Reference-based Analysis Research Articles

System Parameters Sensitivity Analysis of Ocean Thermal Energy Conversion

Ocean Thermal Energy Conversion (OTEC) is a technology to harvest the solar energy stored in the ocean by utilizing the temperature difference between warm surface and cold deep seawater. Considering that the OTEC system works in a low-temperature range, the present paper assessed the technical resources comprehensively by acquiring in-situ thermocline data and conducting a sensitivity analysis of the system parameters. The in-situ temperature profile data were measured in the waters of North Bali, Indonesia. The temperature gradient data based on field measurements were then compared with the HYCOM consortium model. The data were then used as input in the OTEC power and efficiency estimation through a single-stage ranking cycle. The analysis was conducted by varying the type of working fluid, the performance of the heat exchanger, and the location to investigate how the system parameters influenced the power produced. Using an unusual combination of parameters made it difficult to analyze the resulting data multiple times. However, with reference-based analysis and the formulation of calculations, the sensitivity of each parameter could be assessed at both locations. As a result, the ammonia working fluid provided the highest net power output of the system but had the lowest efficiency of all working fluids. The heat exchanger performance in terms of net power and efficiency cannot be separated from the seawater mass flow requirement. This referred to the results where the heat exchanger with a temperature difference of 3°C before and after the seawater passed through the heat exchanger and produced the highest net power and efficiency. Additionally, the net power output reached its convergence level at a water depth of 400m for the Bungkulan site and 450m for Celukan Bawang, which was proportional to the thermocline tendency. Doi: 10.28991/ESJ-2024-08-02-04 Full Text: PDF

Gliomatosis cerebri (GC) growth pattern: A single-center analysis of clinical, histological, and molecular characteristics of GC and non-GC glioblastoma.

The biological understanding of glioblastoma (GB) with gliomatosis cerebri (GC) pattern is poor due to the absence of GC-specific studies. Here, we aimed to identify molecular or clinical parameters that drive GC growth. From our methylome database of IDH (isocitrate dehydrogenase)-wildtype GB, we identified 158 non-GC and 65 GC cases. GC cases were subdivided into diffuse-infiltrative (subtype 1), multifocal (subtype 2), or tumors with 1 solid mass (subtype 3). We compared clinical, histological, and molecular parameters and conducted a reference-free tumor deconvolution of DNA methylation data based on latent methylation components (LMC). GC subtype 1 less frequently showed contrast-enhancing tumors, and more frequently lacked morphological GB criteria despite displaying GB DNA methylation profile. However, the tumor deconvolution did not deliver a specific LMC cluster for either of the GC subtypes. Employing the reference-based analysis MethylCIBERSORT, we did not identify significant differences in tumor cell composition. The majority of both GC and non-GC patients received radiochemotherapy as first-line treatment, but there was a major imbalance for resection. The entire GC cohort had significantly shorter overall survival (OS) and time to treatment failure (TTF) than the non-GC cohort. However, when filtering for cases in which only stereotactic biopsy was performed, the comparison of OS and TTF lost statistical significance. Our study offers clinically relevant information by demonstrating a similar outcome for GB with GC growth pattern in the surgically matched analysis. The limited number of cases in the GC subgroups encourages the validation of our DNA methylation analysis in larger cohorts.

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps.

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview.

Towards mouse genetic-specific RNA-sequencing read mapping.

Genetic variations affect behavior and cause disease but understanding how these variants drive complex traits is still an open question. A common approach is to link the genetic variants to intermediate molecular phenotypes such as the transcriptome using RNA-sequencing (RNA-seq). Paradoxically, these variants between the samples are usually ignored at the beginning of RNA-seq analyses of many model organisms. This can skew the transcriptome estimates that are used later for downstream analyses, such as expression quantitative trait locus (eQTL) detection. Here, we assessed the impact of reference-based analysis on the transcriptome and eQTLs in a widely-used mouse genetic population: the BXD panel of recombinant inbred lines. We highlight existing reference bias in the transcriptome data analysis and propose practical solutions which combine available genetic variants, genotypes, and genome reference sequence. The use of custom BXD line references improved downstream analysis compared to classical genome reference. These insights would likely benefit genetic studies with a transcriptomic component and demonstrate that genome references need to be reassessed and improved.

