sought to identify aspirin-associated transcriptional changes within the colon. To accomplish this objective, we performed RNA sequencing of biopsy-derived colonic epithelial cells from participants enrolled (N=180) in our ongoing aspirin randomized placebo-control trial (RCT) program, “ASPirin Intervention for the REDuction of colorectal cancer risk” (ASPIRED). Individuals with a recent resection of an adenoma were eligible. Participants for whom epithelial cells from rectosigmoid mucosal biopsies were successfully obtained at both pre- and post-treatment timepoints (n=130) were included in this analysis. Participants were randomized to once-daily placebo (n=45), low-dose aspirin (81 mg) (n=46), or standard-dose aspirin (325 mg) (n=39) for 8-12 weeks. For each timepoint, we performed Illumina RNA 100-bp paired-end sequencing of 4,000 fluorescence-activated cell-sorted (FACS) colonic epithelial cells (CD45-; EpCAM+). Transcriptomic read alignment and read counts per gene were obtained using STAR and Salmon. Quality control measures removed 52 participants (20 placebo, 18 low-dose aspirin, and 14 standard-dose aspirin) from the analysis. No significant differences in age, gender, ethnicity, or BMI were observed between placebo and aspirin-treated participants. We then identified genes whose expression is modulated by aspirin exposure (low and standard dose aspirin) while controlling for baseline gene expression differences between placebo and aspirin-exposed patients and for intra-sample gene expression variability using Dream. We found 57 genes were significantly differentially expressed in aspirin-treated participants at an FDR < 0.10. AGR2, which is a CRC oncogene that is upregulated by prostaglandin E2 (PGE2), was significantly downregulated by aspirin treatment suggesting aspirin modules the expression of PGE2 responsive genes in the colonic epithelium. Gene enrichment analysis found aspirin significantly downregulated scaffold proteins (e.g. KRT8, KRT18, and ACTB) and structural ribosomal RNA binding proteins. These pathways are critical processes in epithelial cell differentiation. This finding supports previous in vitro evidence that aspirin results in cell composition changes within the colonic epithelium favoring stem-like cell states, which may underlie its preventive effect. Citation Format: Jonathan M. Downie, Connor M. Geraghty, Thaddeus S. Stappenbeck, Ömer H. Yilmaz, David A. Drew, Andrew T. Chan. The effect of aspirin on the transcriptional landscape of the colon. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5247.

Abstract 220: Performance assessment of the novel G4 sequencing platform for cancer research applications

Abstract Introduction: Next-generation sequencing (NGS) has become a critical tool for both biological research and in-vitro diagnostics, though there remains a need for new DNA sequencing platforms that combine high accuracy, speed, and flexible throughput to provide timely results and cost-effective operations. Here we evaluate the utility of the novel Singular Genomics G4 sequencing platform for cancer research applications by performing whole exome sequencing (WGS) of the human reference control HG001. We further demonstrate performance for methyl-seq, bulk RNA-seq, and scRNA-seq. Methods: To assess performance, we prepared a human whole exome library (IDT xGen Exome) from HG001 gDNA, then sequenced the library via the G4 sequencing platform with F2 flowcells (150M read throughput) to yield 483M 2x150bp reads. Sequence quality and mapping metrics were obtained via GATK4 after read alignment to Grch38 via bwa mem. Germline variants were identified via DeepVariant v1.4 using the Illumina WES model, implemented via Parabricks; performance was assessed by hap.py. Methyl-seq performance was examined by methylDackel analysis of an EM-Seq library (NEB) prepared from 50ng HG001 gDNA and sequencing to 20x coverage via 2x150bp reads. Bulk RNA-seq performance was assessed by replicate sequencing of UHR total RNA on the G4 and Illumina NextSeq platforms. Single cell RNA-seq libraries were prepared from healthy donor PBMC via the 10x Chromium, sequenced on the G4 and Illumina NovaSeq, then analyzed via Cell Ranger, Scanpy and scvi-tools. Results: Exome sequencing of HG001 yielded high quality data (single pass accuracy > 99.7% for R1 and R2) with even coverage of the genome (fold 80 =1.42). hap.py analysis of DeepVariant calls yielded a SNP and Indel F1 of 99.06 and 96.05%, respectively, over high confidence regions. Performance was high over challenging features such as low complexity DNA and GC rich regions. EM-Seq analysis revealed global distributions of cytosine methylation that closely matched public EM-Seq data derived from the Illumina NovaSeq. Bulk RNA-seq transcript counts were highly correlated across technical replicates (R2 = .990) and platforms (R2= .988). Pseudo-bulk analysis of scRNA-seq libraries showed high cross-platform correlation (R2= .982), with nearly identical CellTypist annotations (Adjusted Rand Index = .999). Conclusions: The G4 sequencer delivers sequencing data on par with the state-of-the-art in NGS, but with a faster turnaround than traditional SBS systems. Notably, we observe a high concordance with the leading NGS platform for human DNA variant detection, methyl-seq, bulk RNA-seq and scRNA-Seq. We anticipate the rapid turnaround and flexible throughput of the G4 will have broad utility for basic and translational cancer research applications. Citation Format: Kenneth Gouin, Liz LaMarca, Yu Xiang, Anne Decker, Sabrina Shore, Ryan Shultzaberger, Daan Witters, Timothy Looney, Martin M. Fabani, Eli Glezer. Performance assessment of the novel G4 sequencing platform for cancer research applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 220.