RecombineX: A generalized computational framework for automatic high-throughput gamete genotyping and tetrad-based recombination analysis.

Meiotic recombination is an essential biological process that ensures faithful chromosome segregation and promotes parental allele shuffling. Tetrad analysis is a powerful approach to quantify the genetic makeups and recombination landscapes of meiotic products. Here we present RecombineX (https://github.com/yjx1217/RecombineX), a generalized computational framework that automates the full workflow of marker identification, gamete genotyping, and tetrad-based recombination profiling based on any organism or genetic background with batch processing capability. Aside from conventional reference-based analysis, RecombineX can also perform analysis based on parental genome assemblies, which facilitates analyzing meiotic recombination landscapes in their native genomic contexts. Additional features such as copy number variation profiling and missing genotype inference further enhance downstream analysis. RecombineX also includes a dedicate module for simulating the genomes and reads of recombinant tetrads, which enables fine-tuned simulation-based hypothesis testing. This simulation module revealed the power and accuracy of RecombineX even when analyzing tetrads with very low sequencing depths (e.g., 1-2X). Tetrad sequencing data from the budding yeast Saccharomyces cerevisiae and green alga Chlamydomonas reinhardtii were further used to demonstrate the accuracy and robustness of RecombineX for organisms with both small and large genomes, manifesting RecombineX as an all-around one stop solution for future tetrad analysis. Interestingly, our re-analysis of the budding yeast tetrad sequencing data with RecombineX and Oxford Nanopore sequencing revealed two unusual structural rearrangement events that were not noticed before, which exemplify the occasional genome instability triggered by meiosis.

Evaluation of the compressional damage evolution of ancient fired clay bricks using coda wave analysis

Coda waves have been proven to be sensitive to small changes in the medium. In this paper, moving reference coda wave interferometry and decorrelation were adopted to monitor the compressional damage process of ancient fired clay bricks. Two sets of brick cores obtained from an ancient tomb were loaded to failure, and the ultrasonic signals during the loading process were analyzed. Experimental results showed that moving reference-based analysis is more suitable for damage monitoring and coda wave analysis could provide some precursory indications of brick yielding, the yield strength is highly associated with relative velocity change at yielding.

Effects of Resistant Starch on Symptoms, Fecal Markers, and Gut Microbiota in Parkinson’s Disease — The RESISTA-PD Trial

The composition of the gut microbiota is linked to multiple diseases, including Parkinson’s disease (PD). Abundance of bacteria producing short-chain fatty acids (SCFAs) and fecal SCFA concentrations are reduced in PD. SCFAs exert various beneficial functions in humans. In the interventional, monocentric, open-label clinical trial “Effects of Resistant Starch on Bowel Habits, Short Chain Fatty Acids and Gut Microbiota in Parkinson’sDisease” (RESISTA-PD; ID: NCT02784145), we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch (RS). We enrolled 87 subjects in three study-arms: 32 PD patients received RS (PD + RS), 30 control subjects received RS, and 25 PD patients received solely dietary instructions. We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB. RS was well-tolerated. In the PD + RS group, fecal butyrate concentrations increased significantly, and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention. Clinically, we observed a reduction in non-motor symptom load in the PD + RS group. The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups, including bacteria producing SCFAs. Reference-free analysis suggested punctual, yet pronounced differences in the metagenomic signature in the PD + RS group. RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD. The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies, also investigating whether RS is able to modify the clinical course of PD.

BMC322- A RATIONALLY-DESIGNED LIVE BACTERIAL CONSORTIUM BASED ON MICROBIOME FUNCTIONAL GENOMIC ANALYSIS FOR TREATMENT OF IBD

Abstract The mechanisms by which the intestinal microbiome affects intestinal inflammation and disease activity in IBD is not clear. Most of the current data on the association between the intestinal microbiome and IBD is focusing on the intestinal microbial taxa and very little is known about the functionality of the microbiome in this condition. Aim: To construct a live bacterial consortium for reduction of intestinal inflammation in IBD based on a high-resolution functional microbial analysis, and its association with anti-inflammatory processes. Methods: Fecal metagenomics samples (n=581) and corresponding fecal calprotectin (FCP) levels from patients with Crohn’s disease and Ulcerative Colitis were downloaded from the NCBI (SRA SRP057027, http://hmpdacc.org, SRA SRP002163 and SRP056641 (BioProject PRJNA275349)). Computational analysis was performed using PRISM (Biomica Ltd, Rehovot Israel), a proprietary metagenomics analysis platform integrating assembly-based and reference-based analyses for functional genomics profiling and utilizing BioCOGS, Biomica’s internal comprehensive catalogue of microbial gene-orthology groups. We compared microbial functions between subjects based on severity of inflammation (FCP<70 vs FCP >250 and FCP >250 vs FCP <250) and identified microbial functions associated with low inflammation (FDR 0.1). Validation of the anti-inflammatory activity of BMC322 was performed using a DSS-induced colitis mouse model. Results: Out of the dozens of identified functions, 32 were related to host anti-inflammatory processes, intestinal barrier integrity and colonocyte function. Our functional microbiome investigation has pointed to novel metabolic mechanisms by which specific strains may attenuate mucosal inflammation. Using functional annotations, we infer the biological processes governing microbe-host interactions contributing to reduced inflammation, and identify 16 bacteria of which 4 strains comprise the full array of the identified microbial functions associated with lower disease severity. These strains comprise BMC322- a novel live-bacterial consortium for inflammation reduction in IBD. The anti-inflammatory activity of these strains was validated in in-vitro studies and demonstrated efficacy in preliminary a mouse model of DSS-induced colitis; including shortening period of recovery, disease activity index and fecal lipocalin 2. Conclusion: High-resolution functional genomics profiling of the gut microbiome enables identification of microbial-derived functions relevant to inflammatory disease activity, and microbial strains bearing specific inflammation-reduction activity. These strains have provided the basis for the construction of a 4-strain consortium for intervention in IBD. Preliminary work has validated the anti-inflammatory activity of these strains and their potential as novel therapeutic modality for IBD.

Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

Oxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9–99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

SARS-CoV-2 spread across the Colombian-Venezuelan border.

IntroductionVenezuela and Colombia both adopted measures of containment early in response to the COVID-19 pandemic. However, Venezuela's ongoing humanitarian crisis has decimated its health care system, and forced millions of Venezuelans to flee through its porous border with Colombia. The extensive shared border, and illegal cross-border transit through improvised trails between the two countries are major challenges for public health authorities. We report the first SARS-CoV-2 genomes from Venezuela, and present a snapshot of the SARS-CoV-2 epidemiologic landscape in the Colombian-Venezuelan border region.MethodsWe sequenced and assembled viral genomes from total RNA extracted from nasopharyngeal (NP) clinical specimens using a custom reference-based analysis pipeline. Three assemblies obtained were subjected to typing using the Phylogenetic Assignment of Named Global Outbreak LINeages ‘Pangolin’ tool. A total of 376 publicly available SARS-CoV-2 genomes from South America were obtained from the GISAID database to perform comparative genomic analyses. Additionally, the Wuhan-1 strain was used as reference.ResultsWe found that two of the SARS-CoV-2 genomes from Venezuela belonged to the B1 lineage, and the third to the B.1.13 lineage. We observed a point mutation in the Spike protein gene (D614G substitution), previously reported to be associated with increased infectivity, in all three Venezuelan genomes. Additionally, three mutations (R203K/G204R substitution) were present in the nucleocapsid (N) gene of one Venezuelan genome.ConclusionsGenomic sequencing demonstrates similarity between SARS-CoV-2 lineages from Venezuela and viruses collected from patients in bordering areas in Colombia and from Brazil, consistent with cross-border transit despite administrative measures including lockdowns. The presence of mutations associated with increased infectivity in the 3 Venezuelan genomes we report and Colombian SARS-CoV-2 genomes from neighboring borders areas may pose additional challenges for control of SARS-CoV-2 spread in the complex epidemiological landscape in Latin American countries. Public health authorities should carefully follow the progress of the pandemic and its impact on displaced populations within the region.

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide.

Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.