Two-Cloud Private Read Alignment to a Public Reference Genome

The human genome is the ultimate identifier of an individual even though most of it is identical across human beings. Biological differences between two individuals are encoded in a set of base pair variations called Single Nucleotide Polymorphisms (SNPs), which may be indicative of an individual's personal information such as skin color and susceptibility to diseases. The large-scale nature of human genome necessitates outsourcing of genomic computations to public clouds. However, this raises some serious privacy concerns. The fact that the human reference template is public poses additional challenges. In this paper, we propose a two-cloud private read alignment algorithm using the Burrows-Wheeler Transform and the FM-Index. Our algorithm runs in the same order of complexity as the core FM-Index alignment algorithm without privacy. Our proposed scheme is able to achieve accuracy comparable to modern alignment algorithms such as Bowtie with complete privacy.

NtLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads.

With the increasing affordability and accessibility of genome sequencing data, de novo genome assembly is an important first step to a wide variety of downstream studies and analyses. Therefore, bioinformatics tools that enable the generation of high-quality genome assemblies in a computationally efficient manner are essential. Recent developments in long-read sequencing technologies have greatly benefited genome assembly work, including scaffolding, by providing long-range evidence that can aid in resolving the challenging repetitive regions of complex genomes. ntLink is a flexible and resource-efficient genome scaffolding tool that utilizes long-read sequencing data to improve upon draft genome assemblies built from any sequencing technologies, including the same long reads. Instead of using read alignments to identify candidate joins, ntLink utilizes minimizer-based mappings to infer how input sequences should be ordered and oriented into scaffolds. Recent improvements to ntLink have added important features such as overlap detection, gap-filling, and in-code scaffolding iterations. Here, we present three basic protocols demonstrating how to use each of these new features to yield highly contiguous genome assemblies, while still maintaining ntLink's proven computational efficiency. Further, as we illustrate in the alternate protocols, the lightweight minimizer-based mappings that enable ntLink scaffolding can also be utilized for other downstream applications, such as misassembly detection. With its modularity and multiple modes of execution, ntLink has broad benefit to the genomics community, from genome scaffolding and beyond. ntLink is an open-source project and is freely available from https://github.com/bcgsc/ntLink. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ntLink scaffolding using overlap detection Basic Protocol 2: ntLink scaffolding with gap-filling Basic Protocol 3: Running in-code iterations of ntLink scaffolding Alternate Protocol 1: Generating long-read to contig mappings with ntLink Alternate Protocol 2: Using ntLink mappings for genome assembly correction with Tigmint-long Support Protocol: Installing ntLink.