Species-Specific Marker Discovery in Tilapia

Tilapias (family Cichlidae) are of importance in aquaculture and fisheries. Hybridisation and introgression are common within tilapia genera but are difficult to analyse due to limited numbers of species-specific genetic markers. We tested the potential of double digested restriction-site associated DNA (ddRAD) sequencing for discovering single nucleotide polymorphism (SNP) markers to distinguish between 10 tilapia species. Analysis of ddRAD data revealed 1,371 shared SNPs in the de novo-based analysis and 1,204 SNPs in the reference-based analysis. Phylogenetic trees based on these two analyses were very similar. A total of 57 species-specific SNP markers were found among the samples analysed of the 10 tilapia species. Another set of 62 species-specific SNP markers was identified from a subset of four species which have often been involved in hybridisation in aquaculture: 13 for Oreochromis niloticus, 23 for O. aureus, 12 for O. mossambicus and 14 for O. u. hornorum. A panel of 24 SNPs was selected to distinguish among these four species and validated using 91 individuals. Larger numbers of SNP markers were found that could distinguish between the pairs of species within this subset. This technique offers potential for the investigation of hybridisation and introgression among tilapia species in aquaculture and in wild populations.

Resolving the full spectrum of human genome variation using Linked-Reads.

Large-scale population analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short-read whole-genome sequencing. However, these short-read approaches fail to give a complete picture of a genome. They struggle to identify structural events, cannot access repetitive regions, and fail to resolve the human genome into haplotypes. Here, we describe an approach that retains long range information while maintaining the advantages of short reads. Starting from ∼1 ng of high molecular weight DNA, we produce barcoded short-read libraries. Novel informatic approaches allow for the barcoded short reads to be associated with their original long molecules producing a novel data type known as “Linked-Reads”. This approach allows for simultaneous detection of small and large variants from a single library. In this manuscript, we show the advantages of Linked-Reads over standard short-read approaches for reference-based analysis. Linked-Reads allow mapping to 38 Mb of sequence not accessible to short reads, adding sequence in 423 difficult-to-sequence genes including disease-relevant genes STRC, SMN1, and SMN2. Both Linked-Read whole-genome and whole-exome sequencing identify complex structural variations, including balanced events and single exon deletions and duplications. Further, Linked-Reads extend the region of high-confidence calls by 68.9 Mb. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologues of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.

Short template switch events explain mutation clusters in the human genome.

Resequencing efforts are uncovering the extent of genetic variation in humans and provide data to study the evolutionary processes shaping our genome. One recurring puzzle in both intra- and inter-species studies is the high frequency of complex mutations comprising multiple nearby base substitutions or insertion-deletions. We devised a generalized mutation model of template switching during replication that extends existing models of genome rearrangement and used this to study the role of template switch events in the origin of short mutation clusters. Applied to the human genome, our model detects thousands of template switch events during the evolution of human and chimp from their common ancestor and hundreds of events between two independently sequenced human genomes. Although many of these are consistent with a template switch mechanism previously proposed for bacteria, our model also identifies new types of mutations that create short inversions, some flanked by paired inverted repeats. The local template switch process can create numerous complex mutation patterns, including hairpin loop structures, and explains multinucleotide mutations and compensatory substitutions without invoking positive selection, speculative mechanisms, or implausible coincidence. Clustered sequence differences are challenging for current mapping and variant calling methods, and we show that many erroneous variant annotations exist in human reference data. Local template switch events may have been neglected as an explanation for complex mutations because of biases in commonly used analyses. Incorporation of our model into reference-based analysis pipelines and comparisons of de novo assembled genomes will lead to improved understanding of genome variation and evolution.

Suitability of DNA sampled from Nile tilapia skin mucus swabs as a template for ddRAD-based studies

For conservation, ethical and welfare reasons, minimally invasive DNA sampling is becoming increasingly important in animal genetics studies. Skin mucus swabs have been used as a source for fish DNA in PCR-based studies, but their suitability for high-throughput sequencing has not been examined. DNA was extracted from skin mucus, muscle and fin samples of two Nile tilapia and used in double-digest restriction-site associated DNA (ddRAD) sequencing. Approximately 16,000 and 9000 RAD loci were retrieved from de novo and reference-based analyses respectively. The numbers of RAD loci retrieved from three tissue sources were similar, with >83 % being shared among all three samples. Minor bacterial contamination was detected in a single muscle sample (0.07 % of the total RAD loci). The data indicates that DNA derived from skin mucus can be reliably used for ddRADseq and is likely to be applicable in other similar genomic analyses.

Methods for accurate quantification of LTR-retrotransposon copy number using short-read sequence data: a case study in Sorghum.