SVsearcher: A more accurate structural variation detection method in long read data

Structural variations (SVs) represent genomic rearrangements (such as deletions, insertions, and inversions) whose sizes are larger than 50bp. They play important roles in genetic diseases and evolution mechanism. Due to the advance of long-read sequencing (i.e. PacBio long-read sequencing and Oxford Nanopore (ONT) long-read sequencing), we can call SVs accurately. However, for ONT long reads, we observe that existing long read SV callers miss a lot of true SVs and call a lot of false SVs in repetitive regions and in regions with multi-allelic SVs. Those errors are caused by messy alignments of ONT reads due to their high error rate. Hence, we propose a novel method, SVsearcher, to solve these issues. We run SVsearcher and other callers in three real datasets and find that SVsearcher improves the F1 score by approximately 10% for high coverage (50×) datasets and more than 25% for low coverage (10×) datasets. More importantly, SVsearcher can identify 81.7%–91.8% multi-allelic SVs while existing methods only identify 13.2% (Sniffles)–54.0% (nanoSV) of them. SVsearcher is available at https://github.com/kensung-lab/SVsearcher.

JASPER: A fast genome polishing tool that improves accuracy of genome assemblies.

Advances in long-read sequencing technologies have dramatically improved the contiguity and completeness of genome assemblies. Using the latest nanopore-based sequencers, we can generate enough data for the assembly of a human genome from a single flow cell. With the long-read data from these sequences, we can now routinely produce de novo genome assemblies in which half or more of a genome is contained in megabase-scale contigs. Assemblies produced from nanopore data alone, though, have relatively high error rates and can benefit from a process called polishing, in which more-accurate reads are used to correct errors in the consensus sequence. In this manuscript, we present a novel tool for genome polishing called JASPER (Jellyfish-based Assembly Sequence Polisher for Error Reduction). In contrast to many other polishing methods, JASPER gains efficiency by avoiding the alignment of reads to the assembly. Instead, JASPER uses a database of k-mer counts that it creates from the reads to detect and correct errors in the consensus. Our experiments demonstrate that JASPER is faster than alignment-based polishers, and both faster and more accurate than other k-mer based polishing methods. We also introduce the idea of using a polishing tool to create population-specific reference genomes, and illustrate this idea using sequence data from multiple individuals from Tokyo, Japan.

SCREE: a comprehensive pipeline for single-cell multi-modal CRISPR screen data processing and analysis.

Single-cell CRISPR screens have been widely used to investigate gene regulatory circuits in diverse biological systems. The recent development of single-cell CRISPR screens has enabled multimodal profiling of perturbed cells with both gene expression, chromatin accessibility and protein levels. However, current methods cannot meet the analysis requirements of different types of data and have limited functions. Here, we introduce Single-cell CRISPR screens data analysEs and perturbation modEling (SCREE) as a comprehensive and flexible pipeline to facilitate the analyses of various types of single-cell CRISPR screens data. SCREE performs read alignment, sgRNA assignment, quality control, clustering and visualization, perturbation enrichment evaluation, perturbation efficiency modeling, gene regulatory score calculation and functional analyses of perturbations for single-cell CRISPR screens with both RNA, ATAC and multimodal readout. SCREE is available at https://github.com/wanglabtongji/SCREE.

Benchmarking Oxford Nanopore read alignment-based insertion and deletion detection in crop plant genomes.

Structural variations (SVs) are larger polymorphisms (>50bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked insertion and deletion detection by popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5×, 10×, and 20× coverages typically used in re-sequencing studies. We further validated our findings using maize and soybean datasets. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.

Rhometa: Population recombination rate estimation from metagenomic read datasets.

Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.

Areas of Crush Nuclear Streaming Should Be Included as Tumor Content in the Era of Molecular Diagnostics

Degenerated tissues are frequently observed in malignant tumors, but are not analyzed. We investigated whether nuclear streaming and necrosis samples could be used for genetic analysis to expand the sample pool. A total of 81 samples were extracted from small cell carcinoma and lymphoma FFPE tissue blocks and classified into three histological cohorts: 33 materials with well-preserved tumor morphology, 31 nuclear streaming samples, and 17 necrosis samples. DNA and RNA integrity numbers, percentage of RNA fragments with >200 nucleotides, and next-generation sequencing quality metrics were compared among the cohorts. DNA quality did not significantly differ between nuclear streaming materials and materials with well-preserved morphology, whereas that of the necrosis samples was inferior. RNA quality decreased in the following order: materials with well-preserved morphology > nuclear streaming > necrosis. The sequencing metrics did not differ significantly between the nuclear streaming samples and materials with well-preserved morphology, and reliable variants were detected. The necrosis samples extracted from resections exhibited sequencing failure and showed significantly fewer on-target aligned reads and variants. However, variant allele frequency did not differ among the cohorts. We revelated that DNA in nuclear streaming samples, especially within biopsies, could be used for genetic analysis. Moreover, degenerated non-tumor cells should be counted when evaluating tumor content to avoid misinterpreting the variant allele frequency.

MitoGeneExtractor: Efficient extraction of mitochondrial genes from next‐generation sequencing libraries

Abstract Mitochondrial DNA (mtDNA) sequences are often found as byproducts in next‐generation sequencing (NGS) datasets that were originally created to capture genomic or transcriptomic information of an organism. These mtDNA sequences are often discarded, wasting this valuable sequencing information. We developed MitoGeneExtractor, an innovative tool which allows to extract mitochondrial protein coding genes (PCGs) of interest from NGS libraries through multiple sequence alignments of sequencing reads to amino acid references. General references, for example on order level are sufficient for mining mitochondrial PCGs. In a case study, we applied MitoGeneExtractor to recently published genomic datasets of 1993 birds and were able to extract complete or nearly complete sequences for all 13 mitochondrial PCGs for a large proportion of libraries. Compared to an existing assembly guided sequence reconstruction algorithm, MitoGeneExtractor was faster and substantially more sensitive. We compared COI sequences mined with MitoGeneExtractor to COI databases. Mined sequences show a high sequence similarity and correct taxonomic assignment between the recovered sequence and the assigned morphospecies in most samples. In some cases of incongruent taxonomic assignments, we found evidence for contamination in NGS libraries. MitoGeneExtractor allows a fast extraction of mitochondrial PCGs from a wide range of NGS datasets. We recommend to routinely harvest and curate mitochondrial sequence information from genomic resources. MitoGeneExtractor output can be used to identify contaminated NGS libraries and to validate the species identity of the sequenced animal based on the extracted COI sequences.

Genomic profiling of dioecious Amaranthus species provides novel insights into species relatedness and sex genes

BackgroundAmaranthus L. is a diverse genus consisting of domesticated, weedy, and non-invasive species distributed around the world. Nine species are dioecious, of which Amaranthus palmeri S. Watson and Amaranthus tuberculatus (Moq.) J.D. Sauer are troublesome weeds of agronomic crops in the USA and elsewhere. Shallow relationships among the dioecious Amaranthus species and the conservation of candidate genes within previously identified A. palmeri and A. tuberculatus male-specific regions of the Y (MSYs) in other dioecious species are poorly understood. In this study, seven genomes of dioecious amaranths were obtained by paired-end short-read sequencing and combined with short reads of seventeen species in the family Amaranthaceae from NCBI database. The species were phylogenomically analyzed to understand their relatedness. Genome characteristics for the dioecious species were evaluated and coverage analysis was used to investigate the conservation of sequences within the MSY regions.ResultsWe provide genome size, heterozygosity, and ploidy level inference for seven newly sequenced dioecious Amaranthus species and two additional dioecious species from the NCBI database. We report a pattern of transposable element proliferation in the species, in which seven species had more Ty3 elements than copia elements while A. palmeri and A. watsonii had more copia elements than Ty3 elements, similar to the TE pattern in some monoecious amaranths. Using a Mash-based phylogenomic analysis, we accurately recovered taxonomic relationships among the dioecious Amaranthus species that were previously identified based on comparative morphology. Coverage analysis revealed eleven candidate gene models within the A. palmeri MSY region with male-enriched coverages, as well as regions on scaffold 19 with female-enriched coverage, based on A. watsonii read alignments. A previously reported FLOWERING LOCUS T (FT) within A. tuberculatus MSY contig was also found to exhibit male-enriched coverages for three species closely related to A. tuberculatus but not for A. watsonii reads. Additional characterization of the A. palmeri MSY region revealed that 78% of the region is made of repetitive elements, typical of a sex determination region with reduced recombination.ConclusionsThe results of this study further increase our understanding of the relationships among the dioecious species of the Amaranthus genus as well as revealed genes with potential roles in sex function in the species.