Transposable elements (TEs) are ubiquitous in eukaryotic genomes and their mobility impacts genome structure and function in myriad ways. Because of their abundance, activity, and repetitive nature, the characterization and analysis of TEs remain challenging, particularly from short-read sequencing projects. To overcome this difficulty, we have developed a method that estimates TE copy number from short-read sequences. To test the accuracy of our method, we first performed an in silico analysis of the reference Sorghum bicolor genome, using both reference-based and de novo approaches. The resulting TE copy number estimates were strikingly similar to the annotated numbers. We then tested our method on real short-read data by estimating TE copy numbers in several accessions of S. bicolor and its close relative S. propinquum. Both methods effectively identify and rank similar TE families from highest to lowest abundance. We found that de novo characterization was effective at capturing qualitative variation, but underestimated the abundance of some TE families, specifically families of more ancient origin. Also, interspecific reference-based mapping of S. propinquum reads to the S. bicolor database failed to fully describe TE content in S. propinquum, indicative of recent TE activity leading to changes in the respective repetitive landscapes over very short evolutionary timescales. We conclude that reference-based analyses are best suited for within-species comparisons, while de novo approaches are more reliable for evolutionarily distant comparisons.

Investigation of De Novo Unique Differentially Expressed Genes Related to Evolution in Exercise Response during Domestication in Thoroughbred Race Horses

Previous studies of horse RNA-seq were performed by mapping sequence reads to the reference genome during transcriptome analysis. However in this study, we focused on two main ideas. First, differentially expressed genes (DEGs) were identified by de novo–based analysis (DBA) in RNA-seq data from six Thoroughbreds before and after exercise, here-after referred to as “de novo unique differentially expressed genes” (DUDEG). Second, by integrating both conventional DEGs and genes identified as being selected for during domestication of Thoroughbred and Jeju pony from whole genome re-sequencing (WGS) data, we give a new concept to the definition of DEG. We identified 1,034 and 567 DUDEGs in skeletal muscle and blood, respectively. DUDEGs in skeletal muscle were significantly related to exercise-induced stress biological process gene ontology (BP-GO) terms: ‘immune system process’; ‘response to stimulus’; and, ‘death’ and a KEGG pathways: ‘JAK-STAT signaling pathway’; ‘MAPK signaling pathway’; ‘regulation of actin cytoskeleton’; and, ‘p53 signaling pathway’. In addition, we found TIMELESS, EIF4A3 and ZNF592 in blood and CHMP4C and FOXO3 in skeletal muscle, to be in common between DUDEGs and selected genes identified by evolutionary statistics such as FST and Cross Population Extended Haplotype Homozygosity (XP-EHH). Moreover, in Thoroughbreds, three out of five genes (CHMP4C, EIF4A3 and FOXO3) related to exercise response showed relatively low nucleotide diversity compared to the Jeju pony. DUDEGs are not only conceptually new DEGs that cannot be attained from reference-based analysis (RBA) but also supports previous RBA results related to exercise in Thoroughbred. In summary, three exercise related genes which were selected for during domestication in the evolutionary history of Thoroughbred were identified as conceptually new DEGs in this study.

Recruiting Human Microbiome Shotgun Data to Site-Specific Reference Genomes

The human body consists of innumerable multifaceted environments that predispose colonization by a number of distinct microbial communities, which play fundamental roles in human health and disease. In addition to community surveys and shotgun metagenomes that seek to explore the composition and diversity of these microbiomes, there are significant efforts to sequence reference microbial genomes from many body sites of healthy adults. To illustrate the utility of reference genomes when studying more complex metagenomes, we present a reference-based analysis of sequence reads generated from 55 shotgun metagenomes, selected from 5 major body sites, including 16 sub-sites. Interestingly, between 13% and 92% (62.3% average) of these shotgun reads were aligned to a then-complete list of 2780 reference genomes, including 1583 references for the human microbiome. However, no reference genome was universally found in all body sites. For any given metagenome, the body site-specific reference genomes, derived from the same body site as the sample, accounted for an average of 58.8% of the mapped reads. While different body sites did differ in abundant genera, proximal or symmetrical body sites were found to be most similar to one another. The extent of variation observed, both between individuals sampled within the same microenvironment, or at the same site within the same individual over time, calls into question comparative studies across individuals even if sampled at the same body site. This study illustrates the high utility of reference genomes and the need for further site-specific reference microbial genome sequencing, even within the already well-sampled human microbiome.