Direct Inspection of Primary Aortic Cell Transcriptomes Identifies Candidate Causative Variants in Patients with Thoracic Aortic Aneurysm

Background: Thoracic aortic aneurysm (TAA) is an aortopathy that predisposes to life-threatening aortic dissection. Autosomal dominant disorders associated with TAA include Marfan syndrome (FBN1), Loeys-Dietz syndrome (TGFBR1/2, SMAD3, TGFB2), and vascular type Ehlers-Danlos syndrome (COL3A1). Our objective was to identify single nucleotide variants (SNVs) in these six genes within the transcriptomes of primary aortic cells acquired from patients with aortopathy.Methods: Primary aortic cell lines were cultured directly from the medial layer of surgically explanted aortic tissues in 63 unrelated aortopathy patients. RNA samples were extracted from aortic cells for mRNA sequencing. RNA reads aligning to the 6 selected TAA genes were directly inspected using Integrative Genomics Viewer (Broad Institute), and the identified SNVs were filtered for downstream analysis.Results: Study patients were predominantly male and of European ancestry with a mean age of 52±18 years. Thirty-three (52%) patients had a bicuspid aortic valve, and 10 (16%) had family history of TAA or dissection. A total of 3740 SNVs were identified in patient transcriptomes, and these occurred at 905 distinct genomic coordinates. There were 115 SNVs that were unique within the cohort, not located within a 3’-untranslated region, and had ≥20 aligned reads at the SNV’s position. Using the application Franklin (Genoox) to estimate clinical interpretation, 8 unique SNVs were classified as pathogenic (P) or likely pathogenic (LP). Five of these P/LP SNVs were associated with reduced allelic expression, and gene expression level was below the 20th percentile of study samples for 6 P/LP SNVs. Eighty-six unique SNVs were classified as variants of uncertain significance (VUSs). A total of 39 patients (62%) had at least one SNV classified as P/LP/VUS.
 Conclusion: Transcriptomic analysis of primary aortic cells identified candidate causative SNVs and their relative allelic expression. Further analyses will investigate additional TAA-associated genes and integrate transcriptional abnormalities with genetic variants.

ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data.

Long-read RNA sequencing (RNA-seq) holds great potential for characterizing transcriptome variation and full-length transcript isoforms, but the relatively high error rate of current long-read sequencing platforms poses a major challenge. We present ESPRESSO, a computational tool for robust discovery and quantification of transcript isoforms from error-prone long reads. ESPRESSO jointly considers alignments of all long reads aligned to a gene and uses error profiles of individual reads to improve the identification of splice junctions and the discovery of their corresponding transcript isoforms. On both a synthetic spike-in RNA sample and human RNA samples, ESPRESSO outperforms multiple contemporary tools in not only transcript isoform discovery but also transcript isoform quantification. In total, we generated and analyzed ~1.1 billion nanopore RNA-seq reads covering 30 human tissue samples and three human cell lines. ESPRESSO and its companion dataset provide a useful resource for studying the RNA repertoire of eukaryotic transcriptomes.

Population differentiated copy number variation between Eurasian wild boar and domesticated pig populations

Sus scrofa is a globally distributed livestock species that still maintains two different ways of life: wild and domesticated. Herein, we detected copy number variation (CNV) of 328 animals using short read alignment on Sscrofa11.1. We compared CNV among five groups of porcine populations: Asian domesticated (AD), European domesticated (ED), Asian wild (AW), European wild (EW), and Near Eastern wild (NEW). In total, 21,673 genes were identified on 154,872 copy number variation region (CNVR). Differences in gene copy numbers between populations were measured by considering the variance-based value {V}_{ST} and the one-way ANOVA test followed by Scheffe test. As a result, 111 genes were suggested as copy number variable genes. Abnormally gained copy number on EEA1 in all populations was suggested the presence of minor CNV in the reference genome assembly, Sscrofa11.1. Copy number variable genes were related to meat quality, immune response, and reproduction traits. Hierarchical clustering of all individuals and mean pairwise {V}_{ST} in breed level were visualized genetic relationship of 328 individuals and 56 populations separately. Our findings have shown how the complex history of pig evolution appears in genome-wide CNV of various populations with different regions and lifestyles.

The hitchhikers' guide to RNA sequencing and functional analysis.

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Uncovering novel MHC alleles from RNA-Seq data: expanding the spectrum of MHC class I alleles in sheep

BackgroundMajor histocompatibility complex (MHC) class I glycoproteins present selected peptides or antigens to CD8 + T cells that control the cytotoxic immune response. The MHC class I genes are among the most polymorphic loci in the vertebrate genome, with more than twenty thousand alleles known in humans. In sheep, only a very small number of alleles have been described to date, making the development of genotyping systems or functional studies difficult. A cost-effective way to identify new alleles could be to use already available RNA-Seq data from sheep. Current strategies for aligning RNA-Seq reads against annotated genome sequences or transcriptomes fail to detect the majority of class I alleles. Here, I combine the alignment of RNA-Seq reads against a specific reference database with de novo assembly to identify alleles. The method allows the comprehensive discovery of novel MHC class I alleles from RNA-Seq data (DinoMfRS).ResultsUsing DinoMfRS, virtually all expressed MHC class I alleles could be determined. From 18 animals 75 MHC class I alleles were identified, of which 69 were novel. In addition, it was shown that DinoMfRS can be used to improve the annotation of MHC genes in the sheep genome sequence.ConclusionsDinoMfRS allows for the first time the annotation of unknown, more divergent MHC alleles from RNA-Seq data. Successful application to RNA-Seq data from 16 animals has approximately doubled the number of known alleles in sheep. By using existing data, alleles can now be determined very inexpensively for populations that have not been well studied. In addition, MHC expression studies or evolutionary studies, for example, can be greatly improved in this way, and the method should be applicable to a broader spectrum of other multigene families or highly polymorphic genes.

Genomic Compression With Read Alignment at the Decoder

Genomic Compression With Read Alignment at the Decoder

Accelerating Minimap2 for Accurate Long Read Alignment on GPUs.

Long read sequencing technology is becoming increasingly popular for Precision Medicine applications like Whole Genome Sequencing (WGS) and microbial abundance estimation. Minimap2 is the state-of-the-art aligner and mapper used by the leading long read sequencing technologies, today. However, Minimap2 on CPUs is very slow for long noisy reads. ~60-70% of the run-time on a CPU comes from the highly sequential chaining step in Minimap2. On the other hand, most Point-of-Care computational workflows in long read sequencing use Graphics Processing Units (GPUs). We present minimap2-accelerated (mm2-ax), a heterogeneous design for sequence mapping and alignment where minimap2's compute intensive chaining step is sped up on the GPU and demonstrate its time and cost benefits. We extract better intra-read parallelism from chaining without losing mapping accuracy by forward transforming Minimap2's chaining algorithm. Moreover, we better utilize the high memory available on modern cloud instances apart from better workload balancing, data locality and minimal branch divergence on the GPU. We show mm2-ax on an NVIDIA A100 GPU improves the chaining step with 5.41 - 2.57X speedup and 4.07 - 1.93X speedup : costup over the fastest version of Minimap2, mm2-fast, benchmarked on a Google Cloud Platform instance of 30 SIMD cores.